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<p>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 CsPbBr<sub>1.5</sub>I<sub>1.5</sub> 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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月1日

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  • 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

摘要

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.