In this Chapter, we present
research subjects that need to
be addressed by European
organizations to enable and
support effective development of
European industry in about a
decade from today. The previous
chapters have presented status,
trends, and plans for the near
future, including challenges
that are foreseen to require
special attention within the
coming decade. We build upon
these identified challenges and
specify long-term industrial
needs. These needs are the basis
for research programs for
effective research and
development in appropriate
technological and/or application
domains, so that European
technological strength increases
continuously in time and at the
appropriate rate. Since
lead-time from a first
scientific breakthrough (TRL1)
to market presence of related
products (TRL9) is typically 10
years or more, the effective
identification of the future
industrial needs is a
determining factor for the
success and speed of
innovation.
The long-term vision is
shaped by three main factors:
technology, application domains
and policies. Clearly, all
factors are drivers of
innovation, because (i)
anticipated technological
advances lead to innovative
applications of these advances
and (ii) user needs lead to
technological innovations that
enable applications and
services. At the same time,
policies and politically
established goals and processes
lead technologies and
applications towards common
goals and targets.
Regarding policies, which
lead many technologies and
applications on a pan-European
scale, the ECS community has
specified its common objectives
that influence and shape
long-term innovation and must be
considered in future research
directions. As presented in the
Introduction, these four
high-level common objectives
are:
Boosting industrial
competitiveness through
interdisciplinary technology
innovations.
Ensuring EU digital
autonomy through secure, safe
and reliable ECS supporting key
European application
domains.
Establishing and
strengthening sustainable and
resilient ECS value chains that
support the Green Deal.
Unleashing the full
potential of intelligent and
autonomous ECS-based systems for
the European digital
era.
These objectives, which are
aligned with policies and
European political priorities,
address the need to establish
unrestricted access to goods and
services, free exchange of
know-how and information, under
trusted, protected and regulated
multilateral agreements in the
emerging international political
and economic landscape. European
Union’s policies to protect its
strategic autonomy, and sustain
its competitiveness are shaping
and continuously advancing,
especially for the ECS industry,
which constitutes the backbone
of the digital society. European
digital strategic autonomy –
European Union’s ability to
maintain control and security of
its products, overcoming
disruptions and vulnerabilities
– is one of the major challenges
when considering that its major
economic drivers, i.e.,
digitisation and connectivity,
are strongly dependent on the
supply of hardware and software
from countries outside Europe.
This challenge needs to be
addressed immediately, for the
short term as well as for the
long term, by research programs
on the following topics:
Safety and security:
development of rigorous
methodologies, supported by
evidence, that a system is
secure and safe; safety and
security are requirements for
trustworthiness. These
methodologies should enable
certification through
appropriate methods, such as
testing and/or formal methods to
prove trustworthiness
guarantees.
Artificial intelligence
and machine learning (AI/ML):
AI/ML-based techniques will
contribute significantly to the
development of robust ECS
components, systems, and
applications, with short
development cycles. AI/ML will
influence all major technologies
in ECS development, from
model-based engineering and
embedded software to
fabrication, and will constitute
a major link between quality,
reliability, safety, and
security.
Trustworthiness:
development of methodologies
that integrate traditional ECS
technologies with AI/ML, from
device level up to applications
and human interface.
Trustworthiness is key to the
acceptance of such emerging
systems. Advances in explainable
AI models for human/ system
interaction, safety, security,
risk analysis and management,
liability and certification are
necessary for the required
trustworthiness that will lead
to the acceptance of the new
generation of innovative
products.
The European Green Deal is
another policy that combines
wide civilian acceptance with
high political priority and
shapes innovation strongly. As
climate change and environmental
degradation pose an existential
threat to Europe and the world,
the European Green Deal is the
European strategy to make the
economy of the European Union
sustainable in the long term1.
By 2050, a modern
resource-efficient and
competitive economy must be in
place, characterized by:
Zero net emissions of
greenhouse gases.
Economic growth decoupled
form resource use.
Inclusion (no person and
no place are left
behind).
The ECS community is
instrumental to the realization
of the European Green Deal. The
many challenges associated with
energy management can be tackled
only with ECS-based solutions,
leading to energy-efficient ECS
devices as well.
The first three high-level
common objectives of the ECS
community (competitiveness,
robustness of ECS products and
establishing value chains) can
be achieved only by reaching the
fourth one as well. The
“unleashing” of intelligent and
autonomous ECS-based systems
requires the interdisciplinary
effort and coordination of all
stakeholders; academic,
institutional, and industrial.
In the effort to ensure
effective and timely
identification of effective
exploitation of opportunities, a
close cooperation of all
stakeholders along the value
chain is a prerequisite. This
cooperation is traditionally
strong in Europe and constitutes
a valuable European strategic
asset. This strength is based on
the availability of many
research facilities with
excellent competence and
extensive experience in the ECS
domain. This comprehensive
ecosystem of universities, RTOs,
and industrial research
organizations distributed across
many countries in Europe forms a
leading incubator for pioneering
technologies that enable the
creation of hyper-smart, safe,
secure, and resource-efficient
electronic components and
systems. This ecosystem enables
increasingly networked
scientific work and is the base
for maintaining the
competitiveness of the European
ECS industry now and in the
future. Cooperation also offers
the best opportunities for
coping with the growing
interdependencies and
interdisciplinarity through
strong coupling of basic and
applied research within the
European Research Framework
Programme. This, in turn,
creates the fertile soil, from
which industry can receive
substantial impulses to achieve
breakthrough solutions with
minimal time to market, leading
to maintenance of European
technological excellence and
leadership, which is the
cornerstone of long-term
European technological
leadership and a basis for
prosperity and peace in our
continent.
Additionally, and
independently of policies,
long-term vision is shaped by
technological and application
evolution and revolution. Many
future applications will be
enabled by enhanced functional
and non- functional properties
provided by new technologies
(both hardware and software), as
projected in
technology-application roadmaps
such as the one shown in Figure
4.1. Typically, the advances
that are foreseen through
roadmaps are considered
evolutionary. However, there
have been several occurrences of
revolutionary or disruptive
developments in technology.
These are not projected in
roadmaps; they exploit and
establish innovative
technological models and have
tremendous technological and
societal impact. Often, they
lead to paradigm shifts with
significant impact to business
and society. The World Wide Web
is a typical example of
disruptive technology.
Figure 4.1 - Technology evolution and application requirements.
Over the last decades, the
ECS domain has evolved from a
technology-driven field to an
environment where societal needs
and application requirements
guide the research agendas of
the centres of expertise.
However, technology-driven
research goals need to be a part
of the research agendas,
considering that novel
technologies often create and
enable new classes of
applications. The European
competences in “Beyond CMOS”,
‘More Moore’ and ‘More than
Moore’ have been instrumental in
bringing about this change,
resulting in a strong European
position in markets that require
complex multifunctional smart
systems. Clearly, maintaining
and extending these competences
is fundamental to the continuous
offering of disruptive
technologies that will preserve
the European competitive
position.
In this Chapter, we present
the main research trends that
are of particular importance to
the European strategic research
and innovation agenda. Clearly,
presenting a complete list of
anticipated evolutionary and
revolutionary, or disruptive,
technologies and challenges is
infeasible, by its very nature.
Considering the three factors
that shape the long-term vision
- technology, application
domains and policy - in the
following section we present a
model that enables us to present
challenges in a systematic way.
We consider policies to provide
the framework as well as
parameters for technologies and
applications and then, we
present technological challenges
and needs to be met in
application domains.
Figure 4.2 - Technology domains evolution and application requirements.
As explained in the introductory chapter of this SRIA (Chapter 0), we consider a layered model for the technological and application challenges for ECS, as shown in Figure 4.2.
As explained in the
introductory chapter of this
SRIA (Chapter 0), we consider a
layered model for the
technological and application
challenges for ECS, as shown in
Figure 4..
In the remaining sections of
this Chapter, we present
challenges in technologies,
fundamental and cross-sectional,
as well as in the application
domains that are enumerated in
Figure 4., with the
understanding that our
presentation addresses
evolutionary and revolutionary
technologies based on
conventional technological and
societal understanding.
Independently, our expectation
is that disruptive innovations
will be readily integrated in
our long-term view, since they
will affect part or parts of the
layered model which we exploit
for abstraction, understanding,
presentation and openness.
Europe has a strong
competence in ECS process
technology, enabled by the
presence of an industrial,
institutional, and academic
ecosystem with a long tradition
in multidisciplinary
collaborative research in
regional, transnational and
European cooperative projects.
With the growing complexity of
ECS-based devices and systems,
this multidisciplinary
collaborative approach along the
value chain is one of the major
assets for Europe in maintaining
its competitiveness.
In the More Moore field,
there are strong interests in
Europe for specific activities
that involve very low power
devices, leading to possible
disruptive applications – for
instance, for future IoT
systems, devices for AI/ML,
neuromorphic and photonic
computing devices, embedded
memories, 3D sequential
integration or
application-driven performance
(e.g. high temperature
operations in the automotive
industry).
New materials, including 1D
and 2D structures, ultimate
processing technologies and
novel nanodevice structures for
logic and memories are mandatory
for different applications as
well as new circuit
architectures, design techniques
and embedded software. Some of
these nanostructures are also
very interesting for advanced
sensors, energy harvesters and
photonics. All of these are key
for future high
performance/ultra- low power
tera-scale integration and
autonomous nanosystems.
These promising technologies
that could underpin numerous
future applications will allow
us to overcome a range of
challenges being faced for
future ICs – in particular, high
performance, low/very low static
and dynamic power consumption,
device scaling, low variability,
and affordable cost. Many
long-term challenges must be
addressed to ensure successful
application of these
nanotechnologies. A number of
these are described briefly in
the following:
Nanowires and nanosheets,
for high performance and very
low-power nanoscale devices, the
best material and geometry
options for logic (high speed as
well as low power) need to be
identified.
Millimetre-wave and THz
front ends with III-V MOSFETs
have to be developed (with
applications in communications,
radar, etc.), including 3D
aspects of processing.
Non-conventional
switching devices, like negative
capacitance field-effect
transistors (NCFETs), tunnel
effect transistors (TFET), 1D
(CNT) or 2D (graphene and
others), which could be suitable
for very low power devices, need
development in basic material,
extended characterization of
optimal architectures and design
strategies.
For
nano-electro-mechanical FETs
(NEMS-FET), low-voltage reliable
devices have to be
developed.
Spin-based devices also
for switching and
sensing.
