Opto-electronic neuromorphic architectures (C02)

• Subject Area: Experimental Condensed Matter Physics ; Optics, Quantum Optics and Physics of Atoms, Molecules and Plasmas
• Term: since 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 433682494

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.

• DFG Programme: Collaborative Research Centres
• Subproject of: SFB 1459: Intelligent matter: From responsive to adaptive nanosystems
• International Connection: Netherlands
• Applicant Institution: Universität Münster
• Project Heads: Professor Dr. Rudolf Bratschitsch; Professor Dr. Wolfram Hans Peter Pernice; Professor Dr. Wilfred van der Wiel