Low Energy Consumption Photoelectric Memristors with Multi-Level Linear Conductance Modulation in Artificial Visual Systems Application - Zhou, Zhenyu; Zhang, Zixuan; Li, Pengfei; Guan, Zhiyuan; Li, Yuchen; Li, Xiaoxu; Xu, Shan; Zhao, Jianhui; Yan, Xiaobing (Nano-Micro Letters, 2025)
Optical synapses have an ability to perceive and remember visual information, making them expected to provide more intelligent and efficient visual solutions for humans. As a new type of artificial visual sensory devices, photoelectric memristors can fully simulate synaptic performance and have great prospects in the development of biological vision. However, due to the urgent problems of nonlinear conductance and high-energy consumption, its further application in high-precision control scenarios and integration is hindered. In this work, we report an optoelectronic memristor with a structure of TiN/CeO2/ZnO/ITO/Mica, which can achieve minimal energy consumption (187 pJ) at a single pulse (0.5 V, 5 ms). Under the stimulation of continuous pulses, linearity can be achieved up to 99.6%. In addition, the device has a variety of synaptic functions under the combined action of photoelectric, which can be used for advanced vision. By utilizing its typical long-term memory characteristics, we achieved image recognition and long-term memory in a 3 × 3 synaptic array and further achieved female facial feature extraction behavior with an activation rate of over 92%. Moreover, we also use the linear response characteristic of the device to design and implement the night meeting behavior of autonomous vehicles based on the hardware platform. This work highlights the potential of photoelectric memristors for advancing neuromorphic vision systems, offering a new direction for bionic eyes and visual automation technology. Highlights: 1 Developed a novel photo-memristor with single-pulse low energy consumption (187 pJ) and multi-pulse linearity up to 0.996. 2 Using photoelectric synaptic characteristics, achieved long-term memory in a 3 × 3 array and over 92% activation of female facial feature recognition in a 64 × 64 model. 3 Using the continuous response characteristics of optical synapses, an intelligent driving system with automatic night meeting was designed.