Project acronym 3DALIGN
Project Enhancing the performance of 3D-printed organic thermoelectrics by electric field-assisted molecular alignment
Researcher (PI) Francisco Molina-Lopez
Host Institution (HI) KATHOLIEKE UNIVERSITEIT LEUVEN
Country Belgium
Call Details Starting Grant (StG), PE7, ERC-2020-STG
Summary Thermoelectrics (TEs) are important because they can convert heat directly into electrical energy and enable efficient heating/cooling. However, their popularization has been hindered by 1) their low efficiency (especially at room temperature), 2) the use of rare/toxic materials, and 3) the difficulty to process those materials. In 3DALIGN, I target a 3-in-1 solution to these challenges by using for the first time electric-field-assisted molecular alignment of 3D-printed TE polymers. High electrical/low thermal conductivity is required for efficient TEs, but both conductivities go hand in hand in traditional inorganic TE materials. This paradigm can shift for polymers, which possess complicated molecular structure. Despite their relatively low electrical conductivity, conducting polymers are appealing for TEs due to their much lower thermal conductivity than inorganic TE materials. Existing studies of organic TEs have focused on finding new materials, but no attention has been paid to molecular ordering, a known strategy to improve performance in organic transistors. I have recently developed a versatile method to induce molecular alignment in solution-processed polymers by using externally applied electric fields. In 3DALIGN, I propose to use this new method to boost the electrical conductivity of polymer TEs while inducing minimal alteration in their thermal conductivity. The high-risk of this goal is mitigated by other advantages of using polymer TEs: polymers are less toxic and more abundant than inorganic TE materials; and they are easy to 3D print, enabling a simple fabrication route for large-area through-plane TE structures that will lead to novel applications. In conclusion, this project will shed light in the relationship between molecular ordering and transport properties of organic electronic materials. If successful, it will also introduce a breakthrough in the performance and feasibility of TEs.
Summary
Thermoelectrics (TEs) are important because they can convert heat directly into electrical energy and enable efficient heating/cooling. However, their popularization has been hindered by 1) their low efficiency (especially at room temperature), 2) the use of rare/toxic materials, and 3) the difficulty to process those materials. In 3DALIGN, I target a 3-in-1 solution to these challenges by using for the first time electric-field-assisted molecular alignment of 3D-printed TE polymers. High electrical/low thermal conductivity is required for efficient TEs, but both conductivities go hand in hand in traditional inorganic TE materials. This paradigm can shift for polymers, which possess complicated molecular structure. Despite their relatively low electrical conductivity, conducting polymers are appealing for TEs due to their much lower thermal conductivity than inorganic TE materials. Existing studies of organic TEs have focused on finding new materials, but no attention has been paid to molecular ordering, a known strategy to improve performance in organic transistors. I have recently developed a versatile method to induce molecular alignment in solution-processed polymers by using externally applied electric fields. In 3DALIGN, I propose to use this new method to boost the electrical conductivity of polymer TEs while inducing minimal alteration in their thermal conductivity. The high-risk of this goal is mitigated by other advantages of using polymer TEs: polymers are less toxic and more abundant than inorganic TE materials; and they are easy to 3D print, enabling a simple fabrication route for large-area through-plane TE structures that will lead to novel applications. In conclusion, this project will shed light in the relationship between molecular ordering and transport properties of organic electronic materials. If successful, it will also introduce a breakthrough in the performance and feasibility of TEs.
Max ERC Funding
1 710 853 €
Duration
Start date: 2021-02-01, End date: 2026-01-31
Project acronym BEATRICE
Project Beyond Massive MIMO: Living at the Interface of Electromagnetics and Information Theory
Researcher (PI) Michail MATTHAIOU
Host Institution (HI) THE QUEEN'S UNIVERSITY OF BELFAST
Country United Kingdom
Call Details Consolidator Grant (CoG), PE7, ERC-2020-COG
Summary Massive multiple-input multiple-output (MaMi) is now a core technology for 5G networks. With MaMi, we refer to systems with an unconventionally large number (e.g. hundreds or even thousands) of base station antennas simultaneously serving tens (or even hundreds) of users. To date, the development of MaMi has been exclusively based on information theory (IT) tailored towards cellular communications. While IT is undoubtedly a versatile mathematical tool, it is based on mathematical logic. This theoretical framework now needs to be extended and reshaped to: (i) account for the unique electromagnetic (EM) properties and (ii) incorporate the main feature of future MaMi-based communication systems, namely their capability of sensing the system’s response to the radio waves, and thereby informing its modification. Looking ahead, MaMi will have far more general applications: optical communications, radar, and wireless power transfer to name a few. The grand question that the proposed research will address is: Are the existing IT tools sufficient to understand the physical phenomena and develop the upcoming generation of MaMi-based systems in ten years from now? BEATRICE will address this fundamental question by unifying EM theory and IT and pave the way for an extended range of applications supported by massive antenna arrays after 2025.
