A Gate-Tunable Thermal Persistent Photocurrent Device for In-Sensor Spiking Neural Networks
) 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.
Authors
- Fanxin Liu (ORCID: https://orcid.org/0000-0003-0445-3886)
- L. Zhang
- Pingheng Tan
- Xiao Li
- Yifei Zhang
Institutions
- Nanjing Tech University (CN)
- Chinese Academy of Sciences (CN)
- Flex (Mauritius) (MU)
- Institute of Semiconductors (CN)
- University of Chinese Academy of Sciences (CN)
- Zhejiang University of Technology (CN)
Publication Details
- Journal
- ACS Applied Materials & Interfaces
- Published
- 2026-04-06
- DOI
- https://doi.org/10.1021/acsami.5c25164
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Chinese Academy of Sciences
- Beijing Municipal Natural Science Foundation
- National Key Research and Development Program of China
- Natural Science Foundation of Zhejiang Province