T. Choi, H. Choi, J.H. Choi, H. Choo, H. Jung, H.Y. Kim, T. Song, J. Kye, S. Jung
{"title":"精确的高西格玛失配模型用于Sub-7nm技术的低功耗设计","authors":"T. Choi, H. Choi, J.H. Choi, H. Choo, H. Jung, H.Y. Kim, T. Song, J. Kye, S. Jung","doi":"10.23919/VLSIT.2019.8776509","DOIUrl":null,"url":null,"abstract":"High-sigma yield simulation analysis based on accurate SPICE mismatch model is required for high volume product design. Especially for the low power design in sub-7nm technology, the non-Gaussian behavior of the transistor drain currents $(I_{\\text{ds}})$ is intensifying due to large mismatch variation. To achieve reliable high-sigma simulation, SPICE mismatch model needs to accurately reflect the non-Gaussian $I_{\\text{ds}}$ distribution obtained from the silicon data. Gaussian distribution modeling of channel resistance factor $(R_{\\text{ch}_{-}\\text{f}})$ and source/drain external resistance $(R_{\\text{ext}})$ is proven to be effective to model the skewed Gaussian distribution shape of massive silicon Ids data.","PeriodicalId":6752,"journal":{"name":"2019 Symposium on VLSI Technology","volume":"9 1","pages":"T106-T107"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Accurate High-Sigma Mismatch Model for Low Power Design in Sub-7nm Technology\",\"authors\":\"T. Choi, H. Choi, J.H. Choi, H. Choo, H. Jung, H.Y. Kim, T. Song, J. Kye, S. Jung\",\"doi\":\"10.23919/VLSIT.2019.8776509\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"High-sigma yield simulation analysis based on accurate SPICE mismatch model is required for high volume product design. Especially for the low power design in sub-7nm technology, the non-Gaussian behavior of the transistor drain currents $(I_{\\\\text{ds}})$ is intensifying due to large mismatch variation. To achieve reliable high-sigma simulation, SPICE mismatch model needs to accurately reflect the non-Gaussian $I_{\\\\text{ds}}$ distribution obtained from the silicon data. Gaussian distribution modeling of channel resistance factor $(R_{\\\\text{ch}_{-}\\\\text{f}})$ and source/drain external resistance $(R_{\\\\text{ext}})$ is proven to be effective to model the skewed Gaussian distribution shape of massive silicon Ids data.\",\"PeriodicalId\":6752,\"journal\":{\"name\":\"2019 Symposium on VLSI Technology\",\"volume\":\"9 1\",\"pages\":\"T106-T107\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 Symposium on VLSI Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/VLSIT.2019.8776509\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 Symposium on VLSI Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/VLSIT.2019.8776509","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Accurate High-Sigma Mismatch Model for Low Power Design in Sub-7nm Technology
High-sigma yield simulation analysis based on accurate SPICE mismatch model is required for high volume product design. Especially for the low power design in sub-7nm technology, the non-Gaussian behavior of the transistor drain currents $(I_{\text{ds}})$ is intensifying due to large mismatch variation. To achieve reliable high-sigma simulation, SPICE mismatch model needs to accurately reflect the non-Gaussian $I_{\text{ds}}$ distribution obtained from the silicon data. Gaussian distribution modeling of channel resistance factor $(R_{\text{ch}_{-}\text{f}})$ and source/drain external resistance $(R_{\text{ext}})$ is proven to be effective to model the skewed Gaussian distribution shape of massive silicon Ids data.