Daniel Brunner
Institut FEMO-ST, France
Neural network (NN) concepts revolutionize computing by solving challenges previously thought to be reserved to the abstract intelligence of humans. However, the astonishing and substantial conceptual breakthroughs are so far not mirrored by advances in integrated hardware specialized in physically implementing NNs. As always with computing, scalability is the key metric. Integrated photonic architectures have the potential to revolutionize energy consumption and speed. However, conventional 2D lithography strongly limits the size of integrated NNs due to fundamental scaling laws. We want to overcome this problem by using 3D printed photonic integration, where photonic waveguides realizing a NN’s connections.
Bio:
Daniel Brunner is a CNRS researcher with the FEMTO-ST, France. His interests include novel computing using quantum or nonlinear substrates with a focuses on photonic neural networks. He has received several University and the IOP’s 2010 Roys prize, the IOP Journal Of Physics: Photonics emerging leader 2021 prize as well as the CNRS Bronze medal in 2022. He edited one Book and two special issues, has presented his results 50+ times upon invitation, has published 70+ scientific articles, has been awarded a prestigious ERC Consolidator grant and is a pilot of the BEP-PEPR Electronique project of the France 2030 initiative.
Fabio Cicoira
Polytechnique Montréal, Canada
Materials able to regenerate after damage have attracted a great deal of attention since the ancient times. For instance, self-healing concretes, able to resist earthquakes, aging, weather, and seawater are known since the times of ancient Rome and are still the object of research.
While several mechanically healable materials have been reported, self-healing conductors are still relatively rare, and are attracting enormous interest for applications in electronic skin, wearable and stretchable sensors, actuators, transistors, energy harvesting, and storage devices, such as batteries and supercapacitors.1 Self-healable and recyclable conducting materials have the potential to reduce electronic waste by enabling the repair and reuse of electronic components, which can extend the lifespan of electronic devices. Furthermore, they can be used for wearable electronic and biomedical devices, which are often subject to mechanical stress causing damage to their components.
Conducting polymers exhibit attractive properties that makes them ideal materials for bioelectronics and stretchable electronics, such as mixed ionic-electronic conductivity, leading to low interfacial impedance, tunability by chemical synthesis, ease of process via solution process and printing, and biomechanical compatibility with living tissues. However, they show typically poor mechanical properties and are therefore not suitable as self-healing materials.
In our group, we produced several self-healing and stretchable conductors by mixing aqueous suspensions of the conducting polymer poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) with other materials providing the mechanical characteristics leading to self-healing, like for instance polyvinyl alcohol (PVA), polyethylene glycol, polyurethanes and tannic acid. 2-10 In this talk, various types of self-healing will be presented and correlated with the electrical and mechanical properties of the materials. The use of the self-healing gels and films as epidermal electrodes and other devices will be also discussed.
Conductive materials obtained from blends of polyurethane-PEDOT:PSS and PEG showcase exceptional stretchability, toughness, and self-healing properties. Moreover, these materials can be recycled several times and maintain their mechanical and electrical properties.
- Li, X. Zhou, B. Sarkar, F. Cicoira et al., Adv. Mater. 2108932, 2022.
- Li, X. Li, S. Zhang, F. Cicoira et al., Adv. Funct. Mater. 30, 2002853, 2020.
- Li, X. Li, R. N., S. Zhang, F. Cicoira, et al. Flexible and Printed Electronics 4, 044004, 2019.
- Zhang, Y. Li, F. Cicoira et al. Adv. Electron. Mater1900191, 2019.
- Zhang, F. Cicoira, Adv. Mater. 29, 1703098, 2017.
- Zhou, G. A. Lodygensky, F. Cicoira et al., Acta Biomaterialia139, 296-306, 2022.
- Kateb et al., Flexible and Printed Electronics, 8 (4), 045006, 2024.
- Zhou, F. Cicoira et al., J. Mater. Chem. C, 12, 5708, 2024.
- J. Kim., F. Cicoira et al. Mater. Horiz. 11, 348, 2024.
Harald Gießen
Stuttgart University, Germany
We utilize femtosecond 3D printing to generate complex 3D microoptics, consisting of aspherical systems which include also doublet or multiplett lenses.
We demonstrate achromatic systems using hybrid refractive/diffractive optics as well as achromats using different materials with varying refractive index and dispersion.
Wavefront measurements of the 3D printed systems indicate aberrations over the entire image field of less than lambda/10.
These systems can be printed directly onto single mode or multicore optical fibers, to realize ultrasmall endoscopes and side-looking OCT systems for blood vessels. We have demonstrated imaging of plaques in the coronary arteries of living pigs. When printed onto single quantum emitters or single photon detectors, they allow for efficient in- and out-coupling to single mode fibers for quantum technologies.
Jürgen Klingauf
University of Münster, Germany
Mario Lanza
National University of Singapore
Abstract
Two-dimensional (2D) materials have outstanding physical, chemical and thermal properties that make them attractive for the fabrication of solid-state micro/nano-electronic devices and circuits. However, synthesizing high-quality 2D materials at the wafer scale is difficult, and integrating them in silicon microchips brings associated multiple challenges. Nevertheless, in the past few years substantial progress has been achieved and leading companies like TSMC, Intel and Samsung have started to work in this direction too. In this talk I will discuss how to integrate 2D materials in micro/nano-electronic devices, circuits, and microchips, giving a general overview of the global progress achieved in the field and presenting our last developments in hybrid 2D/CMOS applications. I will put special emphasis on devices and circuits for memristive technologies, including data storage, computation, encryption, and communication. I will also discuss the main technological challenges to face in the next years and provide some recommendations on how to solve them.
Biography 100 words for IMEC
Mario Lanza is an Associate Professor of Materials Science and Engineering at the National University of Singapore. His research focuses on improving electronic devices, integrated circuits, and silicon microchips using novel nanomaterials. He has published over 200 research articles in top journals including Nature, Science and Nature Electronics, and he has been plenary, keynote, tutorial and invited speaker in over 150 conferences. For this work, he and his students have received some of the most prestigious awards in the world (such as the IEEE Fellow) and his articles have been highly cited. He is often consulted by leading semiconductor companies and top publishers. He is an active member of the board of governors of the IEEE – Electron Devices Society, and has been involved in the technical and management committee of top conferences in the field of micro/nano electronics, including IEDM and IRPS.
Simone Mayer
Karlsruhe Institute of Technology (KIT), Germany
Carsten Rockstuhl
Karlsruhe Institute of Technology (KIT), Germany
Nowadays, we can define the spatial distribution of materials over many centimeters with a typical voxel size well below one micrometer thanks to 3D nanoprinting technology. The materials in reach with such a technology offer unprecedented degrees of freedom to control light propagation. In this contribution, we outline various approaches to provide digital blueprints for structured photonic materials that are feasible for fabrication with additive manufacturing techniques and tailored to meet demands in various applications. The structures we design comprise complex free-form surfaces and fully structured photonic materials in 3D. Multiple approaches for solving the inverse problem are exploited in this endeavor, e.g., the adjoint method, automatic differentiation, and machine learning-based methods.
Francesca Santoro
RWTH Aachen, Germany
Marc Verschuuren
SCIL Nanoimprint Solutions, The Netherlands
Rob Voorkamp
SCIL Nanoimprint Solutions, The Netherlands