In the field of alternative
memories, resistive RAM,
magnetic RAM (SOT and VCMA) and
ferroelectric RAM/FeFET/FTJ will
be key for driving the limits of
integration and performance
beyond that afforded by existing
non-volatile, DRAM and SRAM
memories. Research should
address:
Widening the material
screening and programming
schemes.
Variability and
reliability, especially data
retention.
Trade-off between
programming speed and
programming power/data
retention.
Compatibility with
standard logic
processes.
Architectures for memory
embedding in logic, for novel
computing schemes.
In the long-term beyond CMOS
domain, the challenges to be
addressed are in the field of
beyond-conventional CMOS
technologies, non-Boolean logic,
and beyond-von Neumann
architectures, including novel
state variables, new materials
and device, and innovative
device-architecture
interaction.
Integrated photonics,
evolving from silicon photonics,
are required to interface
conventional electronics with
photonic-based communications
and sensors, and, in a longer
perspective, with
photonics-based computing.
The emerging field of Quantum
computing poses its own
challenges in process
technology, equipment, and
materials:
As there are still
several candidates for becoming
the standard quantum computing
technology (such as
semiconductor quantum dots,
superconductor junctions,
photonic circuits, ion-traps,
cold atoms, topological states,
etc.), a wide range of materials
is relevant, together with
innovations in process
technology.
New metrology
capabilities are required,
especially the measurement of
electrical properties, such as
local carrier mobility, is
needed.
To achieve practical
applications, reliable
fabrication, connection, and
read-out of qubits need to be
developed. The low temperatures
at which most quantum systems
are operated requires the
development of cryogenic
devices, to interface
conventional
electronics.
Importantly, all quantum
technologies related to sensing,
communications and computing,
including software, present
significant challenges
today.
Reducing the
environmental impact of
semiconductor
manufacturing
While the chip advancements
are contributing to the
industries across verticals, and
are crucial to achieve the Green
Deal objectives, significant
efforts must be made to tackle
the direct environmental impact
of chips and more generally ECS
manufacturing. Issues to be
addressed include waste
generation, resource usage,
CO2 and GHG
emissions, hazardous materials
use, including PFAS, and scarce
materials use.
Waste generation
Fabricating a small 2g
microchip (≈ 14-10nm technology
node) requires 32-35 kilograms
of water, 1.6kg of petroleum and
72g of chemicals. Since advanced
technology nodes will require
more metal layers and
lithography steps, and, as the
chip production could nearly
double to satisfy chip demand in
the coming years, the
environmental impact of the
semiconductor industry on
power/energy and water
consumption, and on
CO2 and GHG emission,
will strongly increase. This
consumption and emission can
reach unacceptable levels to
cope with the Green Deal
sustainability objectives.
Natural resource
consumption
Regarding water, each chip
needs to be rinsed with
ultrapure water (UPW) to remove
various debris (ions, particles,
silica, etc.) from the
manufacturing process and
prevent the chips from becoming
contaminated. The semiconductor
industry has been working for
more than twenty years to reduce
the amount of water needed to
manufacture a chip for economic
reasons as well. Nowadays, due
to the most frequent occurrence
of droughts, the water issue is
a high priority in the
sustainable development plans of
major semiconductor companies.
Semiconductor manufacturers must
focus their efforts on new ways
to recycle, reduce, and reuse
the water used in their
production. Nevertheless, new
advancements in water treatment
must emerge to allow
semiconductor manufacturers to
recover and reuse wastewater,
remove targeted contaminants,
and even reclaim valuable
products from waste streams. New
long-term approaches will
deserve more R&D efforts for
improving the effluent
segregation systems and hence
increasing the use of recycled
water in semiconductor
manufacturing lines.
The main European
semiconductor manufacturing
should use 100% of renewable
energy sources in 2030.
Likewise, the water and carbon
footprints of the semiconductor
industry must be strongly
reduced to achieve near-zero
CO2 and GHG emission,
for instance.
CO2 and Green
House Gas (GHG)
emission
In the semiconductor
industry, CO2 and
Green House Gas (GHG) emission
arise from process gases used
during wafer etching, chamber
cleaning, and other tasks.
Furthermore, they rise as node
size shrinks. These gases, which
include PFCs, HFCs,
NF3, and
N20, have high
global-warming potential. Gas
recycling of unutilized process
gases and by-products through
various means, such as membrane
separation, cryogenic recovery,
adsorption, and desorption can
be a long-term approach to
reducing GHG emission. In
collaboration with equipment
suppliers, semiconductor fabs
could refine them into pure
process gases that can be used
again, potentially reducing
process-gas emissions. For this
lever to become economically
viable, collaboration between
semiconductor companies,
equipment suppliers and
researchers will be compulsory
to address these major
challenges related to the
separation of process-gas
outflows and purification.
Another long-term approach
could consist of lowering GHG
emissions by switching chemicals
that have a lower environmental
impact than the aforementioned
fluoride gases, such as on-site
generation of molecular
F2 for replacing
NF3, since molecular
F2 has no global
warming potential. Developing
new solutions will require
strong R&D efforts and will
be both costly and time-
consuming, as is the process for
qualifying new chemicals on
existing processes and
tools.
Since most of the
aforementioned fluoride
compounds are used for etching,
another long-term approach could
concern the replacement of some
non-critical etching processes
by additive manufacturing
process steps. Such a
replacement will require strong
R&D efforts to develop
highly selective deposition
processes and/or self-assembled
molecules that can prohibit the
deposition of metal and
dielectrics.
Sustainability issues in
semiconductor manufacturing
induced by PFAs
PFAS is a class of thousands
of synthetic substances known as
‘forever chemicals’ since they
do not break down in the
environment. Most of which are
either persistent themselves or
are transformed into persistent
compounds in the environment.
These substances are hazardous
for human health as they
accumulate in the body, but also
in water, ground, and then the
seas and oceans.
PFAS are defined as
fluorinated substances that
contain at least one fully
fluorinated methyl or methylene
carbon atom (without any
H/Cl/Br/I atom attached to it),
i.e. with a few noted
exceptions, any chemical with at
least a perfluorinated methyl
group (–CF3) or a perfluorinated
methylene group (–CF2–). Due to
the C-F bond strength compared
to C-C bond, PFAS offer a unique
set of technical
characteristics, which include
exceptional heat and chemical
resistance, high electrical
insulation resistance, high
purity, low-outgassing and low
coefficient of friction.
Those intrinsic properties
are the basis of many of the
technical benefits of
fluorinated materials in
semiconductor processing, but
this also leads to their
chemical stability and
environmental persistence.
Fluorination brings unique
physicochemical properties and
consequent qualitative
improvements that are the
enablers of semiconductor,
performance and manufacturing
advancements.
PFAS are used in the
semiconductor manufacturing
industry in the lithography
process, as a component added to
the photoresist to generate
photo-acid generators (PAG),
improve its adhesion to the
silicon wafer, increase its
durability, and enhance its
resistance to harsh chemicals
and high temperatures. In
addition to their use in
photolithography, PFAS are
crucial in producing other
semiconductor components. They
are used in wet chemistries as
surfactants (cleaning, stripping
and etching, and metal plating),
dry etching, chamber plasma
cleaning, CVD and ALD. PFAS are
paramount in packaging materials
to improve thermal stability and
moisture resistance. They are
also used as a coolant in the
chip etching process, as working
fluids for vacuum pumps…
Besides their application in
chipmaking, PFAS are also
essential for semiconductor
manufacturing equipment and
factory infrastructure. Their
exceptional properties, such as
heat resistance and chemical
inertness, make them useful in
equipment components (tubing,
gaskets, containers, filters,
etc.) and lubrication (such as
various oils and greases).
Up to now, excluding some
dedicated applications, there is
no PFAS-free alternative for
most of the aforementioned
applications.
As of today, 1485 tons of
PFAS are used every year for
producing semiconductors in the
European Economic Area. Since
the European Chips Act aims at
doubling the EU’s current
manufacturing capacity from
9-10% to 20% by 2030, it will
require at least a four-fold
expansion of the semiconductor
manufacturing capacity in the
EU, and consequently a four-fold
use of PFAS.
Accordingly, it will be
crucial to conduct research to,
where possible, identify
alternative chemistries that are
preferable from an environmental
point of view and to develop
measurement, recycling,
treatment, and efficient
abatement technologies to
prevent environmental releases
for uses to which no PFAS
alternative can be found.
Scarce materials
use
Finally, to secure their
whole supply chain and for not
wasting mineral resources, in
view of the limited extractable
quantities of metals in the
earth’s crust, chipmakers will
be increasingly concerned with
the potential scarcity of some
ores that are compulsory for
producing ultra-pure metals for
the high-volume manufacturing of
devices. One approach to resolve
this issue is to use recycled
metals instead of premium
metals. Another one is to
recover the metals from the
electronic waste (e-waste),
thus, preventing the use of
natural resources. The recovery
of scarce metals from
microelectronic devices opens a
wide research domain for
material scientists, ensuring
sustainable metal sources for
chipmakers.
The interaction among people
and other information agents and
their environment usually
features a trade of data, which
is curated into information that
either results in a gain of
knowledge and/or the enabling of
purposeful action or reaction to
a given situation.
The width and breadth of such
data trading is expected to
increase in the future in terms
of space and time density.
Seamless integration /
interaction with the environment
and agents involved, based on
evolved human-machine
interaction (e.g. haptic and
brain-computer interfaces) or
machine-to-machine interaction,
is expected in scenarios that
either empower or substitute
humans in decision loops.
ECS conform to the HW and SW
ensembles that at different
levels of integration and
organization complexity, mediate
the different elements of such
information trading:
acquisition, management, and
exploitation. Indeed, HW and SW
integration schemes are the ones
ultimately responsible for
substantiating the expected
increasing number of systems
functions and applications that
are to emerge from the reunion
of:
Communication/interaction
interfaces with users or higher
instances of the decision
chain.
In addition to the continuous
improvement of semiconductor
processes and materials,
increasing the level of
proficiency of elements managing
information, integration of more
diverse components will be
essential to make systems aimed
at monitoring the condition of
people, assets, processes, and
environments less dependable on
the use of energy and on
external supervision. Making
these devices and systems
faster, more sensitive,
efficient, robust, functional,
and apt to different application
scenarios will demand higher
levels of heterogeneity of
materials and fabrication and
assembling processes.
Self-powering, energy
harvesting and storage will
become more and more important
and significant advances are
expected in solid-state devices
to cover the needs of edge and
IoT devices. Integration of
intelligence to these inherently
power-restricted devices
requires novel power-efficient
computational platforms, such as
neural networks and analog
computing approaches in parallel
with CMOS and other traditional
semiconductor devices as
commented in the previous
chapter.
