Photo‐synaptic Memristor Devices from Solution‐processed Ga<sub>2</sub>O<sub>3</sub> Thin Films

Abstract Hardware integration with biological synaptic function is the key to realizing brain‐like computing. Resistive Random Access Memory (RRAM), with a similar structure to biological synapses, are important candidate for the simulation of biological synaptic function. In this work, Ga 2 O 3 film as a functional layer of RRAM is prepared by the solution method, and an RRAM‐based photo‐synaptic device with an Ag/Ga 2 O 3 /Si structure is constructed subsequently. The device exhibits excellent bipolar resistive switching characteristics, with the merits of a large storage window and long retention time. Furthermore, the devices generated excitatory postsynaptic currents (EPSC) and paired‐pulse facilitation (PPF) behaviors under light pulse stimulation, enabling the simulation of synaptic plasticity. The transformation of synaptic behavior from short‐term memory (STM) to long‐term memory (LTM) is achieved by observing the spike‐duration dependent plasticity (SDDP), spike‐intensity dependent plasticity (SIDP), spike‐number dependent plasticity (SNDP) and spike‐rate dependent plasticity (SRDP) characteristics of photonic synapses under different conditions. The device also simulates the process of successive “learning‐forgotten‐remembering”, revealing that RRAM‐based photonic synapses have great potential in the fields of artificial visual perception and memory storage.

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Publication Details

Journal
Advanced Electronic Materials
Published
2024-10-22
DOI
https://doi.org/10.1002/aelm.202400512
Citations
13
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
2.71

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article

Photo‐synaptic Memristor Devices from Solution‐processed Ga2O3 Thin Films

Zhenhua Lin, Dongsheng Cui, Jincheng Zhang, Jingjing Chang et al.
13 citations
Advanced Electronic Materials
Advanced Memory and Neural Computing
2.71
article

Photo‐synaptic Memristor Devices from Solution‐processed Ga2O3 Thin Films

Zhenhua Lin, Dongsheng Cui, Jincheng Zhang, Jingjing Chang, Haoyu Bai, Jie Su, Xiangxiang Gao, Yue Hao, Wei Wang
article en
13 citations

Abstract

Abstract Hardware integration with biological synaptic function is the key to realizing brain‐like computing. Resistive Random Access Memory (RRAM), with a similar structure to biological synapses, are important candidate for the simulation of biological synaptic function. In this work, Ga 2 O 3 film as a functional layer of RRAM is prepared by the solution method, and an RRAM‐based photo‐synaptic device with an Ag/Ga 2 O 3 /Si structure is constructed subsequently. The device exhibits excellent bipolar resistive switching characteristics, with the merits of a large storage window and long retention time. Furthermore, the devices generated excitatory postsynaptic currents (EPSC) and paired‐pulse facilitation (PPF) behaviors under light pulse stimulation, enabling the simulation of synaptic plasticity. The transformation of synaptic behavior from short‐term memory (STM) to long‐term memory (LTM) is achieved by observing the spike‐duration dependent plasticity (SDDP), spike‐intensity dependent plasticity (SIDP), spike‐number dependent plasticity (SNDP) and spike‐rate dependent plasticity (SRDP) characteristics of photonic synapses under different conditions. The device also simulates the process of successive “learning‐forgotten‐remembering”, revealing that RRAM‐based photonic synapses have great potential in the fields of artificial visual perception and memory storage.

Advanced Electronic MaterialsVol. 11(4)
Xidian University (CN)
National Natural Science Foundation of China, China Postdoctoral Science Foundation, Xidian University, National Key Research and Development Program of China, Fundamental Research Funds for the Central Universities, Key Research and Development Projects of Shaanxi Province, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Natural Science Basic Research Program of Shaanxi Province, Basic and Applied Basic Research Foundation of Guangdong Province
Openalex Percentile: Top 9%
Advanced Memory and Neural Computing
2.71
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