Photonic Synapses for Image Recognition and High Density Integration of Simplified Artificial Neural Networks

Abstract

Abstract With the rapid development of artificial intelligence (AI), there is an urgent need for developing a biological sensory perception system that can simulate the human brain for information processing. Inspired by the biological vision system, photo‐responsive photonic synapses are ideal devices for constructing photosensitive artificial neural networks for neuromorphic computing tasks. This paper reports a stable photonic synaptic device in an array layout with adjustable synaptic plasticity under ultraviolet light pulses. Since the heterojunction has a photoconductivity effect and the trap layer provides superior charge carrier trapping capability, optical sensing, memory, and neuromorphic computing are integrated into a single device. Meanwhile, supervised learning of handwritten digitals is achieved by exploiting the multistate conductance by photoelectric co‐modulation and the specific decay law. The recognition rate reaches 90.6% and hardly changes with time. Additionally, the device can simplify the artificial neural network (ANN) and reduce its size to 3.78% of the original network while retaining strong fault tolerance and learning ability. The photonic artificial synapses based on ultraviolet light modulation provide a novel and effective approach for photosensory ANNs to perform in situ computation.

Keywords

• fault tolerance
• image recognition
• photonic synapses
• simplified artificial neural networks
• synaptic plasticity