CRC 1459 C02 - Opto-electronic neuromorphic architectures

Basic data for this 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.