Flexible, Lead-Free (C <sub>6</sub> H <sub>5</sub> CH <sub>2</sub> NH <sub>3</sub> ) <sub>2</sub> CuBr <sub>4</sub> Optoelectronic Memristors for Multimodal Synaptic Emulation and Neuromorphic Learning

Inspired by the highly parallel information processing of biological neural networks, neuromorphic computing based on artificial synapses offers a promising route to overcome the von Neumann bottleneck. Here, we report a flexible optoelectronic memristor employing two-dimensional (C6H5CH2NH3)2CuBr4 perovskite, which simultaneously supports electrical and optical stimuli. The device exhibits bidirectional and multilevel conductance modulation, emulating key synaptic functions including pairedpulse facilitation (PPF), spiketimingdependent plasticity (STDP), spikedurationdependent plasticity (SDDP), and long-term potentiation and depression (LTP, LTD). Ultraviolet illumination lowers SET/RESET voltages and induces photopairedpulse facilitation and cumulative conductance potentiation, demonstrating photoinduced synaptic plasticity. Besides, the memristor maintains stable switching characteristics over 1000 electrical cycles, 104 s retention, 500 bending cycles, and twoweek exposure at 50% relative humidity, underscoring its environmental and mechanical robustness. Finally, a 7 × 7 memristor array network is shown via simulation to perform unsupervised image learning with high fidelity. These results highlight a versatile, leadfree perovskite platform for flexible, lowpower, multimodal neuromorphic hardware.

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Publication Details

Journal
ACS Applied Electronic Materials
Published
2025-10-20
DOI
https://doi.org/10.1021/acsaelm.5c01418
Primary Topic
Advanced Memory and Neural Computing
Type
article
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article

Flexible, Lead-Free (C 6 H 5 CH 2 NH 3 ) 2 CuBr 4 Optoelectronic Memristors for Multimodal Synaptic Emulation and Neuromorphic Learning

Xia Xiao, Dongke Li, Mengting Dong, Tong Chen et al.
ACS Applied Electronic Materials
Advanced Memory and Neural Computing
article

Flexible, Lead-Free (C 6 H 5 CH 2 NH 3 ) 2 CuBr 4 Optoelectronic Memristors for Multimodal Synaptic Emulation and Neuromorphic Learning

Xia Xiao, Dongke Li, Mengting Dong, Tong Chen, Xiaofan Zhou, Ying Li, Jiajun Guo
article en

Abstract

Inspired by the highly parallel information processing of biological neural networks, neuromorphic computing based on artificial synapses offers a promising route to overcome the von Neumann bottleneck. Here, we report a flexible optoelectronic memristor employing two-dimensional (C6H5CH2NH3)2CuBr4 perovskite, which simultaneously supports electrical and optical stimuli. The device exhibits bidirectional and multilevel conductance modulation, emulating key synaptic functions including pairedpulse facilitation (PPF), spiketimingdependent plasticity (STDP), spikedurationdependent plasticity (SDDP), and long-term potentiation and depression (LTP, LTD). Ultraviolet illumination lowers SET/RESET voltages and induces photopairedpulse facilitation and cumulative conductance potentiation, demonstrating photoinduced synaptic plasticity. Besides, the memristor maintains stable switching characteristics over 1000 electrical cycles, 104 s retention, 500 bending cycles, and twoweek exposure at 50% relative humidity, underscoring its environmental and mechanical robustness. Finally, a 7 × 7 memristor array network is shown via simulation to perform unsupervised image learning with high fidelity. These results highlight a versatile, leadfree perovskite platform for flexible, lowpower, multimodal neuromorphic hardware.

ACS Applied Electronic MaterialsVol. 7(21)
PepsiCo (United Kingdom) (GB), Liaocheng University (CN), National Laboratory of Solid State Microstructures, Zhejiang University (CN), Shandong University of Science and Technology (CN), Nanjing University (CN)
Openalex Percentile: Top 70%
Advanced Memory and Neural Computing
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