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.
Authors
- Xia Xiao (ORCID: https://orcid.org/0000-0003-1307-3535)
- Dongke Li (ORCID: https://orcid.org/0000-0003-0333-7920)
- Mengting Dong (ORCID: https://orcid.org/0000-0003-4012-0800)
- Tong Chen (ORCID: https://orcid.org/0000-0002-8319-1142)
- Xiaofan Zhou (ORCID: https://orcid.org/0000-0002-7028-6943)
- Ying Li
- Jiajun Guo
Institutions
- 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)
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
- Field-Weighted Citation Impact
- 0.00