Neuromorphic Active Pixel Image Sensor Array for Visual Memory

Neuromorphic engineering, a methodology for emulating synaptic functions or neural systems, has attracted tremendous attention for achieving next-generation artificial intelligence technologies in the field of electronics and photonics. However, to emulate human visual memory, an active pixel sensor array for neuromorphic photonics has yet to be demonstrated, even though it can implement an artificial neuron array in hardware because individual pixels can act as artificial neurons. Here, we present a neuromorphic active pixel image sensor array (NAPISA) chip based on an amorphous oxide semiconductor heterostructure, emulating the human visual memory. In the 8 x 8 NAPISA chip, each pixel with a select transistor and a neuromorphic phototransistor is based on a solution-processed indium zinc oxide back channel layer and sputtered indium gallium zinc oxide front channel layer. These materials are used as a triggering layer for persistent photoconductivity and a high-performance channel layer with outstanding uniformity. The phototransistors in the pixels exhibit both photonic potentiation and depression characteristics by a constant negative and positive gate bias due to charge trapping/detrapping. The visual memory and forgetting behaviors of the NAPISA can be successfully demonstrated by using the pulsed light stencil method without any software or simulation. This study provides valuable information to other neuromorphic devices and systems for next-generation artificial intelligence technologies.

Automated summary

Neuromorphic engineering has attracted attention for achieving next-generation artificial intelligence technologies in the field of electronics and photonics. An active pixel sensor array for neuromorphic photonics, based on an amorphous oxide semiconductor heterostructure, has been demonstrated to emulate human visual memory. The phototransistors in the pixels exhibit both photonic potentiation and depression characteristics, and the visual memory and forgetting behaviors can be successfully demonstrated without any software or simulation. This study provides valuable information for other neuromorphic devices and systems for next-generation artificial intelligence technologies.