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Analytical modeling electrical conduction in resistive-switching memory through current-limiting-friendly combination frameworks
Journal article   Peer reviewed

Analytical modeling electrical conduction in resistive-switching memory through current-limiting-friendly combination frameworks

Qishen Wang, Karthekeyan Periasamy, Yi Fu, Ya-Ting Chan, Cher Ming Tan, Natasa Bajalovic, Jer-Chyi Wang and Desmond K. Loke
AIP advances, Vol.10(8), pp.085117-085117-6
01/08/2020

Abstract

Materials Science Materials Science, Multidisciplinary Nanoscience & Nanotechnology Physical Sciences Physics Physics, Applied Science & Technology Science & Technology - Other Topics Technology
Resistive-switching memory (RSM) is one of the most promising candidates for next-generation edge computing devices due to its excellent device performance. Currently, a number of experimental and modeling studies have been reported to understand the conduction behaviors. However, a complete physical picture that can describe the conduction behavior is still missing. Here, we present a conduction model that not only fully accounts for the rich conduction behaviors of RSM devices by harnessing a combination of electronic and thermal considerations via electron mobility and trap-depth and with excellent accuracy but also provides critical insight for continued design, optimization, and application. A physical model that is able to describe both the conduction and switching behaviors using only a single set of expressions is achieved. The proposed model reveals the role of temperature, mobility of electrons, and depth of traps, and allows accurate prediction of various set and reset processes obtained by an entirely new set of general current-limiting parameters.
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https://doi.org/10.1063/5.0019266View
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