The specific project objectives are to:
O1) Redefine the information theoretic modelling of concurrent and future MaMi-based systems using knowledge of unique EM characteristics, thereby quantifying their realisable potential.
O2) Develop new topological designs and modulation techniques for robust communication by harnessing knowledge about the EM properties of the transceivers and the propagation medium.
O3) Leverage the world-class T&M facilities at QUB, to design, fabricate and measure novel array topologies which will be able to support a plethora of MaMi-based applications.
Summary
Massive multiple-input multiple-output (MaMi) is now a core technology for 5G networks. With MaMi, we refer to systems with an unconventionally large number (e.g. hundreds or even thousands) of base station antennas simultaneously serving tens (or even hundreds) of users. To date, the development of MaMi has been exclusively based on information theory (IT) tailored towards cellular communications. While IT is undoubtedly a versatile mathematical tool, it is based on mathematical logic. This theoretical framework now needs to be extended and reshaped to: (i) account for the unique electromagnetic (EM) properties and (ii) incorporate the main feature of future MaMi-based communication systems, namely their capability of sensing the system’s response to the radio waves, and thereby informing its modification. Looking ahead, MaMi will have far more general applications: optical communications, radar, and wireless power transfer to name a few. The grand question that the proposed research will address is: Are the existing IT tools sufficient to understand the physical phenomena and develop the upcoming generation of MaMi-based systems in ten years from now? BEATRICE will address this fundamental question by unifying EM theory and IT and pave the way for an extended range of applications supported by massive antenna arrays after 2025.
The specific project objectives are to:
O1) Redefine the information theoretic modelling of concurrent and future MaMi-based systems using knowledge of unique EM characteristics, thereby quantifying their realisable potential.
O2) Develop new topological designs and modulation techniques for robust communication by harnessing knowledge about the EM properties of the transceivers and the propagation medium.
O3) Leverage the world-class T&M facilities at QUB, to design, fabricate and measure novel array topologies which will be able to support a plethora of MaMi-based applications.
Max ERC Funding
1 997 797 €
Duration
Start date: 2021-06-01, End date: 2026-05-31
Project acronym CELLOIDS
Project Cell-inspired particle-based intelligent microrobots
Researcher (PI) Stefano Palagi
Host Institution (HI) SCUOLA SUPERIORE DI STUDI UNIVERSITARI E DI PERFEZIONAMENTO S ANNA
Country Italy
Call Details Starting Grant (StG), PE7, ERC-2020-STG
Summary Microscale robotic devices, or microrobots, could someday enable revolutionary non-invasive medical procedures. However, fundamental limitations still hinder the realisation of this vision. Current microrobots have very limited functionalities: they strongly rely on wireless operation by external fields, which impedes the execution of sophisticated movements and tasks. As a consequence, despite their intended medical use, microrobots cannot move effectively in bodily fluids and tissues. This project addresses exactly this challenge: realising self-contained microrobots that autonomously move in complex 3D biological environments (such as soft body tissues).
Our sources of inspiration are biological cells that naturally move through body tissues, such as immune cells. These cells move by continuously changing their shape, a strategy known as ‘amoeboid movement’. Such shape changes are powered by the self-organized flows and stresses of their intracellular filaments and motor proteins. Analogously, we will realise microrobots that each consist of a swarm of active particles: each microrobot will have a liquid body containing self-propelled particles and different sensitive particles; moreover, the particles swarm will be engineered to exhibit desired collective behaviours. These cell-inspired particle-based microrobots, or celloids, will spontaneously adapt their morphology, generate large body-shape changes, sense environmental cues and control signals, and autonomously navigate soft tissue-like environments.