Next generation computing
devices, using physics to make
computation, pose challenges in
integration as well as in
development. In such approaches,
envisaged in Chapter 3.1, and
made possible in the frame of
processes of Chapter 2.1, other
modes of coding information
besides bits will be used, e.g.
using qubits or encoding in
time, like for neuromorphic
architectures where information
is coded in a succession of
spikes, or their coincidence in
time. Another massively parallel
approach using biological
technology (based on proteins,
DNA construction, etc.) can also
emerge for niche applications,
or for storage2.
Most of these technologies will
be used first in servers for
very specialized acceleration
but will slowly improve to be
integrated into edge
devices.
Moreover, ECS are pivotal
elements of the digital
transition that supports the
current and future quest for
making our civilization
sustainable, maximizing
performance and minimizing
e-waste, and particularly for
slowing down, reverting or
making human environments
resilient to climate change.
In addition to the
sustainability of the ECS
fabrication processes
themselves, the scarcity of
materials and the increasing
demand for ECS imply that
approaches and architectures
that improve the chances of ECS
modules to be repaired and/or
reused, and of their material
constituents to be reclaimed,
need to be addressed. Single-use
or disposable devices need to be
designed with minimal, and
minimally invasive, electronics.
The same applies to
install-and-forget systems to be
deployed in natural remote
locations. For example, organic
and printed electronics can lead
to biocompatible electronics and
more effectively recyclable
systems. Although there are
widespread efforts to recycle
computing systems, we are far
from the goal of effectively
recycling because of the lack of
processes to support an
appropriate circular economic
and business model. Component
and system level challenges
range from homogenizing
component and subsystem
lifecycles to computing models
and materials used. In a wide
range of application domains, a
significant challenge is the
ability for global
reconfiguration of system
resources to satisfy diverse
applications’ functional and
non-functional requirements,
such as latency and energy,
including re-training in AI/ML
subsystems. Significant effort
needs to be made to develop
systems that are scalable
linearly or functionally.
In terms of global resilience
to climate change,
multifunctional smart
information systems will be in
demand to react faster to such
upcoming challenges and risks.
For instance, globalization of
human activity and large-scale
weather changing patterns will
ease the spread of known
diseases beyond their usual
geographical boundaries as well
as spur the appearance of new
ones. Swinging weather
conditions will affect
application fields directly
exposed to climate conditions
such as mobility, energy, or
agrifood/ environmental
applications. ECS helping these
applications to react to those
abrupt changes need to be
integrated and packaged
themselves in a way that can
cope with these harsher
environmental conditions.
Integration, as the art of
recursively combining physical
devices, components, and systems
together to form a new entity
with increased functionality in
the minimum volume possible,
will be key to leverage the
different positive aspects of
diverse technologies and their
reference materials. It has been
already appraised that a
combination of nanoelectronics,
photonics (optoelectronics),
electronic smart systems,
including AI/ML subsystems, and
flexible, organic and printed
electronics is setting the path
for future enabling functional
electronics3,
which will be characterized by
aspects such as:
A shift from physical to
functional integration.
The use of novel
substrates and structural
systems.
Seamless integration in
everyday objects for a broad
spectrum of new
applications.
Real-time capture and
management of multi-physics data
and contextual
information.
Safe and secure
operation.
Networked, autonomous
operations complemented by
software solutions (including
AI).
Eco-design approaches at
product, process, and business
model levels.
The distinction of monolithic
and heterogeneous integration,
and what can be achieved with
them, is subjected to boundaries
that will evolve with time.
Particularly, monolithic
integration at chip, chiplet,
and SoC levels is progressing
through the development and
maximum exploitation of 3D
sequential integration, a
technology with important
research activities in the EU
that will impact applications
with very high-density
interconnections (IoT,
neuromorphic computing, etc.).
Heterogeneous integration, from
non-CMOS materials and device
processing on top of CMOS wafers
to customized application
platforms, is also progressing
thanks to the evolution of
scalable wafer-level or
package-level integration
schemes nurturing compact
System-in-Package (SiP). Still,
maximum versatility comes with
integration of technologically
dissimilar components (e.g.
MEMS/MOEMS- NEMS and ICs,
electronic and photonic
elements, etc.) onto
application-oriented platforms,
which could be board-like or
built on flexible/conformal
substrates. To serve such
versatility, 3D place-and-route
tools with extended ranges of
speed, precision, and gentleness
for handling components that on
occasions are fragile, will be
needed. Modelling/simulation,
characterization and reliability
evaluation tools, which are also
strong European domains, will be
required to take all the new
materials, technologies, device
architectures and operation
conditions into account, so that
cost of development is reduced,
and technology optimization is
speeded up. All those
encompassing schemes are
expected to be beneficial for
the integration of future
high-performance sustainable,
secure, ubiquitous, and
pervasive systems, which will be
of great added value for many
applications in the field of
detection and communication of
health problems, environmental
quality, secure transport,
building and industrial
monitoring, entertainment,
education, etc.
The next generation cyber
physical systems will play a key
role in the future AI, IoT, SoS
realisations, while they will
need to be sustainable and easy
to maintain, update and upgrade
in a cost-effective way, across
their complete lifecycle. Mature
software platforms running on
them will ensure safety and
security by design and be
available as a part of the
European digital infrastructure
to a wide audience for building
services and business.
We envision an open
marketplace for software
frameworks, middleware and
digital twins with a seamless
integration and ubiquitous
presence that will represent a
backbone for the future
development of one-of-a-kind
products. While such artefacts
need to exploit the existing
software stacks and hardware,
they also need to support
correct and high-quality
software by design.
Thus, the envisioned
long-term achievements in
embedded software will drive the
digital industry, while enabling
collaborative product-service
engineering, and making sure to
be inclusive.
For overcoming challenges
related to efficient engineering
of software, new programming
languages and tools for
developing large-scale
applications for embedded SoS
will emerge. Software
engineering will address hybrid
distributed computing platforms,
including efficient software
portability, and the development
of new software architectures
involving edge computing will
follow. Model-based testing will
contribute to handle
uncontrolled SoS.
Short-delivery cycles,
maintenance, and extension of
software systems are goals that
require continuous integration
and deployment. Autonomous
embedded systems and autonomous
processes for IoT & edge
embedded HW/SW co-design, and
integration & orchestration
platforms for IoT and SoS will
contribute to achieving those
goals. Model-based engineering,
based on multi-dimensional,
complex and of scale digital
twins in the edge, as well as
their deployment along with
systems, will contribute to a
continuous integration. Next
generation hybrid digital twins,
based on big data-driven and
classical physics principles,
will be developed and integrated
in embedded hardware, while
supporting enhanced cognition
and intelligence that will
demonstrate enhanced
capabilities to encapsulate the
real word, e.g. power modules,
while enabling unseen
capabilities for supporting
high-level missions as the Green
Deal.
As anticipated, software in
cyber physical systems must
support sustainability:
approaches for lifecycle
management will enable this by
supporting distinction between
core systems capabilities and
applications and services, and
by enabling interplay with
legacy subsystems.
Interoperability must be
built-in and ensured by
integration platforms, and will
enable features such as easy SW
updates, device management, and
data management. Composability
of systems will be a property
supported by properties
contracts and orchestration
systems, and will be directed
towards “write once, run
anywhere” for optimal execution
on the cloud-for-edge computing
continuum.
The modularity of future
cyber physical systems, which
will dynamically compose in
large SoS, will require a
high-level of trust, both at the
level of the constituent
components of SoS and
considering how they connect and
compose. To this regard, the
evolution towards stronger
protocols and interfaces
(including well-defined
pre-compilation connections),
supporting security, privacy,
and dependability aspects,
represents a key factor.
Use of safe, trustworthy
& explainable AI will be
dominant in autonomous systems
and will enable embedded
intelligence. AI will play
several key unconventional roles
in innovation, e.g. as a tool
for SW development/ engineering.
These innovations will be
supported by the European
Processor Initiative and its
integration in cloud servers,
open-source hardware, and
software.
Finally, use of quantum
computing and IoT digital twin
simulation will support software
reliability and trust.
System of Systems (SoS) is
projected to become an area of
exceptional economic growth,
both short term and over the
coming decades4.
This will create a strong market
pull for the complete ECS value
network upstream of the SoS
area.
Strategic investments
addressing open platforms,
engineering, and deployment
efficiency, SoS management and
control represent key factors to
propel Europe towards very large
scale of digitalization and
automation solutions across
integrated and optimised
operations of engineering,
production, logistics,
infrastructures, etc. The
inherent heterogeneity of SoS is
expanded with the new, emerging
computational models that
include accelerators, AI/ML
subsystems, approximate
computing, organic systems, and
others, pose significant
challenges at all levels.
Interoperability and adaptation
to diverse physical interfaces
and communicated data structures
constitute clear examples of
challenges. The management of
heterogeneity at all levels,
including dynamic instantiation
of multi-paradigm computing
resources considering
application requirements and
specifications,
auto-configuration of
distributed resources (locally
or globally) to satisfy
application functional and
non-functional requirements,
require significant advances in
the field. Success in this
direction requires additional
activities towards
standardization for HW/SW
functions as well as scalability
specifications to achieve wide
and cost-effective use in
applications and use case with
variable performance
requirements.
Large scale usage of SoS
technology is further expected
to be a significant contributor
to the Green Deal through
distributed and intelligent
solutions that provide
significant reduction of
environmental footprint in terms
of energy consumption, material
consumption, waste and, in
general, through a more rational
and controlled use of all types
of resources. This strengthens
the ECS value network through
energy efficient and robust
electronics hardware,
connectivity, and embedded
software.
The future evolution of SoS
will further require cooperation
between domains, enabling a
wider shared understanding of
the context and situation, more
useful services, richer
functionality, better user
experience and value
proposition. This evolution will
introduce the concept of
connected and interacting
domains (potentially both
physical and virtual), where
application and services run
transversally on top of
connected vertical domains.
In the medium/long term, many
technologies will allow the
evolution of SoS towards the
scenarios previously described,
including:
Distributed AI, to
control the inherent and quickly
increasing complexity of SoS,
making them secure, reliable,
easier to maintain,
etc.
Connected and interacting
domains, supported by:
Open and robust
integration platforms.
AI method adopted to
address conflicting functional
and non-functional
requirements.
Engineering support for
emerging behaviours in complex
SoS:
Model based
engineering.
Predictability,
controllability, monitoring and
diagnoses.
Automated and autonomous
engineering.
