Work

Recent Advances in on-Chip Learning with Organic Neuromorphic Circuits

Year: 2025

Type: conference-paper

Abstract: Artificial neural networks power AI applications but require substantial computing resources. Neuromorphic engineering, inspired by the brain, uses organic materials that emulate neuroplasticity, offering low-energy operation and adaptability. Organic electrochemical transistors can be used as neuromorphic devices (ECRAM) to enable efficient edge computing with local, adaptive learning capabilities. We have developed compact neuromorphic circuits that learn through reinforcement and supervised learning, advancing low-power, on-chip training suitable for autonomous systems.

Source: IEEE Electron Devices Technology & Manufacturing Conference (EDTM)

DOI: 10.1109/edtm61175.2025.11041659

Author: Yoeri Van De Burgt

Institution: Eindhoven University of Technology

Language: English

Topic: Advanced Memory and Neural Computing

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: European Commission, HORIZON EUROPE Framework Programme, HORIZON EUROPE European Research Council

Award: Neuromorphic Learning in Organic Adaptive Biohybrid Systems