G. Ferrari, A. Ghetti, D. Ielmini, A. Redaelli, A. Pirovano
{"title":"Multiphysics modeling of PCM devices for scaling investigation","authors":"G. Ferrari, A. Ghetti, D. Ielmini, A. Redaelli, A. Pirovano","doi":"10.1109/SISPAD.2010.5604509","DOIUrl":null,"url":null,"abstract":"A multiphysics model for Phase Change Memory (PCM) is calibrated on a large set of experimental data. Critical material and interface properties such as electrical and thermal resistivities and their dependence on temperature are extracted from data or fitting electrical characteristics with numerical simulations. The model is shown to match with a unique set of parameters experimental data from 90nm and 45nm technology nodes. The calibrated model is then exploited to perform a sensitivity analysis of key cell characteristics to geometry and material properties variations. Furthermore, the model is used to predict performance of a scaled down cell suitable for the 32nm technology node and the results demonstrate the consistent scalability of PCM with respect to the technology node.","PeriodicalId":331098,"journal":{"name":"2010 International Conference on Simulation of Semiconductor Processes and Devices","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 International Conference on Simulation of Semiconductor Processes and Devices","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SISPAD.2010.5604509","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11
Abstract
A multiphysics model for Phase Change Memory (PCM) is calibrated on a large set of experimental data. Critical material and interface properties such as electrical and thermal resistivities and their dependence on temperature are extracted from data or fitting electrical characteristics with numerical simulations. The model is shown to match with a unique set of parameters experimental data from 90nm and 45nm technology nodes. The calibrated model is then exploited to perform a sensitivity analysis of key cell characteristics to geometry and material properties variations. Furthermore, the model is used to predict performance of a scaled down cell suitable for the 32nm technology node and the results demonstrate the consistent scalability of PCM with respect to the technology node.