Machine interpretable
content.
Artificial intelligence will
be the enabling foundation for
the digital society, ensuring
that the systems that make up
its framework function in an
effective, efficient, secure,
and safe manner. Most of the
ambitions that are to be
realized in the digital society,
such as a zero-emission economy,
affordable healthcare for
everyone, safe and secure
transactions, etc., can be
achieved only if an underlying
AI infrastructure is in place.
This implies that the Internet
of Things will gradually
transform into the Artificial
Intelligence of Things (AIoT),
where AI constitutes the
interface between the digital
world (e.g. edge and cloud
computing, cognitive and
autonomous cyber-physical
systems, embedded systems) and
the analogue real world.
Artificial intelligence and
machine learning (AI/ML) methods
enable efficient and effective
automated decision making in
domains ranging from system
design, design space exploration
and manufacturing to application
and business processes. As
computing models distribute
functionality at all systems
from the cloud to the edge,
AI/ML methods need to be
distributed and coordinated,
leading to efficient smart
systems at all levels of the
computing hierarchy. In addition
to the ongoing research in AI/ML
for applications in increasing
application domains, efficient
and effective methods for
distributed intelligence and
federated learning become
increasingly important. Advanced
AI approaches, like composite
AI, require heterogeneous
technologies to be addressed
altogether, such as vision and
natural language processing. The
accelerating adoption of AI/ML,
in its various approaches,
results in high demand of
subsystems and accelerators,
creating a significant set of
challenges analogous only to the
growth of the respective market.
Importantly, considering the
known social questions regarding
the adoption of AI, significant
effort needs to be spent on
certifiable and explainable AI,
which will lead to the necessary
social acceptance of AI-related
technologies at all fronts.
AI technology is becoming
increasingly demanding for
computational power, especially
for the learning phase. As
Figure 4. shows, the need for
increased accuracy in AI
techniques leads to methods that
employ increasing numbers of
parameters (for deep learning
techniques, specifically, in
Figure 4.), which, in turn, lead
to dramatic increase of need for
computational power to implement
these techniques. This will
imply new progress in energy
efficiency to keep the Cost of
Ownership affordable. Mainly the
GAFAM and BAITX will be able to
afford the computing
infrastructure that will require
a large number of servers.
Figure 4.3 - The increase of parameters employed for deep learning systems (2018-2021)
IA accelerators will appear
in many devices from the deep
edge to home servers, allowing
to process all kind of data and
changing the way we interact
with computers. Figure 4.
demonstrates this, showing the
dramatic increase of deep
learning chiplets that have been
shipped worldwide in the last
years. Computing systems will
disappear in the environments
and will allow natural
interactions.
To be able to cope with the
diversity of requirements and
the Cambrian explosion of
designs, AI techniques will be
used to select the best
architecture (automatic design
space exploration) and to
generate the code from
high-level specifications
(no-code) with guarantees of
correctness. AI/ML application
in the ECS domain include the
automated design of SoCs, the
respective design space
exploration, the integration and
orchestration of multiple
computing paradigms into
embedded systems (including
AI-based ones) as well as the
design and evaluation of
cyber-physical systems overall,
which include physical
components for operation or
computation.
New AI paradigms will emerge,
including self-supervised
approaches, that can be highly
efficient at the edge and
decreasing the need for a large
database and computing power
like for deep learning. Quantum
computing, in addition to other
computational models, needs to
be explored for efficiency and
effectiveness in AI methods.
Figure 4.4 - The growth of deep learning chip shipments (2018-2025)
The mass market for smart
systems will still be fulfilled
by very advanced foundries, but
“small” foundries will also
appear (assuming that they
become sufficiently cheap and
efficient) to make the very
diversified deep edge devices
that most of the time don’t need
the latest technology. Some
low-cost applications will be
made with non-silicon
technologies, e.g. using
printing or 3D printing
technologies5.
This will allow enterprise level
organizations to have their own
“device manufacturing” that
don’t need high quality clean
rooms. AI/ML technologies will
play an important role in this
direction, towards high
automation that leverages AI/ML
for the exploration of
architectures and code
generation.
Today, connectivity is a key enabler to support the development of innovative applications in several markets, such as consumer, automotive, digital manufacturing, network infrastructure (to name a few). This is also pushed by the need of being connected anywhere+anyhow+anytime. The availability of innovative connectivity technologies, both wireless (e.g. low-power wide area networks (LPWAN), cellular (5/6G)) and wired (e.g. new bus-oriented communication protocols), will enable and enhance a wide range of new business opportunities for the European industry in the context of Systems of Systems (SoS), Cyber-Physical Systems (CPS), and Internet of Things (IoT). Long-term roadmaps for connectivity and interoperability will guide a seamless integration of heterogeneous technologies (hardware and software) for the design and implementation of complex connected systems in effective ways.
Connectivity is a critical asset to any digitalization and automation activity to strengthen Europe’s position and enable the European industry to capture new business opportunities associated with the connected world we live in. It is vital to support European technological leadership in connectivity, fostering digitization based on Internet of Things (IoT) and System of Systems (SoS) technologies; for example, this can be achieved by being at the forefront of new standard development for the current 5G initiative, the emerging SoS market, and the upcoming 6G initiative. Furthermore, to bring added value and differentiation with respect to US and Asian competitors, The European industry has to secure access to any innovative software and hardware technology that enables the efficient engineering of large and complex SoS (which will help to capture more value by targeting higher-end or more innovative applications, as highlighted by the Advancy report6). For instance, connectivity (e.g. in terms of wireless infrastructure market, led by Ericsson and Nokia) will be supported by the European leadership position in the traditional IT environments as well as the embedded segments, guaranteed by companies such as STMicroelectronics, Infineon and NXP. Connectivity from device, over the edge, and to the cloud will need to be virtualized, relying on run-time design, deployment, and management of integrated edge and cloud network architectures. This will enable the connectivity from cloud to far edge, shifting the perspective from point-to-point connectivity to application-to-application connectivity.
Connectivity engineering and management must be significantly improved to support simplified and inexpensive deployment and integration of new applications into SoS, CPS and IoT solutions.
Connectivity will provide the basis for a data layer supporting instant and seamless data and information exchange between producers (supply below the data layer) and consumers (demand above the data layer) within and between domains. This layer will enable large-scale integration of SoS, CPS and IoT solutions. Targeting systems and applications, we should consider the interconnection between sub-systems and focus on individual component technology development, according to needs identified at system or application level. To support this system vision, the promotion of innovative technology enabling heterogeneous integration is key. To fully leverage this heterogeneous integration at hardware level, software interoperability is a parallel challenge to provide connectivity that will allow for SoS integration. Thus, an alternative major challenge is to enable SoS integration through nearly lossless interoperability across protocols, encodings, and semantics. To do so, dedicated software tools, reference architecture and standardization are key to supporting SoS integration, thus enabling the provision of a scalable and evolvable SoS. As it remains very difficult to assume that highly customized embedded systems will be built based on a single, unified, high-level modelling principle and toolset, there is a quest for consolidation, or even the standardization of basic runtime frameworks, component libraries and subsystem interfaces that will ease the deployment of interoperable components into generic, domain-specific solutions and architectural frameworks in a bottom- up fashion. Such an approach is also expected to provide for better traceability of requirement validation, and formal verification of distributed system compositions and their emerging functional and non-functional properties.
Finally, data protection must be ensured at an appropriate level for each user and each functionality, regardless of the technology. One major challenge is to ensure security interoperability across any connectivity. This foresees the utilization of different connectivity technologies, and these differences create security incompatibilities leading to increased engineering costs. Therefore, the development of innovative hardware and software security solutions, that will support and provide correctness and safety, is of fundamental importance. Such a solution will have to be linked with the previous challenges to ease SoS engineering, deployment, and operation in a seamless manner. Security assessment is a significant issue here considering the criticality of applications. Standards and directives are required not only for technology transfer and system evaluation, but for legal purposes as well, considering the existing GDPR legal framework and the emerging laws regarding European and national cybersecurity requirements.
Thus, the following major challenges need to be addressed in the connectivity roadmap until 2050:
Enabling nearly lossless interoperability across protocols, encodings, and semantics.
Ensuring secure connectivity and interoperability.
The European ECS industry has been strong in systems engineering, integration, validation and verification, test and simulation, and certification of innovative ECS-based products. The produced systems are characterized by high quality in such terms as functionality, safety, security, reliability, trustworthiness, and certifiability.
Considering the need to maintain and increase this strength, we need to invest in extending existing, and developing new processes, methods and tools that will ensure European leadership in the field. Emerging ECS components and systems are characterized by new functionalities, increased complexity, and diversity on all fronts, ranging from methods and paradigms to modeling and analysis. As the competition by US and Asian ECS companies is fierce, significant effort and investment is necessary to enable European leadership in the technologies for integration, validation, verification, testing and certifiability. A significant parameter in the establishment of leadership and effective technologies is the support of the European Green Deal, by enabling green development and green ECS-based products. Tools and methods for managing the complete ECS lifecycle are necessary, ranging from resource-considerate and climate-neutral design and operation to development, production and maintenance of ECS-based products addressing issues that include even decommissioning and recycling.
To realize this vision and associated goals, the European efforts need to extend existing, and develop new, processes and methods that cover the whole lifecycle of products, from initial requirements elicitation through design, integration, verification, validation, test, certification, production to commissioning, operation, maintenance, and decommissioning. These processes and methods need to support data collection from production as well as from operation and maintenance to be analyzed and used for continued development and integration, updates in the field, validation, verification and test at the development phase, as well as in the field, at run-time.
Novel architectures and development and analysis tools need to be developed which will enable:
Seamless design, development, integration, verification, validation and test across all layers of the technology stack, from semiconductor up to systems of systems. These methods need to address individual ECS-based systems, groups of systems that form and dissolve statically or dynamically as well as systems that cooperate with other systems and with humans, at the cloud or at the edge. Furthermore, methods and tools need to support open platforms and integration of open systems.
Verification, validation, and test of highly automated and autonomous systems, especially coping with open-world assumption and uncertainty.
System development that includes AI methods, such as explainable and trusted AI.
The use of AI-based methods in the design and development process, for example design space exploration and analysis, including certified products.
Managing the increasing functionality, connectivity, and complexity of systems.
Managing the increased diversity of tools, such as modeling and description languages and simulation and testing tools, in emerging components, modules, and systems.
