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Simultaneous achieving negative photoconductivity response and volatile resistive switching in Cs2CoCl4 single crystals towards artificial optoelectronic synapse

Authors :
Huifang Jiang
Huifang Ji
Zhuangzhuang Ma
Dongwen Yang
Jingli Ma
Mengyao Zhang
Xu Li
Meng Wang
Ying Li
Xu Chen
Di Wu
Xinjian Li
Chongxin Shan
Zhifeng Shi
Source :
Light: Science & Applications, Vol 13, Iss 1, Pp 1-13 (2024)
Publication Year :
2024
Publisher :
Nature Publishing Group, 2024.

Abstract

Abstract The development of negative photoconductivity (NPC)-related devices is of great significance for numerous applications, such as optoelectronic detection, neuromorphic computing, and optoelectronic synapses. Here, an unusual but interesting NPC phenomenon in the novel cesium cobalt chlorine (Cs2CoCl4) single crystal-based optoelectronic devices is reported, which simultaneously possess volatile resistive switching (RS) memory behavior. Joint experiment−theory characterizations reveal that the NPC behavior is derived from the intrinsic vacancy defects of Cs2CoCl4, which could trap photogenerated charge carriers and produce an internal electric field opposite to the applied electric field. Such NPC effect enables an abnormal photodetection performance with a decrease in electrical conductivity to illumination. Also, a large specific detectivity of 2.7 × 1012 Jones and broadband NPC detection wavelength from 265 to 780 nm were achieved. In addition to the NPC response, the resulting devices demonstrate a volatile RS performance with a record-low electric field of 5 × 104 V m−1. By integrating the characteristics of electric-pulse enhancement from RS and light-pulse depression from NPC, an artificial optoelectronic synapse was successfully demonstrated, and based on the simulation of artificial neural network algorithm, the recognition application of handwritten digital images was realized. These pioneer findings are anticipated to contribute significantly to the practical advancement of metal halides in the fields of in-memory technologies and artificial intelligence.

Details

Language :
English
ISSN :
20477538
Volume :
13
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Light: Science & Applications
Publication Type :
Academic Journal
Accession number :
edsdoj.b40f71644e4d445b95da4f66ffd47203
Document Type :
article
Full Text :
https://doi.org/10.1038/s41377-024-01642-8