神经形态工程学
记忆电阻器
材料科学
计算机科学
光电子学
人工神经网络
电子工程
人工智能
工程类
作者
Hangfei Li,Jiashun Liu,Sunyingyue Geng,Tao Sun,Ziyu Lv,Yongbiao Zhai,Ye Zhou,Su‐Ting Han
标识
DOI:10.1002/adma.202508342
摘要
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.
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