Quality, reliability, safety, and cybersecurity are fundamental components of any innovation in the digital economy. Especially in Europe these characteristics are particularly important since European products are well-known to be of high quality in almost every aspect. They are driven by high expectations of the European society demanding these features. Continuous evolution of our European society is driven by the development of electronic components and systems (ECS). By having now a mobile phone in our hands, we can buy a flight ticket, make a money transfer, or maintain social contacts. Even more services are available. ECS simply promises to make our lives more comfortable, safe, and efficient but this promise relies on user trust and acceptance concerning the perception of sufficient privacy, security, understandability, and usefulness in people’s daily lives. In the near future, highly automated and autonomous systems supported by AI will have a constant growing trend. We expect that in the next ten years such systems will be increasingly deployed, not only in controlled environments, such as in manufacturing industries, but massively spread in our personal, professional, and social spheres.
ECS of the future will not require an external environment control to work as wished. More generally, the ECS of the future will have to satisfy different constraints on different scientific and social disciplines and ought to meet both the founding principles of European society.
To maintain European leadership in electronic devices and systems we have to make our efforts to provide innovative products of the well-known high quality, reliability, safety and cybersecurity to our customers. From this perspective we expected the following challenges to be considered in a long term:
Development and integration of new materials for advanced packaging and interfaces, new characterization techniques, and new failure modes caused by new use-case scenarios.
AI/ML methods, including digital twins, to be a cornerstone of keep leadership regarding quality and reliability of ECS made in Europe and be an enabler for new data-driven business models.
Model-based engineering (incl. standardization of data management and processing) to be a key instrument for virtual release of ECS through the supply chain and shortening time to market.
Assuring user trust and acceptance of ECS through early inclusion of user requirements, explainability-by-design, and user education and training.
Model-based engineering (incl. standardization of data management and processing) to be a key instrument for virtual pre-qualification of ECS and shortening time-to-market.
Data transmission methods and protocols that are so reliable that they can be used to transmit life-sustaining information over long distances (e.g. for robotic surgery).
Reliable and certified software that can be kept even if the underlying hardware or hardware architecture is changed, including potential influence of SW updates on HW reliability.
Software that can adapt to a degrading underlying hardware to achieve a long-lasting and reliable HW/SW combination.
Liability of trusted AI-driven systems (it is based on trustworthiness of (AI-driven) systems, included safety and certification of AI-driven CPS, which is a main challenge in 2021).
Safely manage /design for human interactions in complex systems, SoS and application scenarios
Ensuring sustainability, cybersecurity, safety, and privacy, for AI-driven and quantum-based systems (based on “ensuring safety, security and privacy and sustainability of (AI-driven) systems”).
The attention to the environment and privacy have increased significantly in the population. This implies that conceiving any safety solutions is not enough. Our vision on 2030 is an integration between disciplines, which have nothing to do with safety or computer science in stricto sensu, such as privacy, social trust, liability, and sustainability.
Importantly, all properties of quality, reliability, trustworthiness and safety are heavily dependent on the integrity of the supply chain that is employed to develop ECS and ECS-based systems. The business models that are employed within Europe and abroad, lead to dependence of organizations on suppliers and contractors for provisioning of hardware as well as software components and systems. Trust among them is not a given. Actually, many cybersecurity incidents that have hit headlines originate from exploited vulnerabilities in the supply chain. Conventional verification and testing methods are not sufficient to address these problems, which range from insertion of hardware trojans at fabrication plants and implanting malicious hardware components in systems to compromised system and application software. Addressing these problems requires significant research effort for new methods to validate systems at all levels of hardware and software.
The recent advances in machine learning (ML) and artificial intelligence (AI) have opened several opportunities in hardware design and design tools, especially in embedded computing and design of cyber-physical systems. The major directions are: (i) exploitation of AI/ML techniques in component and system design, (ii) architectures and designs for efficient AI/ML processing in emerging solutions, and (iii) effective AI/ML methods for embedded and cyber-physical application domains. Thus, AI/ML is increasingly becoming a fundamental technology that influences all foundational technology layers and cross-sectional technologies depicted in the model shown in Figure 2.
The increasing complexity of integrated circuits (IC), still following Moore’s Law, leads to a (still) exponentially increasing design space that needs to be explored for IC designs at all levels of design abstraction. The problem of design space exploration is not new and has been addressed with various methods in the past, in the context of design synthesis. The problem includes identification of designs that meet design specifications as well as optimization of desired parameters, such as delay, area, power, etc. Heuristic algorithms have been employed extensively to identify appropriate designs, since exhaustive search of design spaces for modern IC’s is infeasible. AI/ML methods are increasingly adopted in design space exploration in place or complementing heuristic algorithms at several stages of circuit design, from manufacturing and physical design to RTL design and high-level synthesis. Importantly, AI/ML are increasingly exploited in design testing and verification.
AI/ML methods are used in several approaches to address different problems in the system design process. A comprehensive review of the problems and approaches is given by M. Rapp et al.7, which classifies the problem types solved with ML with 3 parameters: (i) development of predictions, actions and/or data, (ii) the design stage and (iii) the exploited AI/ML algorithm. In terms of predictions, currently, AI/ML methods are used and proposed for predicting design properties after explored decisions, while for actions, they are used in place of existing techniques at various stages of design, such as RTL design, placement and routing, etc., with the purpose to optimize an appropriate criterion. As most of the employed algorithms are supervised learning ones, data constitute a significant component of effective employment of AI/ML techniques. As such data do not exist or may differ significantly in practice, because of the different existing tool chains, AI/ML methods are used to produce or collect training data within and from the design process itself. Employed algorithms range from regression to neural networks, mostly for supervised learning, based on models that range from graphs to images at the various stages of the design process.
Despite the large and increasing research work in employing AI/ML in embedded system design, there are several challenges that need to be addressed for the penetration of research results in practical design frameworks. The biggest problem is that of appropriate data for training the models; this is a well-known problem in most application domains of AI/ML. The need for large amounts of appropriate data, considering the exponential space of design alternatives that need to be evaluated, will lead to long delays until the appropriate models are built. Alternative methods for generating appropriate date will also need to be developed.
Machine Learning (ML) and, more generally, Artificial Intelligence (AI) algorithms are essential to extract relevant information from massive data amounts. As the computational capabilities of edge- and end-devices (also indicated as “far edge”) is ever increasing, ML-based inference processing is no longer carried out in the cloud, but is distributed across the device-edge-cloud continuum. This requires the identification of a proper architecture to efficiently distribute the computational load, with dynamic methods for model compression or model splitting among the architecture tiers. Edge AI or, more generally, edge intelligence has emerged as crucial in multiple applications. Likewise, the possibility of implementing tiny ML (TinyML) algorithms in constrained devices opens new perspective in the Internet of Things (IoT). Distributed ML will involve several aspects, including latency, bandwidth utilization, data safety, data quantization, privacy, cost.
AI/ML accelerators constitute innovative components in new generation computing systems of all scales, from large and core to edge. Research in this area is continuously growing with accelerators that target application domains, from low-power embedded systems for inference to back-end high- performance accelerators for model training. Several surveys exist presenting methodologies for AI/ML accelerations, especially for neural networks8,9,10 , while there also exist surveys of commercial accelerators11. The wide range of application domains of accelerators poses significant challenges to designs, from performance to power to cost. Especially for embedded systems, accelerators are effectively targeted for edge systems, from automotive to medical, from industry to consumer. From the surveys it becomes alarmingly clear that most technologies are developed and commercialized by organisations outside Europe. European efforts exist, such as AnIA12 , Axelera AI’s AI Edge accelerator, Infineon’s Parallel Processing Unit (PPU) AI accelerator13 and others. However, market penetration, even in the start-up domain, is limited when compared to the US and Asia. Efforts focus on specific application domains, such as computer vision, automotive, etc. Strengthening of these efforts is necessary, to address challenges for cost-effective accelerators in a wide range of domains. The challenges include development of effective architectures and exploration of the trade-offs among memory design, power consumption, high performance and arithmetic calculation precision that dominate AI/ML neural network accelerators, in conjunction with specialized algorithms.
Significant challenges also exist in adopting AI/ML in embedded and cyber-physical application domains. AI/ML methods are already used and are continuously being developed for effective operation of embedded and cyber-physical systems in various environments, through predicting operational parameters, including input, and/or environmental parameters. However, often, the unpredictability of parameters combined with the lack of effective training data leads to suboptimal operation and creates a significant challenge to develop effective solutions that are application dependent.
Quantum technologies are promising to provide effective solutions to a wide range of application domains. Some application-specific systems are at the first steps of commercialisation, such as quantum sensors for gravitometers and neuroimaging and quantum communication devices specifically for quantum key distribution. At the other end, quantum computing requires significant research and advanced development effort to produce large scale, fault tolerant and general-purpose systems. Considering the challenges and the characteristics of quantum systems, the expectation is that quantum computing will complement conventional computing and digital systems, focusing on specific complex problems such as fractioning large numbers, optimizations requiring multiple variables, solving quantum problems as in quantum chemistry and physics, data analysis and sorting, and data encryption.
Quantum systems have been developed exploiting the wave-particle duality at the physics level and their ability to achieve discrete states. Their power originates from the exploitation of their superposition and entanglement properties. Superposition refers to their ability to exist in two or more states at once, while entanglement refers to their ability to create interdependencies among particles even when separated by large distances.
Superposition and entanglement enable powerful operations in terms of computing and communications, considering that entanglement promises high speed and secure communications while superposition coupled with entanglement enables effective parallel processing. Quantum networks and a Quantum Internet are the targets of several research efforts, including a test site in Chicago in the USA. Quantum computers are the focus of several efforts around the world, from large companies, such as IBM, Google and Intel, and small companies, like Rigetti, IonQ and Oxford Quantum Circuits, to universities such as TU Delft in Europe and the University of Science and Technology of China.
Quantum operations in computing and communication systems are based on quantum bits, named qubits, where a qubit constitutes the smallest unit in which quantum information is generated, transduced, processed, stored, and transmitted. A qubit is a superposition of the classical binary states (0,1). Analogously to classical computers, quantum computers perform a series of operations (a quantum algorithm) to modify qubit superpositions (probability of being in a particular state) and entanglements to increase some probabilities and to reduce others. Measurement of a qubit causes its state, and the states of entangled qubits, to collapse to either ‘0’ or ‘1’ with a probability dependent on the state of the qubit at the time of measurement. The goal is to maximize the probability of measuring the correct answer.
There are two main quantum computing models today:
Analog or adiabatic quantum computing, and
Gate-based quantum computation.
