{"title":"Comprehensive Design-oriented FDSOI EKV Model","authors":"Hung-Chi Han, Antonio A. D'Amico, C. Enz","doi":"10.23919/mixdes55591.2022.9838014","DOIUrl":null,"url":null,"abstract":"The work presents the comprehensive design-oriented EKV model for FDSOI technologies, including the back-gate effects and geometry dependence. Despite its simplicity, the model correctly captures not only the dependence of the threshold voltage versus the back-gate voltage, but also the changes in the slope factor and low-field mobility. This results in a normalized transconductance efficiency that becomes independent of the back-gate voltage over a wide range. The model is validated thanks to the use of the Python-based automated parameter extraction tool on a 22 nm FDSOI technology.","PeriodicalId":356244,"journal":{"name":"2022 29th International Conference on Mixed Design of Integrated Circuits and System (MIXDES)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 29th International Conference on Mixed Design of Integrated Circuits and System (MIXDES)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/mixdes55591.2022.9838014","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
The work presents the comprehensive design-oriented EKV model for FDSOI technologies, including the back-gate effects and geometry dependence. Despite its simplicity, the model correctly captures not only the dependence of the threshold voltage versus the back-gate voltage, but also the changes in the slope factor and low-field mobility. This results in a normalized transconductance efficiency that becomes independent of the back-gate voltage over a wide range. The model is validated thanks to the use of the Python-based automated parameter extraction tool on a 22 nm FDSOI technology.