Memristive synapse based on perovskite Cs3Cu2I5 for reservoir computing

作者：Bin Li, Yao Meng, Yin Zheng, Jinwen Lin, Jiang Wu, Hong Zhou, Xinman Chen · 发表于：Applied Physics Letters · 年份：2025 · DOI：10.1063/5.0281204 · 被引用次数：2 · 研究领域：Advanced Memory and Neural Computing、Neural Networks and Reservoir Computing、Neural dynamics and brain function

Perovskite memristors have attracted considerable attention for their potential in emulating artificial synapses. However, the widespread use of the toxic lead-based perovskites poses significant challenges to futural application. In this work, we developed a lead-free memristor with an Ag/PMMA/Cs3Cu2I5/ITO architecture, in which the Cs3Cu2I5 halide perovskites functional layer was fabricated by physical vapor deposition. The memristor demonstrated a data retention time of 104 s and stable resistive switching behavior over 100 cycles under electrical pulses stimulation. Furthermore, it emulated a range of biologically relevant synaptic functions, including paired-pulse facilitation, short-term plasticity, long-term plasticity, spike-amplitude-dependent plasticity, spike-number-dependent plasticity, and spike-duration-dependent plasticity. Capitalizing on its nonlinear dynamics and short-term memory characteristics, the device was further integrated into a reservoir computing (RC) system. The RC system demonstrated strong recognition performance and robustness when tested with distorted digital image datasets. These results suggest that the Cs3Cu2I5-based memristor provides a promising, environmentally friendly platform for next-generation artificial intelligence hardware.