This project will establish a radically new method to design microrobots, and will result in microrobots capable of autonomous navigation of body tissues. The celloids will also constitute a robophysical model for studying the migration of immune and cancer cells, and will enable a number of revolutionary medical procedures, including long-term monitoring and non-invasive interventions in delicate organs (e.g. brain).
Summary
Microscale robotic devices, or microrobots, could someday enable revolutionary non-invasive medical procedures. However, fundamental limitations still hinder the realisation of this vision. Current microrobots have very limited functionalities: they strongly rely on wireless operation by external fields, which impedes the execution of sophisticated movements and tasks. As a consequence, despite their intended medical use, microrobots cannot move effectively in bodily fluids and tissues. This project addresses exactly this challenge: realising self-contained microrobots that autonomously move in complex 3D biological environments (such as soft body tissues).
Our sources of inspiration are biological cells that naturally move through body tissues, such as immune cells. These cells move by continuously changing their shape, a strategy known as ‘amoeboid movement’. Such shape changes are powered by the self-organized flows and stresses of their intracellular filaments and motor proteins. Analogously, we will realise microrobots that each consist of a swarm of active particles: each microrobot will have a liquid body containing self-propelled particles and different sensitive particles; moreover, the particles swarm will be engineered to exhibit desired collective behaviours. These cell-inspired particle-based microrobots, or celloids, will spontaneously adapt their morphology, generate large body-shape changes, sense environmental cues and control signals, and autonomously navigate soft tissue-like environments.
This project will establish a radically new method to design microrobots, and will result in microrobots capable of autonomous navigation of body tissues. The celloids will also constitute a robophysical model for studying the migration of immune and cancer cells, and will enable a number of revolutionary medical procedures, including long-term monitoring and non-invasive interventions in delicate organs (e.g. brain).
Max ERC Funding
1 499 375 €
Duration
Start date: 2021-02-01, End date: 2026-01-31
Project acronym CHANSON
Project Chiral semiconductor nanophotonics for ultraresolved molecular sensing
Researcher (PI) Alberto CURTO
Host Institution (HI) TECHNISCHE UNIVERSITEIT EINDHOVEN
Country Netherlands
Call Details Starting Grant (StG), PE7, ERC-2020-STG
Summary Chirality plays a pivotal role in chemistry and medicine because most biological molecules have either right- or left-handed conformations. Circular dichroism can distinguish the chirality of matter thanks to a small difference in absorption of light with opposite circular polarizations. However, it is severely limited by low sensitivity and low spatial resolution due to weak chiral light-matter interaction. As a result, using light, we cannot resolve the chirality of individual nanoscale objects for critical applications such as detecting protein aggregates responsible for a variety of diseases.
CHANSON pushes the limits of optically resolvable chirality through new concepts in semiconductor nanophotonics. We tailor semiconductor nanostructures to specifically boost chiral fluorescence thanks to the interplay of photons, charges, and spins. Using novel contrast mechanisms, we increase both fluorescence intensity and polarization to remove the barriers that hinder circular dichroism. The project combines two routes for ultrasensitive and super-resolved molecular detection: 1) Nanophotonic sensors based on semiconductor nanoantennas; 2) Excitonic sensors based on atomically thin semiconductors.
The ambitious target is to map with nanoscale spatial resolution the lowest possible molecular concentrations down to a single chiral molecule. To tackle this major scientific challenge, I propose the concept of a metasurface canvas consisting of arrays of semiconductor nanostructures. By providing a platform for fluorescence-based sensing of both light-emitting and non-emitting analytes, the results could revolutionize the screening of pharmaceuticals for neurodegenerative diseases, amongst others.
Summary
Chirality plays a pivotal role in chemistry and medicine because most biological molecules have either right- or left-handed conformations. Circular dichroism can distinguish the chirality of matter thanks to a small difference in absorption of light with opposite circular polarizations. However, it is severely limited by low sensitivity and low spatial resolution due to weak chiral light-matter interaction. As a result, using light, we cannot resolve the chirality of individual nanoscale objects for critical applications such as detecting protein aggregates responsible for a variety of diseases.
