A 2D‐3D Perovskite Memristor‐Based Light‐Induced Sensitized Neuron for Visual Information Processing
Hangfei Li, Jiashun Liu, Sunyingyue Geng, Tao Sun, Ziyu Lv, Yongbiao Zhai, Ye Zhou, Su‐Ting Han
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.