Reconfigurable Selector-Free All-Optical Controlled Neuromorphic Memristor for In-Memory Sensing and Reservoir Computing
Lu Chen, Jialin Meng, Jieru Song, Kangli Xu, Tianyu Wang, Hao Zhu, Qingqing Sun, David Wei Zhang, Lin Chen
Abstract
Recently, the rising demand for data-based applications has driven the convergence of image sensing, memory, and computing unit interfaces. While specialized electronic hardware has spurred advancements in the in-memory and in-sensor computing, integrating the entire signal-processing chain into a single device still faces significant challenges. Here, a reconfigurable all-optical controlled memristor with the selector-free feature is demonstrated. The conductance of the device can be controlled within the pure light domain, which enables it to integrate sensing, memory, and computing together. The integrate-and-fire behavior is also realized through electrical stimuli. Furthermore, the device exhibits an excellent rectifying ratio and nonlinearity to overcome the sneak current. Finally, an in-memory sensing and computing architecture is realized through reservoir computing based on neuron and synaptic functions mimicked by the proposed device. Such an all-in-one paradigm facilitates the computing architecture with low energy consumption, low latency, and reduced hardware complexity.