CHANSON pushes the limits of optically resolvable chirality through new concepts in semiconductor nanophotonics. We tailor semiconductor nanostructures to specifically boost chiral fluorescence thanks to the interplay of photons, charges, and spins. Using novel contrast mechanisms, we increase both fluorescence intensity and polarization to remove the barriers that hinder circular dichroism. The project combines two routes for ultrasensitive and super-resolved molecular detection: 1) Nanophotonic sensors based on semiconductor nanoantennas; 2) Excitonic sensors based on atomically thin semiconductors.
The ambitious target is to map with nanoscale spatial resolution the lowest possible molecular concentrations down to a single chiral molecule. To tackle this major scientific challenge, I propose the concept of a metasurface canvas consisting of arrays of semiconductor nanostructures. By providing a platform for fluorescence-based sensing of both light-emitting and non-emitting analytes, the results could revolutionize the screening of pharmaceuticals for neurodegenerative diseases, amongst others.
Max ERC Funding
1 499 858 €
Duration
Start date: 2021-02-01, End date: 2026-01-31
Project acronym COLOR-UP
Project All-optical sub-THz signal filtering with multi-COLOR lasers
Researcher (PI) Martin Marc Henry VIRTE
Host Institution (HI) VRIJE UNIVERSITEIT BRUSSEL
Country Belgium
Call Details Starting Grant (StG), PE7, ERC-2020-STG
Summary MicroWave Photonics (MWP) has been delivering on-chip devices with outstanding performances to answer the demand of Information and Communication Technologies for always faster, more efficient and more compact systems. Yet, some stringent limitations form a roadblock for disruptive specifications: for instance, on-chip MWP frequency filters hardly perform beyond 60 GHz, whereas the technology and applications require frequencies in the sub-THz range from 100 GHz to several THz. This frequency band will directly support future ultra-fast telecom systems, but also sensing techniques such as THz spectroscopy e.g. for food contaminant detection or mm-precision RADARs for robotic systems.
With COLOR'UP, my goal is to remove this frequency roadblock by exploring and implementing on-chip a radically new concept exploiting the nonlinear dynamics of multi-colour lasers. These lasers naturally generate a set of sharp beat-notes in the sub-THz range corresponding to the frequency separation between the different wavelengths. Injecting an optical beam in a multi-colour laser with a modulation at well-chosen frequencies can lead to injection-locking of all wavelengths simultaneously. Spectral components that are not matching the beat-notes will however not be picked up and will be filtered out in the laser output.
In this project, I will demonstrate that this effect can be exploited to create all-optical on-chip MWP bandpass filters with the capability to cover the entire sub-THz range from tens of GHz, up to a few THz. My goals are four-fold: (1) design and realize multi-colour lasers with tailored spectra to achieve filtering at precise frequencies (2) study the underlying filtering mechanism to optimize the filter performances (3) develop on-chip control techniques based on optical feedback to control the filter properties (4) make a Proof-of-Concept demonstration of the filter on an InP photonic integrated circuit emitting in the telecom band, around the 1.55 um wavelength
Summary
MicroWave Photonics (MWP) has been delivering on-chip devices with outstanding performances to answer the demand of Information and Communication Technologies for always faster, more efficient and more compact systems. Yet, some stringent limitations form a roadblock for disruptive specifications: for instance, on-chip MWP frequency filters hardly perform beyond 60 GHz, whereas the technology and applications require frequencies in the sub-THz range from 100 GHz to several THz. This frequency band will directly support future ultra-fast telecom systems, but also sensing techniques such as THz spectroscopy e.g. for food contaminant detection or mm-precision RADARs for robotic systems.
With COLOR'UP, my goal is to remove this frequency roadblock by exploring and implementing on-chip a radically new concept exploiting the nonlinear dynamics of multi-colour lasers. These lasers naturally generate a set of sharp beat-notes in the sub-THz range corresponding to the frequency separation between the different wavelengths. Injecting an optical beam in a multi-colour laser with a modulation at well-chosen frequencies can lead to injection-locking of all wavelengths simultaneously. Spectral components that are not matching the beat-notes will however not be picked up and will be filtered out in the laser output.
