Design of gelatin-based bionic device for neural computing applications

Abstract

This study introduces an approach to the development of resistive random-access memory (RRAM) devices by utilizing gelatin-based materials and incorporating an ultrathin Al2O3 interface layer fabricated using atomic layer deposition (ALD). The integration of the ALD technology for Al2O3 deposition ensures precise control over the layer thickness and quality, contributing to enhanced device uniformity and stability. The AGAI device can withstand more than 200 switching cycles while maintaining a high ON/OFF ratio exceeding 103, and it also successfully emulates synaptic plasticity characteristics, including long-term synaptic plasticity and short-term synaptic plasticity, as demonstrated by their responses to paired-pulse facilitation and paired-pulse depression, respectively, thereby validating their potential for neural computing applications. Additionally, the hybrid conduction mechanism in the AGAI device, involving aluminum and oxygen vacancy filaments, provides remarkable control over the switching process, leading to repeatability in synaptic characteristics and resistance states. These capabilities introduce new possibilities for the development of neuromorphic computing systems, which aim to mimic the functionality of the human brain in processing and storing information.