Artificial Visual Synaptic Architecture with High-Linearity Light-Modulated Weight for Optoelectronic Neuromorphic Computing

A brain-like neuromorphic computing system, as compared with traditional Von Neumann architecture, has broad application prospects in the fields of emerging artificial intelligence (AI) due to its high fault tolerance, excellent plasticity, and parallel computing capability. A neuromorphic visuosensory and memory system, an important branch of neuromorphic computing, is the basis for AI to perceive, process, and memorize optical information, now still suffering from nonlinearity of synaptic weight, crosstalk issues, and integration incompatibility, hindering the high-level training and inference accuracy. In this work, we propose a new optoelectronic neuromorphic architecture by integrating an electrochromic device and a perovskite photodetector. Ascribing to the superior memory characteristics of the electrochromic device and sensitive light response of the perovskite photodetector, the neuromorphic device shows typical visual synaptic functionalities such as light triggering, neural facilitation, long-term potentiation, and depression (LTP and LTD). Furthermore, by adjusting the intensity and wavelength of external light signals, the visual synaptic function of the device can be modulated, enabling the device to exhibit high weight linearity in all current output ranges and improve information processing capability and image recognition accuracy. Moreover, both the electrochromic and perovskite layers possess the advantage of large area fabrication and integration, which enables the fabrication of large device arrays with high integration compatibility and scalability. In this study, 10 × 10 device arrays are demonstrated and each device shows uniform light responses, memory behaviors, and synaptic performances. MNIST and CIFAR-10 algorithms are used to simulate the image recognition properties of the synaptic architecture, and the calculated recognition accuracy is 97.94 and 91.04%, respectively, with an error less than 2.5%. The proposed artificial visual neuromorphic architecture will provide a potential device prototype for efficient visual neuromorphic systems.