Shan Deng, Guodong Yin, W. Chakraborty, S. Dutta, S. Datta, Xueqing Li, K. Ni
{"title":"铁电场效应管捕获关键行为的综合模型:可扩展性、变化性、随机性和累积性","authors":"Shan Deng, Guodong Yin, W. Chakraborty, S. Dutta, S. Datta, Xueqing Li, K. Ni","doi":"10.1109/VLSITechnology18217.2020.9265014","DOIUrl":null,"url":null,"abstract":"In this work, we developed a comprehensive model for ferroelectric FET (FeFET), which can capture all the essential ferroelectric behaviors. Unlike previous models, which can describe only a subset but not all the reported ferroelectric behaviors, the proposed model can: i) predict device performance with geometry scaling; ii) quantify the device-to-device variation with device scaling; iii) exhibit stochasticity during a single domain switching; and iv) capture the accumulation of domain switching probability with applied pulse trains. This comprehensive model would enable researchers to explore a wide range of FeFET applications and guide device development, optimization and benchmarking.","PeriodicalId":6850,"journal":{"name":"2020 IEEE Symposium on VLSI Technology","volume":"23 5 1","pages":"1-2"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"47","resultStr":"{\"title\":\"A Comprehensive Model for Ferroelectric FET Capturing the Key Behaviors: Scalability, Variation, Stochasticity, and Accumulation\",\"authors\":\"Shan Deng, Guodong Yin, W. Chakraborty, S. Dutta, S. Datta, Xueqing Li, K. Ni\",\"doi\":\"10.1109/VLSITechnology18217.2020.9265014\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this work, we developed a comprehensive model for ferroelectric FET (FeFET), which can capture all the essential ferroelectric behaviors. Unlike previous models, which can describe only a subset but not all the reported ferroelectric behaviors, the proposed model can: i) predict device performance with geometry scaling; ii) quantify the device-to-device variation with device scaling; iii) exhibit stochasticity during a single domain switching; and iv) capture the accumulation of domain switching probability with applied pulse trains. This comprehensive model would enable researchers to explore a wide range of FeFET applications and guide device development, optimization and benchmarking.\",\"PeriodicalId\":6850,\"journal\":{\"name\":\"2020 IEEE Symposium on VLSI Technology\",\"volume\":\"23 5 1\",\"pages\":\"1-2\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"47\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE Symposium on VLSI Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/VLSITechnology18217.2020.9265014\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE Symposium on VLSI Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VLSITechnology18217.2020.9265014","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Comprehensive Model for Ferroelectric FET Capturing the Key Behaviors: Scalability, Variation, Stochasticity, and Accumulation
In this work, we developed a comprehensive model for ferroelectric FET (FeFET), which can capture all the essential ferroelectric behaviors. Unlike previous models, which can describe only a subset but not all the reported ferroelectric behaviors, the proposed model can: i) predict device performance with geometry scaling; ii) quantify the device-to-device variation with device scaling; iii) exhibit stochasticity during a single domain switching; and iv) capture the accumulation of domain switching probability with applied pulse trains. This comprehensive model would enable researchers to explore a wide range of FeFET applications and guide device development, optimization and benchmarking.