In this project, I will demonstrate that this effect can be exploited to create all-optical on-chip MWP bandpass filters with the capability to cover the entire sub-THz range from tens of GHz, up to a few THz. My goals are four-fold: (1) design and realize multi-colour lasers with tailored spectra to achieve filtering at precise frequencies (2) study the underlying filtering mechanism to optimize the filter performances (3) develop on-chip control techniques based on optical feedback to control the filter properties (4) make a Proof-of-Concept demonstration of the filter on an InP photonic integrated circuit emitting in the telecom band, around the 1.55 um wavelength
Max ERC Funding
1 499 371 €
Duration
Start date: 2020-11-01, End date: 2025-10-31
Project acronym Cont4Med
Project Estimation and control under limited information with application to biomedical systems
Researcher (PI) Matthias A. Mueller
Host Institution (HI) GOTTFRIED WILHELM LEIBNIZ UNIVERSITAET HANNOVER
Country Germany
Call Details Starting Grant (StG), PE7, ERC-2020-STG
Summary The goal of this project is to develop estimation and control strategies for systems where only a (very) limited amount of information (measurements and models) is available. The main motivation to consider these problems are biomedical applications, where such a small amount of available information is often inherent. Examples include hormone concentration measurements when considering thyroidal diseases (which are typically only taken every few days or even weeks) or monitoring the size of a tumor. Estimating the current state of the system and devising appropriate control actions is very challenging in such applications. This is not covered by existing approaches in the literature, necessitating the development of novel methods and tools. Within this project, I will in particular focus on the following aspects. First, observability of nonlinear systems subject to few (sampled) measurements will be studied and sampling strategies together with suitable nonlinear state estimators will be derived. Second, state estimation and control strategies will be developed for situations with only partial or no model knowledge. Again, this is of intrinsic importance in biomedical applications where often the underlying physical principles are only partially understood or too complex. This necessitates the design of data- and learning-based methods, for which desired guarantees can be given, even in case of few measurements. Third, the developed tools will be extended to large-scale systems, where estimation and control has to be achieved in a distributed fashion. The successful achievement of the project goals will (i) enable estimation and control in systems with very few, sampled measurements, (ii) constitute a big step towards a holistic data-based systems and control theory, (iii) result in a new, data-driven, paradigm for the control of large-scale systems, and (iv) enable the design of systematic, personalized, and optimal control strategies in biomedical applications.
Summary
The goal of this project is to develop estimation and control strategies for systems where only a (very) limited amount of information (measurements and models) is available. The main motivation to consider these problems are biomedical applications, where such a small amount of available information is often inherent. Examples include hormone concentration measurements when considering thyroidal diseases (which are typically only taken every few days or even weeks) or monitoring the size of a tumor. Estimating the current state of the system and devising appropriate control actions is very challenging in such applications. This is not covered by existing approaches in the literature, necessitating the development of novel methods and tools. Within this project, I will in particular focus on the following aspects. First, observability of nonlinear systems subject to few (sampled) measurements will be studied and sampling strategies together with suitable nonlinear state estimators will be derived. Second, state estimation and control strategies will be developed for situations with only partial or no model knowledge. Again, this is of intrinsic importance in biomedical applications where often the underlying physical principles are only partially understood or too complex. This necessitates the design of data- and learning-based methods, for which desired guarantees can be given, even in case of few measurements. Third, the developed tools will be extended to large-scale systems, where estimation and control has to be achieved in a distributed fashion. The successful achievement of the project goals will (i) enable estimation and control in systems with very few, sampled measurements, (ii) constitute a big step towards a holistic data-based systems and control theory, (iii) result in a new, data-driven, paradigm for the control of large-scale systems, and (iv) enable the design of systematic, personalized, and optimal control strategies in biomedical applications.
