Low-Power Perovskite Neuromorphic Synapse with Enhanced Photon Efficiency for Directional Motion Perception

TLDR

Artificial intelligence vision systems require fast, accurate optical imaging, yet traditional optoelectronic devices struggle with limited response speed and low computational efficiency, hindering real‑time visual processing. The study introduces a novel photomemristor based on halide perovskite thin films to overcome limitations of existing optoelectronic devices. The photomemristor is fabricated by tuning iodide content to improve film quality and reduce dark current, and its spike‑timing‑dependent plasticity is employed to build a spiking neural network for directional perception. The device achieves >85 % external quantum efficiency, consumes only 0.6 nJ, and attains 99.1 % accuracy in directional perception, demonstrating a promising hardware solution for efficient optoneuromorphic and edge computing.

Abstract

The advancement of artificial intelligent vision systems heavily relies on the development of fast and accurate optical imaging detection, identification, and tracking. Framed by restricted response speeds and low computational efficiency, traditional optoelectronic information devices are facing challenges in real-time optical imaging tasks and their ability to efficiently process complex visual data. To address the limitations of current optoelectronic information devices, this study introduces a novel photomemristor utilizing halide perovskite thin films. The fabrication process involves adjusting the iodide proportion to enhance the quality of the halide perovskite films and minimize the dark current. The photomemristor exhibits a high external quantum efficiency of over 85%, which leads to a low energy consumption of 0.6 nJ. The spike timing-dependent plasticity characteristics of the device are leveraged to construct a spiking neural network and achieve a 99.1% accuracy rate of directional perception for moving objects. The notable results offer a promising hardware solution for efficient optoneuromorphic and edge computing applications.