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In-sensor computing hardware based on emerging reconfigurable photosensors can effectively reduce redundant data and decrease power consumption, which can greatly promote the evolution of machine vision. However, because of the complex device structures and low integration abilities, the common architectures mainly lie in two dimensions, resulting in low time and area efficiencies. Here we propose a three-dimensional (3D) neuromorphic photosensor array for parallel in-sensor image processing. It is constructed on a vertical Graphite/CuInP 2 S 6 /Graphite photosensor unit, where the directional Cu + ion migrations after voltage pulse programming enable a reconfigurable photovoltaic effect and an in-sensor computing capability. With a memristor-like device structure, van der Waals interfaces, and a high uniformity with a low crosstalk problem, a 10 × 10 array is fabricated for intelligent image recognition. Furthermore, using a vertically stacked 3D 3 × 3 × 3 array, we demonstrate an in-sensor convolution strategy with high time and area efficiencies.
Wang et al. (Thu,) studied this question.