In-material physical computing based on reconfigurable microwire arrays via halide-ion segregation

作者：Dengji Li, Pengshan Xie, Yuekun Yang, Yunfan Wang, Changyong Lan, Yiyang Wei, Jiachi Liao, Bowen Li, Zenghui Wu, Quan Quan, Yuxuan Zhang, You Meng, Mingqi Ding, Yan Yan, Yi Shen, Weijun Wang, Sai‐Wing Tsang, Shi‐Jun Liang, Feng Miao, Johnny C. Ho · 发表于：Nature Communications · 年份：2025 · DOI：10.1038/s41467-025-60530-w · 被引用次数：5 · 研究领域：Advanced Memory and Neural Computing、Perovskite Materials and Applications、Ferroelectric and Negative Capacitance Devices

Conventional computer systems based on the Von Neumann architecture rely on silicon transistors with binary states for information representation and processing. However, exploiting emerging materials’ intrinsic physical properties and dynamic behaviors offers a promising pathway for developing next-generation brain-inspired neuromorphic hardware. Here, we introduce a stable and controllable photoelectricity-induced halide-ion segregation effect in epitaxially grown mixed-halide perovskite CsPbBr1.5I1.5 microwire networks on mica, as confirmed by various in-situ measurements. The dynamic segregation and recovery processes show the reconfigurable, self-powered photoresponse, enabling non-volatile light information storage and precise modulation of optoelectronic properties. Furthermore, our microwire array successfully addressed a typical graphical neural network problem and an image restoration task without external circuits, underscoring the potential of in-material dynamics to achieve highly parallel and energy-efficient physical computing in the post-Moore era. This study demonstrates stable photoelectricity-induced halide segregation in epitaxial mixed-halide perovskite microwire networks on mica, verified by in-situ observations. Dynamic segregation-recovery processes and reconfigurable self-powered photoresponse reveal physical computing potential.