Max ERC Funding
1 497 965 €
Duration
Start date: 2021-01-01, End date: 2025-12-31
Project acronym DeepEmbryo
Project Reverse-engineering the development of embryos with physics-informed machine learning
Researcher (PI) Herve TURLIER
Host Institution (HI) CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS
Country France
Call Details Starting Grant (StG), PE7, ERC-2020-STG
Summary Embryogenesis is archetypal of a self-organized process, where the emergence of a complex structure stems from the interaction of its elementary parts. Progress in imaging and molecular genetics allow us to delve into embryos at unprecedented spatiotemporal resolutions, but extracting biophysical information from this complex multidimensional data is a highly technical challenge. As a result the principles of multicellular self-organization remain far from understood. DeepEmbryo proposes to fill this gap by pioneering the use of deep learning to reverse-engineer early embryo development directly from high-resolution 3D microscopy movies. Focusing on four animal groups (mammals, ascidians, nematodes and annelids), the project will combine physical modeling and machine learning to tackle three fundamental questions from a unique transversal perspective: Q1 What are the forces shaping early embryos? Using convolutional neural networks, I will develop an automated method to directly infer cell forces from membrane-labeled images of embryos. Q2 How do cells coordinate forces, division and signaling? Regarding cells as dynamical systems, I will model them with minimal neural networks and design a multi-agent embryo model able to learn by reinforcement the fundamental feedback controls between mechanics and fate. Q3 What principles ensure developmental robustness? Using deep generative models, I will infer intra-specie developmental variability to identify robust developmental traits and mechanisms. Using dropout techniques as virtual analog to genetic knockout, I will produce experimentally testable new predictions, refining my inaugural virtual embryos. Pioneering a new field at the frontier of developmental biology, artificial intelligence and physics, DeepEmbryo will uncover the fundamental engineering principles of early embryogenesis, with far-reaching implications in multi-agent modeling, evolutionary biology, physical inference and tissue engineering.
Summary
Embryogenesis is archetypal of a self-organized process, where the emergence of a complex structure stems from the interaction of its elementary parts. Progress in imaging and molecular genetics allow us to delve into embryos at unprecedented spatiotemporal resolutions, but extracting biophysical information from this complex multidimensional data is a highly technical challenge. As a result the principles of multicellular self-organization remain far from understood. DeepEmbryo proposes to fill this gap by pioneering the use of deep learning to reverse-engineer early embryo development directly from high-resolution 3D microscopy movies. Focusing on four animal groups (mammals, ascidians, nematodes and annelids), the project will combine physical modeling and machine learning to tackle three fundamental questions from a unique transversal perspective: Q1 What are the forces shaping early embryos? Using convolutional neural networks, I will develop an automated method to directly infer cell forces from membrane-labeled images of embryos. Q2 How do cells coordinate forces, division and signaling? Regarding cells as dynamical systems, I will model them with minimal neural networks and design a multi-agent embryo model able to learn by reinforcement the fundamental feedback controls between mechanics and fate. Q3 What principles ensure developmental robustness? Using deep generative models, I will infer intra-specie developmental variability to identify robust developmental traits and mechanisms. Using dropout techniques as virtual analog to genetic knockout, I will produce experimentally testable new predictions, refining my inaugural virtual embryos. Pioneering a new field at the frontier of developmental biology, artificial intelligence and physics, DeepEmbryo will uncover the fundamental engineering principles of early embryogenesis, with far-reaching implications in multi-agent modeling, evolutionary biology, physical inference and tissue engineering.
Max ERC Funding
1 957 751 €
Duration
Start date: 2021-01-01, End date: 2025-12-31
Project acronym ELEPHANT
Project On-Chip Electronics, Photonics, Plasmonics and Antennas: A Novel Enabling Platform for sub-THz Signal Processing
Researcher (PI) Maurizio BURLA
Host Institution (HI) TECHNISCHE UNIVERSITAT BERLIN
Country Germany
Call Details Starting Grant (StG), PE7, ERC-2020-STG
Summary The ELEPHANT project aims at combining the best of four worlds by bringing together the fastest electronics, photonics, plasmonics and antennas to create a novel enabling technology for future THz signal processing.
The THz range has a tremendous untapped potential for a breadth of applications, as next-generation wireless communications, sensing, security, medical imaging, and more. However, efficient transport and processing of THz signals is a major challenge to this date, as at those frequencies electronic circuits are inherently limited by high dispersion and material losses. As a consequence, current approaches still rely on low-efficiency discrete components, which suffer from limited power, high losses and very high costs. While photonics allows low-loss transport of THz frequencies over large distances and broadband processing, today’s electronic-photonic platforms do not offer the required conversion speeds. Current efforts using organic materials have not proven sufficient stability and scalability.
I plan to solve the challenge of THz signal processing by creating a novel integrated THz platform that allows to convert THz signals to the optical domain efficiently and with high fidelity, and to process them using a low-loss photonic processing core with THz bandwidth.
