Hidetoshi Matsuoka, Hiroshi Ikeda, H. Higuchi, Yoshinori Tomita
{"title":"An accurate modeling method utilizing application-specific statistical information and its application to SRAM yield estimation","authors":"Hidetoshi Matsuoka, Hiroshi Ikeda, H. Higuchi, Yoshinori Tomita","doi":"10.1109/ISQED.2010.5450411","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a new model construction method utilizing application specific physical information and present its application to SRAM yield calculation. The physical information is extracted as statistical distributions from past simulation results automatically. Experimental results show our method achieves 700x speed up over non modeling method and more than 10x speed up over the conventional modeling method. It requires only 5.3 samples to model a fifth order full cross term polynomial with 21 coefficients and is free from over-fitting and singular matrix problem. This modeling method can be a general approach to create models with application specific physical information.","PeriodicalId":369046,"journal":{"name":"2010 11th International Symposium on Quality Electronic Design (ISQED)","volume":"771 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 11th International Symposium on Quality Electronic Design (ISQED)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISQED.2010.5450411","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In this paper, we propose a new model construction method utilizing application specific physical information and present its application to SRAM yield calculation. The physical information is extracted as statistical distributions from past simulation results automatically. Experimental results show our method achieves 700x speed up over non modeling method and more than 10x speed up over the conventional modeling method. It requires only 5.3 samples to model a fifth order full cross term polynomial with 21 coefficients and is free from over-fitting and singular matrix problem. This modeling method can be a general approach to create models with application specific physical information.