Analog quantum computing is typically based on quantum annealing, which performs processing by initialisation of the system followed by slow, global control of the qubits towards a final state and readout. In this computational model, the energy state of a quantum system encodes or models a problem and the energy landscape, which starts flat, changes slowly and continuously to a final state with the energy peaks and valleys representing the problem to be solved. Analog computers constitute some of the most developed quantum systems to date, but their functionality is limited to simple and very specific problems, because they have limited ability to reduce noise, which impairs qubit quality.
Gate-based quantum computation is analogous to traditional gate-based computers, using a sequence of quantum gates, each composed of a few qubits, that perform logical operations, followed by measurement. Unlike many classical logic gates, quantum logic gates are reversible. A universal set of quantum gates is still needed to achieve the full capability of quantum computation.
Gate-based quantum computing is error prone, due to noise. High gate and qubit error rates limit the scalability of quantum systems, leading to the challenging requirement for fault-tolerant quantum computation (FTQC) that would enable system scalability. In this direction, gate error rates as well as qubit and gate fidelity are metrics for the robustness of gate operations; qubit fidelity measures the loss of qubit coherence due to its interaction with its environment and due to shifts of its quantum states over time. These metrics essentially measure how closely actual gate operations match -on average- ideal versions of these operations. Analogously to classical computing error correction methods, quantum error correction (QEC) dramatically lowers effective error rates by encoding the quantum state using redundant “physical” qubits and using a QEC code to emulate stable qubits with very low error rates, often called “fault-tolerant” or “logical” qubits. Importantly, logical qubits currently require a large number of physical qubits and many quantum gates to maintain their state, incurring significant resource overhead in terms of both additional qubits for each “logical” qubit, and additional quantum gates for each logical operation. As an example, for simulating a chemical structure, a FTQC computer based on 111 “logical” qubits would require 108 and 105 “physical” qubits when the physical qubit error rate decreases from 10-3 to 10-914. The development of an accurate and general-purpose quantum computer would need between 104-106 “logical” qubits (Fig. 1) and a quantum and gate fidelity around (1-10-15) for industrial applications15. Since QEC leads to considerable overhead with conventional technology, Noisy Intermediate Scale Quantum (NISQ) computers are an intermediate step towards large scale error corrected fault-tolerant quantum computers (FTQC). NISQ computers use qubits without QEC, and although not fully fault-tolerant, they are expected to be practical in the near term for some applications. NISQ computers will require around 102 to 104 logical qubits (Fig. 1) and a very high qubit fidelity, improving the mean error rate of current quantum computers which reaches 4%. To reduce the impact of environmental noise, and of the unwanted interactions on qubit fidelity, quantum error mitigation (QEM) algorithms are used to mitigate these errors rather than completely remove them.
Quantum computers are expected to find their first use cases in combination with classical computers, so that the QC will serve as an accelerator, that solves the one part of the computation that is computationally expensive for a classical computer, and the classical computer runs other tasks. In this way, even a relatively small and noisy quantum computer can provide benefit in a larger problem.
Figure 5 shows the expected evolution of the number of “logical” qubits for both FTQC and NISQ approaches.
Figure 4.5 - The trend of the number of “logical” qubits and the goal for NISQ and FTQC approaches16
The number of qubits, and qubit and quantum gate fidelity in a system are the defining parameter for effective quantum computing. For this reason, significant research and development effort is spent on investigating alternative technologies for qubit manufacturing. Current efforts focus on superconducting qubits, semiconductor gate-defined quantum dots, color centers, trapped ions, cold atoms, photons and topologically protected Majorana modes. These alternative qubit technologies differ significantly in physical aspects, operational and miniaturization challenges. Neutral atoms in vacuum are entirely different from superconducting and electron spin qubits, and photon-based qubits, also named “flying qubits”, are also quite different from other types of qubits which are static in location; in contrast to solid-state qubits, photons do not have significant decoherence problems but are harder to generate, control and detect in a deterministic way. Figure 6 provides a mapping of current qubit technologies in terms of maturity and research intensity, while Table 1 presents the properties of the alternative qubit manufacturing technologies.
Table 1: Properties of various qubit manufacturing technologies for quantum computing
Qubit technology
Superconductor
Trapped ion
Si e- spin
Cold atom
Photon
Color centers
Qubit size
100µm²
1mm²
100nm²
1-10µm²
5-25µm²
100nm²
Quantum gate
Microwave Magnetic field
Laser Microwave
Magnetic field
Laser Microwave
Interference
Microwave
1-qubit fidelity
99.96%
99.999%
99.93%
99.9%
99.9952%
2-qubit fidelity
99.3%
99.9%
> 99%
99.5%
98%
99.2%
Gate speed
12-400ns
100µs
1µs
0.4-2µs
1ns
20-50ns
Coherence time T2
150µs
50s
20ms
40s
150µs
0.6s
Variability
3%
0.01%
0.1-0.5%
-
0.5%
large
Operation Temperature
15mK
10K
1K
4K
4-10K
≈ 273K
Entangled qubits
433
32
6
256
≈ 20
5
Figure 4.6 - Core qubit technologies mapped by maturity, intensity, and disruption potential17
The different quantum computing approaches lead to challenges in problem-solving aspects of quantum computers, in addition to the manufacturing issues. Quantum algorithms and software for quantum computing are in their first steps and present significant challenges. The goals of user-friendly quantum computers that are programmed effectively and efficiently require significant advances in terms of algorithms and software tools. Recent advances in specialized application domains such as localisation, optimisation and machine learning exploit quantum computers effectively but require very specialised knowledge. Popular current environments and tools, such as Qiskit, demonstrate the ability of tools to make quantum computing more attractive to new generations of engineers, but the challenge of producing tools such as appropriate compilers and interpreters, needs to be met in order to enable a sufficiently wide adoption and evolution of quantum computing.
The European Union has issued ambitious policy statements regarding transport and smart mobility:
Emissions from transport could be reduced to more than 60% below 1990 levels by 2050.
The EU has adopted the Vision Zero and Safe System approach, to eliminate deaths and serious injuries on European roads.
Sustainable Mobility for Europe: safe, connected, and clean.
To realise this vision, possible scenarios include the projection that mainly autonomous and electrically driven vehicles (FEVs) will be on the road, and that all road users will be connected. It is envisaged that other road users (bicycles, pedestrians, public transport) will also participate in this connected, autonomous model, in addition to transportation network infrastructure (tolls, signals, etc.), creating an augmented Internet of Vehicles. Key networking technologies, such as the emerging 5G cellular connections with their very low latency (ms range) and the powerful edge nodes (Mobile Edge Computing, MEC), will enable highly effective vehicular communications for traffic management and safety applications. Railways and maritime transport will also become more autonomous. Fully integrated multimodal traffic will be applied, in which air, railways and maritime are fully integrated with road transport.
Until now, the rule was simple: more income equals more travel distance as Figure 7 indicates. Will this very simple equation still work in the future?
Figure 4.7 - The correlation between growth and individual mobility
Many attempts have been made in the past to move people from individual to public transport. Huge investments have been made into infrastructure, fast trains have been deployed massively, bus lines installed. Nevertheless, this did not change the distribution of shares in the transport between planes, trains and cars. It could just allow to keep pace in an ever-increasing mobility. The most important means of transport is still, and will also very probably be in the next decade, the individual car.
If we want to reduce emissions and energy waste, then we need to focus on this means of transport, and make it more ecologically friendly. It must use less space and energy.
The global mobility is undergoing a significant change triggered by the increasing thread of global warming. The European Union started the Green Deal, which is radically changing the way mobility works. Classical fossil fuel-based transport and mobility will be completely replaced by CO2 neutral mobility. As all the alternative mobility systems, be it battery-based, H2 based or synthetic-fuel-based, have their individual challenges, all of them have in common that CO2 neutrality is only possible in using a connected, shared and energy-usage minimized mobility network. To reduce the space needed by cars, the best solution would be to use them better during the day by sharing their usage.
Owning cars should thus be replaced by shared mobility and intermodal transportation offering the most convenient and CO2 neutral way to move goods or persons from point A to point B. This will be only possible by a stable, everywhere available, user-friendly, secure, fast communication system connecting people, traffic operations, cars, trucks, busses, airplanes, ships, busses, etc. across the globe.
Today’s vehicles contain more software than any other embedded system and most compute applications. Tomorrow’s vehicles will multiply the software lines of code by a factor 6. Semiconductor value in the car will more than double through the next 10 years (from 600$ to 1200$). Some already talk about ‘’software- enabled vehicles” or “data-centers on wheels”. Besides the electronics and software to get the car rolling, there will also be a lot of complexity added in terms of performing safety & security checks and to monitor the health or lifetime of electronic components and batteries. Overall, the evolution of the car and mobility in general results in the rise of complexity in electronics and software that has almost become uncontrollable.
Additionally, the growing global population as well as the aging society will be supported by more and more automated transport means at all mobility variants taking the best advantage of the available resources as roads, parking space, airspace, and water space, and serving best the needs for mobility of the society. This will be supported by sensors combining different sensor principles in one sensor with significantly less power consumption, as well as new AI optimized edge computers in the transport vehicles. These systems will be part of completely new HW/SW systems spanning from sensors via embedded edge computers via predictable, fast, clean (also for the user), safe, secure, and failsafe communication to globally interconnected cloud systems.
Power electronics is the enabling technology for the efficient generation, conversion, distribution, and usage of electrical energy. It is a cross-functional technology covering very high gigawatt (GW) power (e.g. in energy transmission lines) down to the very low milliwatt (mW) power needed to operate a mobile phone, and even to microwatt (μW) to power autonomous sensor nodes. Many market segments, such as domestic and office appliances, computers and communication, ventilation, air conditioning and lighting, factory automation and drives, traction, automotive and renewable energy, can potentially benefit from the application of power electronics technology. The ambitious goals of the EU to reduce energy consumption and CO2 emissions can only be achieved through extensive application and use of power electronics, as this is the basic prerequisite for:
Efficiently feeding wind and solar energy into the power grids.
The stabilization of the power grids with an increased share of fluctuating renewable energy sources.
Highly efficient, variable speed, motor drives.
Energy-efficient and low-emission mobility with hybrid and full electric vehicles.
Energy-saving lighting technology.
Efficient recovery of braking energy.
Energy management of batteries.
Control appliances and building management systems via the grid interface (smart grids).
