High-Precision Attention Mechanism for Machine Vision Enabled by an Artificial Optoelectronic Memristor Synapse
Lixun Wang, Yuejun Zhang, Zhecheng Guo, Xiaohan Meng, Qikang Li, Mengfan Xu, Runsheng Gao, Xiaojian Zhu, Pengjun Wang
Abstract
The rapid advancement of artificial intelligence has facilitated the broad application of machine vision systems in diverse industries. However, these systems are often confronted with computational challenges stemming from an overwhelming amount of data. Here, we have developed a novel optoelectronic memristor synapse constructed from an ITO/Nb:SrTiO 3 heterostructure, which synergistically integrates light signal detection with information processing and memory functions. Notably, we have achieved precise decoupling of the interactions between light power and wavelength at the hardware level, significantly enhancing the accuracy and efficiency of image processing. Furthermore, by incorporating an attention mechanism analogous to that of human vision, we have enabled the device to weight key information and filter out irrelevant data. Experimental results demonstrate that this attention mechanism can increase the accuracy of facial recognition by 13% while reducing the data load by 35–65%. This work is expected to advance the development of optoelectronic synapses in machine vision.