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

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 () and Johnny C. Ho ()
Additional contact information
Dengji Li: City University of Hong Kong
Pengshan Xie: City University of Hong Kong
Yuekun Yang: Nanjing University
Yunfan Wang: City University of Hong Kong
Changyong Lan: University of Electronic Science and Technology of China
Yiyang Wei: University of Electronic Science and Technology of China
Jiachi Liao: City University of Hong Kong
Bowen Li: City University of Hong Kong
Zenghui Wu: City University of Hong Kong
Quan Quan: City University of Hong Kong
Yuxuan Zhang: City University of Hong Kong
You Meng: City University of Hong Kong
Mingqi Ding: City University of Hong Kong
Yan Yan: City University of Hong Kong
Yi Shen: City University of Hong Kong
Weijun Wang: City University of Hong Kong
Sai-Wing Tsang: City University of Hong Kong
Shi-Jun Liang: Nanjing University
Feng Miao: Nanjing University
Johnny C. Ho: City University of Hong Kong

Nature Communications, 2025, vol. 16, issue 1, 1-10

Abstract: Abstract 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.

Date: 2025
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DOI: 10.1038/s41467-025-60530-w

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