Optical synaptic devices with ultra-low power consumption for neuromorphic computing

Chenguang Zhu; Huawei Liu; Wenqiang Wang; Li Xiang; Jie Jiang; Qin Shuai; Xin Yang; Tian Zhang; Biyuan Zheng; Hui Wang; Dong Li; Anlian Pan

doi:10.1038/s41377-022-01031-z

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Optical synaptic devices with ultra-low power consumption for neuromorphic computing

Original Articles

- Optical synaptic devices with ultra-low power consumption for neuromorphic computing
- Optical synaptic devices with ultra-low power consumption for neuromorphic computing
- LSA 2022年11卷第12期页码：3008-3017
- Affiliations：
  
  1.Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Materials Science and Engineering, Hunan University, 410082, Changsha, China
  2.Hunan Institute of Optoelectronic Integration, Hunan University, 410082, Changsha, China
  3.School of Physics and Electronics, Central South University, 410083, Changsha, China
- Author bio：
  
  Li Xiang (xiangli93@hnu.edu.cn)
  Dong Li (liidong@hnu.edu.cn)
  Anlian Pan (anlian.pan@hnu.edu.cn)
- Funds：
- DOI：10.1038/s41377-022-01031-z
  中图分类号：
- 纸质出版日期：2022-12-31，
  
  网络出版日期：2022-11-29，
  
  收稿日期：2022-07-20，
  
  修回日期：2022-10-31，
  
  录用日期：2022-11-01
- Accepted：
Scan QR Code
Optical synaptic devices with ultra-low power consumption for neuromorphic computing[J]. LSA, 2022,11(12):3008-3017.

Zhu, C. G. et al. Optical synaptic devices with ultra-low power consumption for neuromorphic computing. Light: Science & Applications, 11, 3008-3017 (2022).
Optical synaptic devices with ultra-low power consumption for neuromorphic computing[J]. LSA, 2022,11(12):3008-3017. DOI： 10.1038/s41377-022-01031-z.

Zhu, C. G. et al. Optical synaptic devices with ultra-low power consumption for neuromorphic computing. Light: Science & Applications, 11, 3008-3017 (2022). DOI： 10.1038/s41377-022-01031-z.

摘要

Abstract

Brain-inspired neuromorphic computing

featured by parallel computing

is considered as one of the most energy-efficient and time-saving architectures for massive data computing. However

photonic synapse

one of the key components

is still suffering high power consumption

potentially limiting its applications in artificial neural system. In this study

we present a BP/CdS heterostructure-based artificial photonic synapse with ultra-low power consumption. The device shows remarkable negative light response with maximum responsivity up to 4.1 × 10

A W

−1

= 0.5 V and light power intensity of 0.16 μW cm

−2

(1.78 × 10

A W

−1

on average)

which further enables artificial synaptic applications with average power consumption as low as 4.78 fJ for each training process

representing the lowest among the reported results. Finally

a fully-connected optoelectronic neural network (FONN) is simulated with maximum image recognition accuracy up to 94.1%. This study provides new concept towards the designing of energy-efficient artificial photonic synapse and shows great potential in high-performance neuromorphic vision systems.

关键词

Keywords

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