The project fully builds on my cutting-edge results on photonic signal processing blocks with THz bandwidths using compact (10s µm-long) silicon photonics nanowires, and my recent demonstration of plasmonic modulators offering 500 GHz speeds, the fastest to date. I will create novel architectures suitable for analog processing and realize them in a scalable manner on bipolar CMOS platforms, together with THz antennas for wireless interfacing, and high-speed amplifiers to achieve the signal powers needed in real-world applications.
The new platform will impact all the crucial THz fields, and it will be put to the test by creating the first photonic-wireless THz beamforming transceiver.
Summary
The ELEPHANT project aims at combining the best of four worlds by bringing together the fastest electronics, photonics, plasmonics and antennas to create a novel enabling technology for future THz signal processing.
The THz range has a tremendous untapped potential for a breadth of applications, as next-generation wireless communications, sensing, security, medical imaging, and more. However, efficient transport and processing of THz signals is a major challenge to this date, as at those frequencies electronic circuits are inherently limited by high dispersion and material losses. As a consequence, current approaches still rely on low-efficiency discrete components, which suffer from limited power, high losses and very high costs. While photonics allows low-loss transport of THz frequencies over large distances and broadband processing, today’s electronic-photonic platforms do not offer the required conversion speeds. Current efforts using organic materials have not proven sufficient stability and scalability.
I plan to solve the challenge of THz signal processing by creating a novel integrated THz platform that allows to convert THz signals to the optical domain efficiently and with high fidelity, and to process them using a low-loss photonic processing core with THz bandwidth.
The project fully builds on my cutting-edge results on photonic signal processing blocks with THz bandwidths using compact (10s µm-long) silicon photonics nanowires, and my recent demonstration of plasmonic modulators offering 500 GHz speeds, the fastest to date. I will create novel architectures suitable for analog processing and realize them in a scalable manner on bipolar CMOS platforms, together with THz antennas for wireless interfacing, and high-speed amplifiers to achieve the signal powers needed in real-world applications.
The new platform will impact all the crucial THz fields, and it will be put to the test by creating the first photonic-wireless THz beamforming transceiver.
Max ERC Funding
1 894 375 €
Duration
Start date: 2021-09-01, End date: 2026-08-31
Project acronym gAIa
Project Scalable Co-optimization of Collective Robotic Mobility and the Artificial Environment
Researcher (PI) Amanda PROROK
Host Institution (HI) THE CHANCELLOR MASTERS AND SCHOLARS OF THE UNIVERSITY OF CAMBRIDGE
Country United Kingdom
Call Details Starting Grant (StG), PE7, ERC-2020-STG
Summary The behavior of intelligent systems, both living and artificial, is influenced through the structure of their surrounding environment. In nature, environmental constraints dictate the creation, unfolding, and interaction of living beings. Living systems are prototypes for collective robot behaviors— yet, despite the obvious influence of spatial constraints on interactions, the optimization of mobile robots and their immediate environment has been disjoint. Little thought has been given to what would make an artificial environment conducive to effective and efficient collective robotic mobility.
The premise of this project is that the environment is as much a variable as the robot itself. I want to expose the coupling between environmental structure and collective robotic mobility. In pursuit of this goal, I propose a co-optimization scheme that finds the best robot-environment pairs in an automated, scalable manner. The work in this project will (i) optimize control policies that define the behavior of collective mobile robot systems, and (ii) find environments that are more conducive to efficient coordination and cooperation. The developed techniques will allow us to perform first-of-a-kind analyses that would reveal novel environmental paradigms and the collective robot policies optimized around them. Ultimately, this project will spearhead new ways of thinking about transport planning and urban design, in the wake of a new generation of mobile vehicles that are connected and coordinated.
Summary
The behavior of intelligent systems, both living and artificial, is influenced through the structure of their surrounding environment. In nature, environmental constraints dictate the creation, unfolding, and interaction of living beings. Living systems are prototypes for collective robot behaviors— yet, despite the obvious influence of spatial constraints on interactions, the optimization of mobile robots and their immediate environment has been disjoint. Little thought has been given to what would make an artificial environment conducive to effective and efficient collective robotic mobility.
