Defect-induced subgap state engineering in neuromorphic metal-oxide phototransistors for in-sensor color processing

Abstract

Recently, with the rapid development of autonomous vehicles, intelligent robots, and mobile electronics, retina-inspired neuromorphic photosensors have attracted growing interest as color image processors for machine vision systems. These devices typically mimic essential functions of the human retina, such as multicolor detection and the processing of raw visual information. However, most neuromorphic photosensors have been implemented with heterojunction channel structures or complex circuit architectures, resulting in system complexity, low resolution, and inefficient energy consumption. Here, we propose a neuromorphic phototransistor with a homogeneous channel structure using subgap-engineered metal-oxide (MO) semiconductors. Despite the absence of conventional heterojunction channel architectures, subgap-engineered MO phototransistors, achieved through intentional doping with alkali metal ions, demonstrate broad spectral responsivity and analog conductance modulation in a compact device structure. Particularly, when doped with a small amount of alkali metal ions (Li 5 at%-MO), the device exhibits in-sensor color image processing capabilities, including full-color detection, enhanced analog conductivity, and distinct sensing performance based on the input color. By applying a 7 × 7 neuromorphic phototransistor array using the Li 5 at%-MO semiconductor as the frontend device, innovative refinement tasks of raw color images such as color character sharpness, noise reduction, and contrast enhancement were successfully achieved, significantly contributing to the overall performance enhancement of the machine vision system.