A Gate-Tunable Thermal Persistent Photocurrent Device for In-Sensor Spiking Neural Networks

ACS Applied Materials & Interfaces 2026

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Authors: Xiao Li, Liutianyi Zhang, Yifei Zhang, Fanxin Liu, Lin Wang, Pingheng Tan, Jiangbin Wu
DOI: 10.1021/acsami.5c25164
Cited by: 1
References: 52

Abstract

) in a leaky integrate-and-fire model, we further demonstrate an in-sensor spiking neural network with 91.95% accuracy on MNIST, highlighting the potential of thermally engineered persistent-photocurrent devices for neuromorphic vision.

Fields of Study & Subjects

Advanced Memory and Neural Computing 2D Materials and Applications Topological Materials and Phenomena

Publication details

Journal / source
ACS Applied Materials & Interfaces
Publisher
American Chemical Society (ACS)
Volume
18
Issue
14
Pages
20860–20868
Publication date
6 April 2026
Language
EN