Enhanced In-Sensor Computing with Spike Number-Dependent Plasticity Characteristics in an InGaSnO Optical Neuromorphic Device for Accelerating Machine Vision

Abstract

In-sensor computing systems based on optical neuromorphic devices have attracted increasing attention to improve the efficiency and accuracy of machine vision systems. However, most materials used in optical neuromorphic devices exhibit spike timing-dependent plasticity (STDP) behavior in response to input light signals, leading to complex in-sensor computing and reduced machine vision accuracy. To address this issue, we introduce an indium gallium tin oxide (IGTO) semiconductor designed to enhance spike number-dependent plasticity (SNDP) in response to input light signals while eliminating the STDP behavior. Here, an IGTO-based optical neuromorphic device shows enhanced SNDP characteristics, which are attributed to the strong Sn-O bonding, as verified by photoemission spectroscopy (PES) analysis. The IGTO-based device consistently reaches the same conduction state after 8 light pulses regardless of the pulse timing and also achieves a conduction state based on the number of input light pulses even when 15 different pulse sets are applied. These results exhibit the SNDP characteristics of the IGTO-based device. Notably, in-sensor computing with the SNDP-enhanced device reduces multilayer perceptron (MLP) training time by 87.7% while achieving high classification accuracy. This study demonstrates that in-sensor computing systems with SNDP characteristics in optical neuromorphic devices have significant potential to accelerate machine learning for highly efficient machine vision systems.