The estimated energy savings that can be achieved by introducing state-of-the-art and future power electronics components into systems is enormous, estimated at more than 25% of current electricity consumption in the EU. Since power electronics is a key technology in achieving a sustainable energy society, the demand for power electronics solutions will show significant growth over the coming decades. The European industry holds a strong position in the field of silicon-based power semiconductors and modules and is establishing a robust foundation for future progress in wide bandgap semiconductor technology. Europe also has high-quality power electronics research groups at universities and research institutes with well-established networks and associations across Europe to provide platforms for discussion, cooperation, and joint research.
A long-term roadmap for power technology needs to cover different sectors.
New, highly-efficient power devices based on wide-bandgap semiconductor materials such as SiC and GaN-on-silicon, and possibly Ga2O3, AlN, diamond, diamond-on-silicon, or nanowire-based materials.
New, cost-efficient, Si-based power devices to enable high efficiencies for mass-market applications such as super-junction MOSFETs.
Power management for very low-power applications as required for IoT, including the development of energy harvesting technologies, covering the full range from GW to μW levels.
High temperature-capable packages serving new materials and 3D technologies that offer the highest requirements and integration capabilities.
In the energy roadmap towards 2050, five major challenges were identified:
Smart & Efficient – Managing Energy Generation, Conversion and Storage Systems, trying to fulfil the vision of loss-free energy conversion and generation.
Energy management from On-Site to Distribution Systems.
Transmission grids with the goal to achieve in 2020 solutions to cope with rising grid loads as a base for the carbon-free energy transition of Europe.
Efficient Community and Regional Energy management.
Cross-Sectional Tasks for Energy System Monitoring & Control, so that highly integrated monitoring and control of energy systems and grids, are achieved utilizing innovative ECS-based solutions.
These challenges need to be addressed to achieve the current EU policy target of 30% savings by 2030 by utilising innovative ECS-based solutions, as well as the milestones of (a) -55% GHG emissions until 2030 (getting closer to zero emissions due in 2050) and (b) grid integration. To realize this vision, we need to target the decentralisation of energy sources, opportunities with networked systems, limitations in peak electricity supply, oversupply times, new demand for electric energy supply for urban mobility, and the introduction of storage systems. This will lead to new challenges in energy management providing flexibility, stability and reliability in the grids and distribution for communities and cities. Furthermore, we need to develop components for HV transmission, of 1.2 MV or even higher voltages, to roll out an efficient energy transmission over Europe. Also, we need to combine local generation & demand site management with transmission & distribution grid operation & control technologies from sub-MW to GW scale, and we need to develop resilient solutions coping with adverse conditions resulting from the advancing climate change.
Additional technical solutions are needed to increasing share of renewable energy generation, self- consumption (mainly heating/cooling and EV) and building optimization, as well as introducing and managing new types of renewable energy carriers like hydrogen.
Relevant promising technologies, already under use and extension, include (a) artificial intelligence & advanced communication techniques for cyber-security increasing resilient energy system control, and (b) optimal control of distributed generation and dispersed energy-storage devices as well as robust, high power control devices.
Digital Industry is a must of European productive and commercial evolution on the next decade, following and empowering the EU policy related to digitalisation. Digital capabilities and functions will be the enabler for safer, greener, sustainable, lower cost and more productive, autonomous, and competitive EU industrial ecosystem.
EU planned, mostly after the Covid experience and lesson learnt, strategic investments addressing digitalisation including industrial productive arena, edge technological engineering studies, developments and deployment efficiency and services, addressing, in addition to industrial production, logistics, transportation, health, critical EU infrastructure etc.
The future evolution of EU Industry into Digital Industry will further require cooperation between multiple domains enabling a wider shared understanding of the context and situation, filling the gap towards EU industrial strategic autonomy and more useful services, richer functionalities, better user experience and value proposition introducing the concept of connected and interacting domains.
The manufacturing industry can essentially be classified into two main categories: process industry and discrete product manufacturing. The process industry transforms material resources (raw materials, feedstock) during a (typical) (semi)continuous conversion into a new material that has significantly different physical and chemical properties than the starting substance. Discrete manufacturing refers to the production of distinct items. Automobiles, furniture, toys, smartphones, and airplanes are examples of discrete manufacturing products. The resulting products are easily identifiable and differ greatly from process manufacturing where the products are undifferentiated, for example oil, natural gas, and salt. Another meaningful way to distinguish between manufacturing industries is by dissecting the domain by the end-product categories, such as energy industry, chemical industry, petrochemical (oil & gas), food industry, pharmaceutical industry, pulp & paper industry, steel industry (process industries), and furthermore car manufacturing, machine industry, robotics, and the semiconductor industry. Also, these subdomains constitute significant industrial domains for Europe. These industries are ever more demanding and voluminous consumers of ECS technologies such as sensors, big data, artificial intelligence, real-time system, digital twins, safety & security, computing systems, lifecycle engineering, human-system integration etc. ECS technologies are essential parts of most of the advances in these domains.
The perspective of industry is reflected in several efforts. The major ones are described in the following:
The SPIRE18 Roadmap 2030 and the SPIRE Vision 2050, which lists the following targets.
Replacement of fossil-based materials by bio-based materials requiring completely new processes.
Re-use of waste streams that require complete redesign of materials, products, and related production processes.
New resource efficient applications that require completely new designed processes.
Complete redesign of industrial parks to realize industrial symbiosis.
The Factories of the Future (EFFRA19) roadmap summarizes its vision as follows:
Agile value networks: lot-size one – distributed manufacturing.
Excellence in manufacturing: advanced manufacturing processes and services for zero- defect and innovative processes and products.
The human factor: developing human competences in synergy with technological progress.
Sustainable value networks: manufacturing driving the circular economy.
Interoperable digital manufacturing platforms: supporting an eco-system of manufacturing services.
The Connected Factories20 project forecasts the emergence of new manufacturing concepts, such as:
Hyperconnected factories.
Autonomous factories.
Collaborative product-service factories.
Recently, a federation was set up of the three electronics ecosystems in Europe in nanoelectronics, electronic smart systems and flexible, organic & printed electronics (https://5e-project.eu/). Combinations of nanoelectronics and flexible organic & printed electronics that provide functionalities to electronic smart systems lead to novel solutions. This trend to functional electronics is characterized by the following aspects:
A shift from physical to functional integration.
The use of novel substrates and structural systems.
Eco-design approaches at product, process, and business model levels.
Real time capture & management of multi-physics data and contextual information.
Networked, autonomous operations complemented by software solutions (incl. AI).
Seamless integration in everyday objects in a broad spectrum of new applications.
All these efforts provide high-level targets, which translate into diverse and much more concrete targets in each domain, ending up also in a number of technology challenges in this ECS-SRIA, such as distributed AI along the edge-to-cloud continuum, computation and simulation capabilities, communications and interacting domains, engineering support for emerging behaviors in complex SoS, model-based engineering, predictability, controllability, monitoring and diagnosis, automation, autonomy and robotics, teleoperation, telepresence, simulation and training.
Clearly, ECS technologies that enable distributed Industrial IoT (IIoT) systems to monitor and control manufacturing systems and processes will enable disruptive industrial innovations and realise the vision of Industry 4.0 and the Industrial Internet that will lead manufacturing worldwide. Overall, these long-term trends translate into the need to invest in technology research and innovation projects in the following areas:
The rise of artificial intelligence (AI), a powerful edge, and cloud computing networks; methods and algorithms need to evolve to more complex, reliable and explainable AI.
Collection of measurement data, including image, video, and 3D animation, and, in general, large volumes of heterogeneous and unstructured data.
New production schemes such as:
Modular factories, i.e., smaller standard units to be assembled according to needs, also mobile units.
More end-user driven agile production, i.e. end-users more connected to production and logistics chains.
Hyper-connected factories.
New production technologies, e.g. 3D printing, and other novel emerging methods, leading to production that is closer to customers.
Methods to extend closed-loop production lines to closed-loop regions (extensive recycling, net energy, zero-emission and waste, close to end-users).
Autonomous to human-machine co-work, to enable flexibility and reduce excessive complexity.
Recyclable electronics, since the digital industry will increasingly become a producer and enabler of “green electronics over the next decade”, leading to the need to recycle as many electronic components and systems as possible.
The rising cost of healthcare, caused by an aging population, is one of the major challenges that present-day society must deal with. To keep healthcare accessible and affordable for everyone, it will change radically in the coming decades. Healthcare will become increasingly decentralised and personalised, as medical care will move from the hospital to people’s homes as much as possible. This transition in healthcare can only be achieved through the massive development of digital healthcare devices that can provide personalized monitoring, mentoring and treatment.
ECS will keep on being key enablers to realize the continuum of healthcare, notably in linking well-being, diagnostics, therapeutic approaches and rehabilitation issues. In addition to providing the tools for personal management of individual health and monitoring of health condition, ECS and smart systems will play an active role in assistive technologies with the goal to reduce inequalities linked to impairments originating in loss of physiological or anatomical structure or function after a disease or an accident. Ambient Assisted Living (AAL) is a high-priority direction for Europe, to support its increasing aging population.
In the long term, personalised and patient-tailored healthcare will be at the forefront of technology advancement. Further miniaturization of biomedical devices and integration of smart integrated systems (e.g. smart catheters, electroceuticals) will have significant impact on point of care diagnosis and treatment. Real-time localized detection of disease and minimally invasive targeted drug delivery will be a key priority. Achieving enhanced reliability and building stakeholder confidence in these technology advancements will be key to successful implementation. Data integrity and security around the use and storage of personal information will require new methods of application development and a robust system of operation, especially if moving towards a more connected healthcare approach with more focus on tailored patient diagnosis and treatment.
Beyond those technological challenges, aspects such as reliability, safety and privacy issues in terms of regulation and uptake by practitioners, especially when dealing with procurement policies, have to be tackled. A priority will be in bringing these stakeholders closer in the involvement phase of developing key enabling technologies (KETs) for healthcare applications with a customer pull and technology push approach.
Improvements in medicine over the ages greatly benefited from advancements in other disciplines. Medicine evolved over time from a “mechanical” medicine (surgery) toward “chemistry” medicine and more recently biotech medicine. Nowadays, the development in ICT and digitization has an important impact on the way healthcare is addressed. In ten years from now “digital medicine” will be deployed, and will complement, not necessarily replace, the tools offered to medicine to improve the benefits for patients and medical professionals.
These tools may include, for instance, human models also known as the “digital twin”. Here, ECS will have a crucial role in ensuring the necessary link between the digital and the real twins. Real time acquisition and processing of data and vital parameters collected from on-body IoT sensors, is a key technology that will advance existing wearables and will enable identification and prediction of a person’s condition. The use of AI technologies, based on extended measurement data, will enable significant advances in this area.
