ZnO-ITO/WO_3−x heterojunction structured memristor for optoelectronic co-modulation neuromorphic computation

Traditional transistors confront severe challenges of insufficient computing capability and excessive power consumption in large-scale neuromorphic systems. To address these critical bottlenecks, we propose an optoelectronic memristor based on zinc oxide-indium tin oxide/tungsten oxide (ZnO-ITO/WO_3−x) heterojunctions as a promising solution. Through applying different types of electrical and optical signals, the device successfully emulates diverse synaptic functions including short-term/long-term synaptic plasticity, alongside short-term and long-term memory. Introducing the ZnO-ITO functional layer enhances the photoresponse of the WO_3−x-based memristor and demonstrates “learning-forgetting-relearning” behavior under optical modulation. Furthermore, based on the photoelectric cooperative memristor array, a convolutional neural network for vehicle type recognition is constructed, which solves the problem of zero weight and negative weight complexity. In regard to energy efficiency, the neural network built with this device operates at a power level of only 10⁻³ W, representing a reduction of more than 4 orders of magnitude compared with a standard central processor. Hence, the photoelectric memristor proposed in this work provides a new idea for neuromorphic computing and is expected to promote the development of energy-efficient brain-like computing.