Adaptive Convolutional Neural Networks for Enhanced Memory Retention and Restoration in Optoelectronic Vision Devices

Abstract

Optoelectronic devices based on optically responsive materials have gained significant attention due to their low cross talk and reduced power consumption. These devices rely on light‐induced changes in conductance states, which are used to create synaptic weights for image recognition tasks in neural networks. However, a major drawback of such devices is the rapid decay of conductance states after light stimulus removal, which hinders their long‐term memory and performance without a continuous external stimulus in place. To address this issue, a platform neural network scheme is proposed to counter the natural decay of conductance in optoelectronic devices. The approach restores the memory effect of the devices and significantly enhances their performance by several orders of magnitude without using additional energy‐intensive techniques like training pulses or gate fields. Herein, the model is validated experimentally using optoelectronic devices fabricated with two different materials, BP and doped In2O3, and demonstrates the restoration of memory/image retention ability to any material system being studied for optoelectronic synapses and vision. This approach has important implications for the practical application of neuromorphic visual processing technologies, bringing them closer to real‐world applications.

Keywords

• convolutional neural networks
• machine vision
• memory retention
• neuromorphic photonics
• optoelectronics