Studies on charge trap memristor based bio-inspired neuromorphic devices

Abstract

With the increasing volume of data that needs to be processed, research on neuromorphic devices is emerging that mimic energetically efficient biological systems. From a hardware perspective, recent studies aim to replicate the parallel information processing characteristics of the central nervous system capable of efficient processing for large amounts of information. Concurrently, active research seeks to emulate complex biological features using simple electronic components. Although various next-generation semiconductor devices are being explored to realize this, challenges such as leakage currents, device variability, and reliability issues persist for the large-scale integration. Continuous research efforts are necessary to address these issues. In this study, I aimed to improve the limitations of existing memristor-based next-generation semiconductor devices based on the charge trap mechanism. Through an examination of the charge trap-based operational mechanism and band engineering of the oxide thin film layer, I successfully implemented stable characteristics in artificial synapse components. Furthermore, by implementing a biomimetic artificial nociceptor with adjustable thresholds for the first time, I aimed to present a new direction in the research of next-generation neuromorphic devices.