Google Search
Prof. Dr. Kathy Lüdge
Head of Group
Sekretary´s office
Dagmar Böhme
Tel.: +49 (0) 3677 69 3706
Postanschrift:
Technische Universität Ilmenau
Fakultät für Mathematik und Naturwissenschaften
Institut für Physik
We study dynamical instabilities arising from optical feedback, optical injection or mode coupling in lasers. The carrier dynamics in the gain material (e.g., semiconductor nanostructures) plays a central role. Another research topic concerns the extent to which these optical systems can be used for hardware-based machine learning. We use numerical methods to solve coupled differential equations as well as analytical methods of nonlinear dynamics for bifurcation analysis.
Brain-inspired or neuromorphic chips working with biologically inspired spiking neural networks have gained attention as they promise highly efficient ways to process data. Developing neuromorphic systems with electronic and photonic hardware is part of the new European collaborative project SPIKEPro (Spiking Photonic-Electronic IC for Quick and Efficient Processing) within the European Innovation Council (EIC) framework. TU Ilmenau is one of the partners that are gathered from TU Eindhoven, University of Strathclyde, University College London, and HP Enterprise Belgium. SPIKEPro proposes a science-towards-technology breakthrough by combining low-energy electrical and photonic neurons into a joint spiking neural network on an integrated circuit. SPIKEPro's chip integration approach is based on a common technology platform, connecting ultrafast laser optical neurons with efficient electrical spiking diodes through non-volatile synaptic weights. This enables to simultaneously capitalize on the advantages of both electronics and photonics to deliver efficient and high-speed SNNs going beyond existing implementations. In addition to reducing the energy consumption per spike in the network, SPIKEPro will also develop novel learning strategies and algorithms able to work with reduced number of synaptic connections. The outcome of SPIKEPro will have lasting economic, societal and scientific impact. The project will bring ultra-fast and efficient neuromorphic hardware into the disparate fields of edge computing, sensor data processing, high-speed control and computational neuroscience.
Reservoir computing is a machine learning method that can be easily implemented in hardware and is being intensively investigated in the research group. In the project NeurosensEar Neuromorphic Acoustic Sensor Technology for High-Performance Hearing Aids of Tomorrow, funded by the Carl Zeiss Foundation, we are investigating micro-mechanical resonators as InSensor Reservoir Computers.
DFG Projekt (2020-2023) "Hybrid photonic computing in delay-coupled non-linear systems with memory"
Teilprojekt im Sonderforschungsbereich SFB910 (2019-2022) "Collective phenomena in laser networks with nonidentical units"