eprintid: 18978
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/89/78
datestamp: 2024-06-04 14:11:26
lastmod: 2024-06-04 14:11:26
status_changed: 2024-06-04 14:04:34
type: conference_item
metadata_visibility: show
creators_name: Bature, U.I.
creators_name: Mohd Nawi, I.
creators_name: Md Khir, M.H.
creators_name: Ba Hashwan, S.S.
creators_name: Zahoor, F.
creators_name: Rahman, M.O.
title: Enhancing Energy Efficiency: Statistical Analysis of RRAM Operations using ZnO-based Approach
ispublished: pub
keywords: Energy efficiency; Flash memory; II-VI semiconductors; MATLAB; RRAM, Flow analysis; High-voltages; Memory technology; Multi-physics; Power operation; RRAM operation; Simulation; Thermal energy flow; Two-dimensional; ZnO-based RRAM, Zinc oxide
note: cited By 0; Conference of 2023 IEEE International Conference on Sensors and Nanotechnology, SENNANO 2023 ; Conference Date: 26 September 2023 Through 27 September 2023; Conference Code:195657
abstract: RRAM has garnered significant attention from various perspectives; however, certain barriers hinder its widespread adoption. These challenges revolve around reliability and power operation, making their resolution crucial. This work highlights the effects of operating at high voltages (>10V), which also affect other memory technologies, including Flash memory. The study introduces an insightful approach involving voltage flow analysis coupled with exploring the fundamental thermal energy flow in ZnO-based RRAM stacks operating within 1.2V. The model adopts a two-dimensional arrangement in COMSOL Multiphysics and further employs MATLAB (LiveLinkTM). The research delves into the dynamic resistance switching process, elucidating the role of joule heating effects, field-driven ion transport, and the temperature influence when the operating voltage exceeds 1.2V, as depicted in various plots. This investigation advances our comprehension of the complex dynamics involved in RRAM operation and demonstrates methods to enhance device performance, improve reliability, and ensure long-term functionality in practical applications. © 2023 IEEE.
date: 2023
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182747810&doi=10.1109%2fSENNANO57767.2023.10352520&partnerID=40&md5=17d9927d62c27eeab419282656e088bf
id_number: 10.1109/SENNANO57767.2023.10352520
full_text_status: none
publication: 2023 IEEE International Conference on Sensors and Nanotechnology, SENNANO 2023
pagerange: 150-153
refereed: TRUE
citation:   Bature, U.I. and Mohd Nawi, I. and Md Khir, M.H. and Ba Hashwan, S.S. and Zahoor, F. and Rahman, M.O.  (2023) Enhancing Energy Efficiency: Statistical Analysis of RRAM Operations using ZnO-based Approach.  In: UNSPECIFIED.