A 2D‐3D Perovskite Memristor‐Based Light‐Induced Sensitized Neuron for Visual Information Processing

H Hang‐fei Li (College of Electronics and Information Engineering Shenzhen University Shenzhen 518060 P. R. China) J Jiashun Liu S Sunyingyue Geng (College of Electronics and Information Engineering Shenzhen University Shenzhen 518060 P. R. China) T Tao Sun Z Ziyu Lv Y Yongbiao Zhai Y Ye Zhou S Su‐Ting Han (Department of Applied Biology and Chemical Technology and Research Institute for Smart Energy The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong 999077 P. R. China)

Abstract

Abstract Implementing Leaky Integrate‐and‐Fire (LIF) neurons in hardware is poised to enable the creation of efficient, low‐power spiking neural networks (SNNs). This is attributed to the ability of LIF neurons to mimic the rapid response and sensitivity of biological neurons, thereby reducing unnecessary computational resources. The fixed firing frequency of conventional LIF neurons limits their adaptability to complex, dynamic environments. Existing variable‐frequency LIF neurons often require additional circuitry, which increases system complexity. In this study, a 2D‐3D organic‐inorganic hybrid perovskites (OHPs) memristor is presented, incorporating 2D passivation of methylammonium lead iodide (MAPbI 3 ) with phenylethylammonium iodide (PEAI). The introduction of the 2D layer increases the migration energy barrier and restricts the diffusion of ions, thus enabling the modulation of the current decay and light responsivity. By leveraging the tunable decay and wavelength selectivity of the memristor, a light‐induced sensitized neuron (LISN) with an enhanced firing frequency is developed using a fundamental circuit design. Furthermore, LISN, which exhibits improved temporal processing and long‐term dependency management, are integrated into sensitized spiking neural networks (SSNNs) to demonstrate their superior classification capabilities. This study underscores the potential of LISN‐based neuromorphic systems in visual information processing and offers new insights for applications in complex scenarios.

Article Details

Volume / Issue Vol. 37, Issue 42
Published October 01, 2025
ISSN 0935-9648
Publisher Unknown Publisher

Journal Info

Advanced Materials

Unknown Publisher

ISSN: 0935-9648 Physical Sciences

Authors (8)

H

Hang‐fei Li

College of Electronics and Information Engineering Shenzhen University Shenzhen 518060 P. R. China

J

Jiashun Liu

S

Sunyingyue Geng

College of Electronics and Information Engineering Shenzhen University Shenzhen 518060 P. R. China

T

Tao Sun

Z

Ziyu Lv

Y

Yongbiao Zhai

Y

Ye Zhou

S

Su‐Ting Han

Department of Applied Biology and Chemical Technology and Research Institute for Smart Energy The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong 999077 P. R. China