Reseach area C: Adaptive solid-state nanosystems
C02
C02 Opto-electronic neuromorphic architectures
Prof. Dr. Rudolf Bratschitsch - Physics Institute
Prof. Dr. Wolfram H. P. Pernice - Physics Institute
Prof. Dr. Wilfred G. van der Wiel - Physics Institute and MESA+ (University of Twente)Project description
In this project, we will develop adaptive opto-electronic networks using linear photonic crossbar arrays combined with non-linear dopant network processing units (DNPUs) for machine learning in materia. The DNPUs will provide multi-terminal non-linear activations which are individually trainable and thus enable novel material-based learning algorithms to be implemented in hardware. We will further operate our envisaged architecture in reverse mode using DNPUs as nonlinear input modules to photonic crossbar arrays. Based on such hybrid opto-electronic networks, we will create nanoscale matter systems with optical and electrical feedback, enabling learning capability.
Ivonne Bente Opto-electronic neuromorphic architectures | Pernice
Lorenzo Cassola Silicon-Based Neuromorphic Computation in Materio | van der Wiel
Reinier Cool Incorporating Memory into Unconventional Computing Devices| van der Wiel
C03
**PhD Position available**
C03 Self-assembly of hybrid nanostructures for brain-inspired electronics
Prof. Dr. Andreas Heuer - Institute of Physical Chemistry
Prof. Dr. Bart Jan Ravoo - Organic Chemistry Institute
Prof. Dr. Wilfred G. van der Wiel - Physics Institute and MESA+ (University of Twente)Project description
Disordered nanomaterial networks exhibit complex energy landscapes that can be tuned towards reconfigurable computational functionality. Here, we focus on realizing local memory in nanoparticle networks using dynamic molecular junctions based on molecular switches. The electronic properties of these networks will be investigated experimentally and in simulations. We aim to achieve short-term memory, adaptive and learning properties of the network in response to optical and electrical stimuli. Additionally, long-term memory will be introduced by integration of magnetic nanoparticles in the network.
Jonas Mensing Kinetic Monte Carlo Model for Computing Functionalities in Nanoparticle Networks | Heuer
Dominik Mählmann Photo-Responsive Nanoparticles for Brain-Inspired Electronics | Ravoo
Marc Beuel Self-Assembly of Hybrid Nanostructures for Neuromorphic Electronics | van der Wiel
C04
C04 Adaptive magnonic networks for advanced nanoscale computing
Prof. Dr. Rudolf Bratschitsch - Physics Institute
Prof. Dr. Sergej O. Demokritov - Institute of Applied Physics
Prof. Dr. Wolfram H. P. Pernice - Physics InstituteProject description
The objective of this project is to develop interconnected, easy-to-train intelligent magnonic networks with embedded memory for nanoscale signal processing and computing, as well as functional elements of intelligent systems allowing brain-inspired computing. We have gained extensive knowledge about the propagation of spin waves in nanoscale magnetic waveguides, demonstrated new approaches for the implementation of memory, and developed efficient methods for the fabrication of nanoscale magnonic devices and networks based on ion implantation. In the second funding period, we will implement complex nanomagnonic networks that demonstrate intelligent responses to electrical, magnetic, and optical stimuli.
Jannis Bensmann Ultrafast Dynamics in Magnetic Nanosystems | Bratschitsch
Kirill Nikolaev Dynamics of magnon Bose-Einstein condensate | Demokritov
Dmitrii Raskhodchikov Spin Wave Systems for Reservoir Computing | Pernice
C05
**PhD Position available**
C05 Coherent nanophotonic neural networks with adaptive molecular systems
Prof. Dr. Benjamin Risse - Faculty of Mathematics and Computer Science
Prof. Dr. Carsten Schuck - Physics InstituteProject description
The goal of this project is to develop and evaluate fundamental building blocks for optical artificial neural networks based on coherent nanophotonic circuits. The implementation of effective nonlinear photo-responsive systems has emerged as a primary challenge for novel realizations of optical neural networks for deep-learning applications. By comparing different systems, considering both optical-computing and computer-science aspects, a variety of requirements for nanophotonic neural networks will be determined. Crucial challenges in achieving light-matter interaction at the microscale that is strong enough to yield the nonlinear optical behaviour required for activation functions in optical neural networks will be addressed.
Marlon Becker Explainable Deep Learning and Nanophotonic Neural Networks | Risse
Peter Lazarowicz Molecular Photoswitches in Nonlinear Nanophotonics for use in Optical Neural Networks | Schuck
C06
C06 Mixed-mode in-memory computing using adaptive phase-change materials
Prof. Dr. Wolfram H. P. Pernice - Physics Institute
Prof. Dr. Martin Salinga - Institute of Materials PhysicsProject description
In this project, we employ mixed-mode neuromorphic architectures to implement in-memory learning through electrically programmable artificial neurons and synapses based on phase-change materials. Optimal compositions will be devised, implemented, and tested using advanced fabrication methods and nanoscale analytics. We will develop and implement mixed-precision algorithms for in-memory learning and will apply our architecture for processing of external sensory input. In the long term, we strive to develop brain-inspired nanoscale computing devices, which are able to carry out adaptive optical information processing.
Zhongyu Tang Development of Lossless Photonic Memories with Novel Materials | Pernice
Akhil Varri Neuromorphic photonic computing | Pernice
Daniel Wendland Neuromorphic Photonic Computing | Pernice
Nishant Saxena Phase Change Materials for Programmable Electronic-Photonic Systems | Salinga
Niklas Vollmar Electro-Optical in-Memory Computing With Phase Change Materials | Salinga
C07
** PhD positions available **
C07 Dynamic redox switching in molecular junctions: Intelligent interfaces for neuromorphic computing - starting in 2025
Prof. Dr. Frank Glorius - Organic Chemistry Institute
Dr. Robert Hein - Organic Chemistry Institute
Prof. Dr. Christian Nijhuis - MESA+ Institute (University of Twente) and Center for Soft NanoscienceProject description
In this project, we aim to implement neuromorphic architectures at the molecular level by self-assembly of redox-active switches in molecular conductance junctions. Coupling of multiple processes with different kinetics can induce feedback pathways and dynamic switching, leading to hysteretic negative differential resistance, synaptic behaviour, and Pavlovian learning. Several challenges are addressed in this project, including scalable and robust formation of molecular conductance junctions, chemical diversification and tuning of dynamic switching properties, exploration of more diverse feedback mechanisms and different learning/adaptation paradigms, and development of rational design principles and theoretical frameworks.
C08
** PhD positions available **
C08 Intelligent colour centre networks - starting in 2025
Jun.- Prof. Dr. Iris Niehues - Organic Chemistry Institute
Prof. Dr. Christian Nijhuis - MESA+ Institute (University of Twente) and Center for Soft NanoscienceProject description
In this project we will integrate colour centres with plasmonic junctions to develop electrically driven nanoscale light sources. Combined with the responsiveness to external stimuli of colour centres, we aim to develop networks of adaptive opto-electronic devices showing forms of intelligent behaviour, such as synapse-like learning by introducing feedback loops between emitters leading to time-dependent emission behaviour. To comprehend the manipulation and utilization of emitters for intelligent material applications, we will employ optical far- and near-field techniques. Our perspective goal is to demonstrate coupling of emitters into small networks that show the possibility of reservoir computing, or temporal computing schemes that mimic synapses.