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

    We will develop adaptive nanoscale opto-electronic networks for machine learning in materio. Memory functionality is embedded via phase-change materials (PCMs). Learning capability is obtained by combining local field enhancement through plasmonic nanoparticles (NPs) with optical and electrical feedback. NP single-electron transistors will employ PCMs as tunnel barriers that can be programmed by ultra-short optical pulses combined with feedback from electrical high-frequency signals. We will study both regular and disordered NP networks created via bottom-up self-assembly and top-down nanofabrication. Our long-term goal is to realize matter-like processors that communicate with each other, and to analyse electrical sensory input, providing intelligent response for machine-learning tasks.

    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

    C03 Self-assembly of hybrid nanostructures for neuromorphic 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 networks of functionalized metal nanoparticles can be configured into devices such as classifiers. Here we significantly enhance the functionality of these devices by introducing organic ligands that can be switched between different configurations and/or charge states. Additionally, we will also use magnetic nanoparticles. These new features will not only enhance the addressability, but will also introduce memory, allowing to tackle new (time-dependent) problems. By directly connecting chemical synthesis, physical experiment, and simulation of data-driven and physical models, the project will explore the development of intelligent matter based on Material Learning.

    Jonas Mensing Kinetic Monte Carlo Model for Computing Functionalities in Nanoparticle Networks | Heuer

    Lisa Schlichter Self-Assembly of Hybrid Nanostructures for Brain-Inspired Electronics | Ravoo

    Marc Beuel Self-Assembly of Hybrid Nanostructures for Neuromorphic Electronics | van der Wiel

  • C04

    C04 Adaptive magnonic networks for nanoscale reservoir computing

    Prof. Dr. Rudolf Bratschitsch - Physics Institute
    Prof. Dr. Sergej O. Demokritov - Institute of Applied Physics
    Prof. Dr. Wolfram H. P. Pernice - Physics Institute

    Project description

    We plan to develop nanoscale reservoir computing devices based on adaptive magnonic networks with embedded memory functionality. Adaptive networks based on both lithographically patterned and self-assembled nanostructures will be realized, which will be controlled using ultrafast optical programming. In the mid-term, we will combine the developed controllable building blocks into ordered and disordered networks with multiple input and output terminals, allowing implementation of reservoir-computing devices at the nanoscale. In the long term, we strive to employ interconnected magnonic reservoirs for realizing adaptive surfaces responding to magnetic, electric, 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

    C05 Coherent nanophotonic neural networks with adaptive molecular systems

    Jun.-Prof. Dr. Benjamin Risse - Faculty of Mathematics and Computer Science
    Jun.-Prof. Dr. Carsten Schuck - Physics Institute

    Project description

    This project targets the implementation and performance evaluation of elementary linear and nonlinear building blocks for coherent optical artificial neural networks. The focus will lie on integrating a large variety of nonlinear photo-responsive chemical and molecular systems developed within this CRC with nanophotonic devices that allow for straightforward replication. We will assess the characteristics of the resultant building blocks for dedicated training, regularization and explainable AI strategies to derive tailored analysis and optimization algorithms. This interdisciplinary combination will yield nanophotonic neural network components and accompanying digital twins that pave the way for large-scale artificial intelligence.

    Marlon Becker Explainable Deep Learning and Nanophotonic Neural Networks | Risse


  • 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 Physics

    Project description

    We develop neuromorphic architectures that exploit phase-change materials to implement in-memory computing. Nanophotonic waveguides will allow for realizing high-bandwidth neuromorphic processors with both optical and electrical feedback. Nanoscale artificial synapses will be based on phase-change materials, which are electrically programmed into multiple memory states for weighted optical readout. Using material engineering and spatially resolved phase-state assignment, we will create interconnected logic arrays for mixed-mode 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