The premise of this project is that the environment is as much a variable as the robot itself. I want to expose the coupling between environmental structure and collective robotic mobility. In pursuit of this goal, I propose a co-optimization scheme that finds the best robot-environment pairs in an automated, scalable manner. The work in this project will (i) optimize control policies that define the behavior of collective mobile robot systems, and (ii) find environments that are more conducive to efficient coordination and cooperation. The developed techniques will allow us to perform first-of-a-kind analyses that would reveal novel environmental paradigms and the collective robot policies optimized around them. Ultimately, this project will spearhead new ways of thinking about transport planning and urban design, in the wake of a new generation of mobile vehicles that are connected and coordinated.
Max ERC Funding
1 495 338 €
Duration
Start date: 2021-01-01, End date: 2025-12-31
Project acronym I-Wood
Project Forest Intelligence: robotic networks inspired by the Wood Wide Web
Researcher (PI) Barbara Mazzolai
Host Institution (HI) FONDAZIONE ISTITUTO ITALIANO DI TECNOLOGIA
Country Italy
Call Details Consolidator Grant (CoG), PE7, ERC-2020-COG
Summary Plants are connected to each other by an underground network of fungi that provide them with nutrients, help share resources, and extend their perception abilities. This mycorrhizal network, known as the Wood Wide Web, plays a crucial role in maintaining healthy natural ecosystems, and in limiting the global warming. Thus, it must be preserved in order to mitigate the speeding up of the carbon cycle and its effects on climate change. Robotics and Artificial Intelligence (AI) can offer concrete solutions for a deeper analysis of natural processes at the basis of this global change and for developing sustainable technologies. Based on that, I-Wood proposes a new paradigm of virtual and physical robotic networks inspired by the belowground fungus-mediated inter-plant communication and by the associated collective behaviours. Specifically, I-Wood will study, extract and formalize the rules of plant-fungus interaction mechanisms to develop: a plant-inspired perceptron-like model; and a new generation of plant-inspired robots able to explore soil using their roots with growing, ageing, branching, and elongating abilities in response to their network-augmented perception and implementing plant-inspired collective behaviours. By imitating plants, these distributed intelligent systems will co-develop morphology and behaviour in a dynamic environment. Impact and feasibility of the proposed approach will be tested in a mixed social network, scale-down in a confined environment, where robots will interact with real plants to facilitate the development of mycorrhizal networks. Grounded on a strong multi-disciplinary approach, I-Wood will pave the way for new paradigms in robotics and embodied AI, based on solutions that overcome the current animal-based or brain-based model, novel approaches for the use of robotics in biology and for new scientific knowledge on plants community with a major significance for biodiversity and climate protection.
Summary
Plants are connected to each other by an underground network of fungi that provide them with nutrients, help share resources, and extend their perception abilities. This mycorrhizal network, known as the Wood Wide Web, plays a crucial role in maintaining healthy natural ecosystems, and in limiting the global warming. Thus, it must be preserved in order to mitigate the speeding up of the carbon cycle and its effects on climate change. Robotics and Artificial Intelligence (AI) can offer concrete solutions for a deeper analysis of natural processes at the basis of this global change and for developing sustainable technologies. Based on that, I-Wood proposes a new paradigm of virtual and physical robotic networks inspired by the belowground fungus-mediated inter-plant communication and by the associated collective behaviours. Specifically, I-Wood will study, extract and formalize the rules of plant-fungus interaction mechanisms to develop: a plant-inspired perceptron-like model; and a new generation of plant-inspired robots able to explore soil using their roots with growing, ageing, branching, and elongating abilities in response to their network-augmented perception and implementing plant-inspired collective behaviours. By imitating plants, these distributed intelligent systems will co-develop morphology and behaviour in a dynamic environment. Impact and feasibility of the proposed approach will be tested in a mixed social network, scale-down in a confined environment, where robots will interact with real plants to facilitate the development of mycorrhizal networks. Grounded on a strong multi-disciplinary approach, I-Wood will pave the way for new paradigms in robotics and embodied AI, based on solutions that overcome the current animal-based or brain-based model, novel approaches for the use of robotics in biology and for new scientific knowledge on plants community with a major significance for biodiversity and climate protection.
Max ERC Funding
2 000 000 €
Duration
Start date: 2021-05-01, End date: 2026-04-30
