CuSbS<sub>2</sub>-Based Near-Infrared Optoelectronic Synaptic Memristor: Toward Biomimetic Applications with Superior Synaptic Plasticity

Emerging near-infrared (NIR) optoelectronic synaptic devices, which enable parallel perception and memorization of information, play a pivotal role in developing high-efficiency neuromorphic computing systems with visual perception and complex learning capabilities. Here, CuSbS<sub>2</sub> is used in an innovative method to achieve an artificial NIR optoelectronic synapse. This CuSbS<sub>2</sub> memristor not only exhibits stable nonvolatile resistive switching behaviors, featuring low operating voltages (-0.69/0.68 V) with variations [Δ<i>V</i>(σ)/μ] both less than 13% and long retention time exceeding 10<sup>4</sup> s, but also realizes diverse synaptic plasticity triggered by both electric signals and NIR light, with the achievement of comprehensive synaptic functionalities, including excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), short-term to long-term potentiation (STP/LTP), short-term and long-term memory (STM/LTM), as well as spike-timing-dependent plasticity (STDP). Moreover, utilizing the excellent optoelectric performance of CuSbS<sub>2</sub>, neuromorphic functions of designing a high-accuracy NIR imaging system are implemented, further verifying its practical application potential. The results confirm that CuSbS<sub>2</sub> is a highly promising candidate material for constructing NIR optoelectronic artificial synapses for advanced synaptic applications, paving a solid way for the future development of neuromorphic systems integrating sensing, memory, and processing capabilities.

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Publication Details

Journal
The Journal of Physical Chemistry Letters
Published
2025-09-25
DOI
https://doi.org/10.1021/acs.jpclett.5c02625
Citations
1
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.73
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article

CuSbS2-Based Near-Infrared Optoelectronic Synaptic Memristor: Toward Biomimetic Applications with Superior Synaptic Plasticity

Zhanchuan Cai, Jianping Lan, Yan Li, Fengxia Yang
1 citations
The Journal of Physical Chemistry Letters
Advanced Memory and Neural Computing
0.73
article

CuSbS2-Based Near-Infrared Optoelectronic Synaptic Memristor: Toward Biomimetic Applications with Superior Synaptic Plasticity

Zhanchuan Cai, Jianping Lan, Yan Li, Fengxia Yang
article en
1 citations

Abstract

Emerging near-infrared (NIR) optoelectronic synaptic devices, which enable parallel perception and memorization of information, play a pivotal role in developing high-efficiency neuromorphic computing systems with visual perception and complex learning capabilities. Here, CuSbS<sub>2</sub> is used in an innovative method to achieve an artificial NIR optoelectronic synapse. This CuSbS<sub>2</sub> memristor not only exhibits stable nonvolatile resistive switching behaviors, featuring low operating voltages (-0.69/0.68 V) with variations [Δ<i>V</i>(σ)/μ] both less than 13% and long retention time exceeding 10<sup>4</sup> s, but also realizes diverse synaptic plasticity triggered by both electric signals and NIR light, with the achievement of comprehensive synaptic functionalities, including excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), short-term to long-term potentiation (STP/LTP), short-term and long-term memory (STM/LTM), as well as spike-timing-dependent plasticity (STDP). Moreover, utilizing the excellent optoelectric performance of CuSbS<sub>2</sub>, neuromorphic functions of designing a high-accuracy NIR imaging system are implemented, further verifying its practical application potential. The results confirm that CuSbS<sub>2</sub> is a highly promising candidate material for constructing NIR optoelectronic artificial synapses for advanced synaptic applications, paving a solid way for the future development of neuromorphic systems integrating sensing, memory, and processing capabilities.

The Journal of Physical Chemistry LettersVol. 16(39)
Macau University of Science and Technology (MO), Institute of Molecular Functional Materials (CN), Hubei University of Automotive Technology (CN), Intelligent Health (United Kingdom) (GB), Northwest Normal University (CN)
Openalex Percentile: Top 23%
Advanced Memory and Neural Computing
0.73
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