Finally, progress in interfacing electronics components and systems with biological systems will offer seamless connection to the body for continuous monitoring but also for electrostimulation purposes. Results from the human brain flagship project will provide input for improved deep brain stimulation. Electroceuticals and nerve stimulation will enhance treatments of diseases and partially replace pharmaceutic treatments, thus avoiding side effects.
Some additional developments are presented in the following:
Fully personalized medicine will be enabled by smart monitoring of health parameters, including factors from the molecular to the environmental levels. Developments in healthcare will benefit from the concept of “digital twins”, so that prediction of health evolution and preventive treatment will become reality and standard procedures. Fully personalized and accurate health data will be available anywhere, anytime.
Drug development will be assisted by emerging methodologies such as ‘organ-on-chip’.
3D-bioprinting. Medicine is highly benefiting from advancement in other disciplines such as genomics or 3D printing. Combining 3D printing of living material and of electronic systems will develop a bottom-up approach to medicine, with advanced and personalized prosthetics and implants increasing biocompatibility, solving the problem of powering, and increasing quality of life.
Cyborgisation. Future Brain-Computer Interface (BCI) technology will enable new ways of communication, e.g. for people with severe disabilities. By the 2040s wearable or implantable BCI technology will probably make smartphones obsolete. Due to the massive exposition of the physical and biological world in cyberspace, BCI systems will have to incorporate new means of protection of technology, data, and consciousness – like heartbeat, venous system, fMRI or 'Brainprints' as the top measures of security.
These innovations in the medical domain can be accelerated by the creation of an ECS-based technology platform for medical applications. The Health.E Lighthouse21 initiative has compiled a list of emerging medical domains where further technical developments are required:
Bioelectronic medicines.
Organ-on-Chip.
Personal ultrasound.
X-ray free interventions.
Smart minimally invasive instruments.
Smart drug delivery.
Intelligent wound care.
Ambulatory monitoring.
Point-of-care diagnostics.
Remote sensing and monitoring.
E-health.
Despite this urgent need and the enormous resources that are being invested in research, true innovation in terms of products reaching the market has been slow. One of the root causes identified is the lack of open technology platforms. This will release the power of Moore’s Law, that has been the driving force in electronics for more than fifty years, to the healthcare domain: “Moore for Medical”. It is the vision of the Health.E Lighthouse that innovation can be accelerated by stimulating the development of truly open technology platforms.
The list of challenges that ECS will face in the next decade is changing and new issues, linked to the developments described above, will have to be addressed. Security and reliability remain major issues to guarantee safety and integrity of medicine. Regulation will have to be developed to address these concerns. Furthermore, ethical issues may become more and more critical in the uptake of patients and may lead to fundamental decisions in the way medicine will evolve.
Over the following decades the global population will increase, rising to an estimated peak of 9.78 billion by 2064. By the middle of the century, about two-thirds of the population will live in urban areas. This will require new digital approaches to supply the growing number of people with food, which will involve a great threat to food security for certain countries and especially for large cities. Digitalisation has already helped initiate open field farming through precision agriculture, but there are other ways of targeting this issue, especially by the emerging areas of “digital farming” and “vertical farming”. In this form of farming, plants are grown in vertical arrays, inside buildings, where growing conditions can be optimized. Crops are supplied with nutrients via a monitored system under artificial lighting and can thus be grown year-round. This method makes it possible to grow plants without soil and natural sunlight, with optimal growth conditions being created artificially. The full potential of this approach can only be achieved with the help of information technology (IT) and IoT components and paradigms such as AI and Industry 4.0, which all still need to be adopted for this purpose. With these digital farming approaches, it will be possible to secure food supply autonomy and food safety for large parts of the EU. Furthermore, investigation into the provision of corresponding technologies and approaches will enhance the strategic autonomy of Europe.
On top of this, and considering the huge negative impact by the climate change, the European Green Deal is a response to these challenges. It includes two main programs “From farm to fork” and “Biodiversity 2030” having a strong impact in the goals of this Chapter, which should contribute to reach the targets defined by these two programs by the introduction of the adequate ECS technologies and solutions.
From Farm to Fork
European food is already a global standard for food that is safe, plentiful, nutritious and of high quality. Now European food should also become the global standard for sustainability. EU agriculture, the manufacturing, processing, retailing, packaging, and transportation of food make a major contribution to air, soil and water pollution and GHG emissions, and has a profound impact on biodiversity. As such, food systems remain one of the key drivers of climate change and environmental degradation. For this reason, the From Farm to Fork action targets to reduce dependency on pesticides and antimicrobials, reduce excess fertilisation, increase organic farming, improve animal welfare, and reverse biodiversity loss.
Biodiversity Strategy for 2030
Biodiversity is also crucial for safeguarding EU and global food security. Biodiversity loss threatens our food systems22, putting our food security and nutrition at risk. Biodiversity also underpins healthy and nutritious diets and improves rural livelihoods and agricultural productivity23. For instance, more than 75% of global food crop types rely on animal pollination.
A sustainable food system will be essential to achieve the climate and environmental objectives of the Green Deal, while improving the incomes of primary producers and reinforcing EU’s competitiveness.
To contribute to reach the targets of these two programs, the long-term vision of this Chapter includes the following challenges:
Food security:
Intelligent and adaptive food production should take advantage of smart (bio) sensing for high-quality monitoring to reduce the amount of water and chemicals used in such processes, and to prevent contamination.
Precision farming systems should require robots with advanced sensing and perception capabilities and drones with intelligent computer vision devices to provide a higher level of detail and on-demand images.
Farming Systems should have machine-to-machine interoperable communication (sensors, advanced farming machines and robotic collaborative systems) for cost-effectiveness.
Food safety:
Plants and Animals control; AI should allow to monitor, quantify, and understand individual plants and animals and their variability to control the bio-physical processes (like growing conditions) and understand the biological environment (with plants and animals) to ensure food safety.
Plant precision breeding and plant phenotyping should apply large-scale and high-precision measurements of plant growth, architecture, and composition to optimize plant breeding.
Integrated pest management should provide smart systems based on portable real-time pest disease diagnostics and monitoring platforms to provide rapid local and regional disease incidence alerts. They should include insect traps.
Livestock welfare and health should require smart sensor systems to monitor animal activity to provide useful information for the early detection of diseases and to increase animal wellbeing. They should be also needed for rapid verification of bacterial infection and behavioral observations to control disease spread.
Intelligent logistic systems for food chains should require sensing and monitoring of food quality during transport and storage. They should be efficient and interoperable among the logistics chain.
End-to-end food traceability should integrate blockchain into current technology to prevent fraud and counterfeiting and provide direct access to end-consumers.
Environmental protection and sustainable production:
In-situ, real-time monitoring of soil nutrients and herbicides should be carried out through intelligent and miniaturized sensors with appropriate packaging. Furthermore, this type of systems should detect weeds, preserve the “good ones” and eradicate the ones that are competing with the crop in question.
Air quality monitoring (indoor, urban, and rural) should require the development and deployment of real-time intelligent multi-sensor technologies with high selectivity and embedded (re-)calibration techniques. Focus should be put in the GHG emission from animals by performing the analysis of the gathered data to support decision making for mitigation of main issues.
Smart waste management should provide smart monitoring, controlling waste treatment units in real time as well as gas emissions in landfills and anaerobic digestion monitoring. Data analytics including gamification for behavioral triggers.
Water resource management:
Smart healthy-water systems should provide secure drinking water distribution by detecting in real-time compounds and contaminants through data analysis capabilities to take the adequate measures to mitigate these issues to secure water quality and its distribution over the network. This requires online information on the status of water sources at larger scales than before. For this, healthy water systems should require connected high-integrated multi-parameter diagnostic sensors for real-time chemical analysis to ensure freshwater.
Efficient and intelligent water distribution should require novel smart metering solutions based on various technologies, including electrochemical multi-parameter sensors with high stability, anti-fouling, high accuracy capabilities, and be cost effective. Furthermore, optical sensors based on different principles integrated into miniaturized systems, at a low cost, are also required.
Biodiversity restoration for Ecosystems Resilience, Conservation and Preservation:
Biodiversity restoration for Forestry Ecosystem should provide precision forestry system with remote sensing and AI/ML monitoring capabilities to map and assess the condition of the EU forests as well as early detection and prevention of threats to the forests (wildfires, pests, diseases, etc.). Furthermore, smart systems are required for environment monitoring of forests and fields, as well as CO2 footprint monitoring. Remotely monitor wildlife behaviour and habitat changes, and provide timely warning upon illegal poaching activity, are also needed.
Ubiquitous connectivity (“everywhere and always on(line)”) drive people to rely on intelligent applications and the services they use and offer. Public and private infrastructures will increasingly be connected, monitored, and controlled via digital infrastructures (“always measuring”) and devices.
Furthermore, the trend of combining of working at the office and from home (or other remote locations), which has been triggered by the Covid-19 pandemic, will continue, and people will endeavor to combine work and private life in other ways.
Digital infrastructures with increased quality of service (QoS) and available bandwidth, will support these trends and will be ubiquitous, both in rural areas as well as in cities. These networks will be open and secure and will support intelligent control management of critical infrastructures, such as water supply, street lighting and traffic. Edge/cloud solutions will arise which will enable increased multimodal situational awareness and ubiquitous localization.
Social inclusion and collective safety and privacy will be enhanced by improved access to public services and communities (as healthcare, education, friends, family, and colleagues), supported by technological innovations in several directions, such as tele-presence, serious gaming, chatbots, virtual reality, robots, and personal and social assistants.
More and more, these solutions will be human-centered, will have cognitive abilities, apply nudging techniques, and support personal development, health, and well-being.
The European ECS community, from academia to industry, is a world leader in research, development, and innovation for the past decades. The competition of US and Asian communities is strong and requires significant European effort and investment, so that Europe remains a leader in the coming years, considering the dramatic increase in need for ECS systems due to the emergence of IoT and the corresponding embedded and cyber-physical systems.
In this Chapter, we have presented research and innovation directions for ECS in the long-term, considering the European priorities, such as the Green Deal, and the main objectives of the European ECS community. Considering the interdependence of emerging technologies, application domains and policies that drive innovation, and considering the corresponding trends of European industry in the next few years, we identified long-term challenges for technologies and applications, to provide direction for the community to meet the expected needs of the future. Clearly, the list of challenges and directions that we provide is neither complete nor restricting. Innovation is a continuous process that adapts to new technological capabilities as they progress, application needs and, even, new application domains that are not foreseen. However, the current review indicates a clear path to establishing European leadership considering current trends and constraints.