{"title":"标准细胞库符合工艺密度约束的预放置评估","authors":"Digvijay Rajurkar, Sivakumar Venkataraman","doi":"10.1109/MWSCAS47672.2021.9531884","DOIUrl":null,"url":null,"abstract":"Improving design manufacturability and yield requires microchip designs to satisfy process density constraints for pre-defined windows in the die. Iterations in DFM convergence poses a key challenge to physical verification which heavily depends on the compliance of standard cell library to process density requirements. The proposed novel technique involves training a classifier model using supervised reinforced learning to evaluate layer wise density margin as a function of cell area, process window overlaps and usage at block-level thus facilitating ease of use. Process density compliance is analyzed for library offerings on Intel 10nm node demonstrating 85% success in predicting failing cells","PeriodicalId":6792,"journal":{"name":"2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)","volume":"71 1","pages":"236-240"},"PeriodicalIF":0.0000,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Pre-placement evaluation of Standard Cell Library compliance to Process Density constraints\",\"authors\":\"Digvijay Rajurkar, Sivakumar Venkataraman\",\"doi\":\"10.1109/MWSCAS47672.2021.9531884\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Improving design manufacturability and yield requires microchip designs to satisfy process density constraints for pre-defined windows in the die. Iterations in DFM convergence poses a key challenge to physical verification which heavily depends on the compliance of standard cell library to process density requirements. The proposed novel technique involves training a classifier model using supervised reinforced learning to evaluate layer wise density margin as a function of cell area, process window overlaps and usage at block-level thus facilitating ease of use. Process density compliance is analyzed for library offerings on Intel 10nm node demonstrating 85% success in predicting failing cells\",\"PeriodicalId\":6792,\"journal\":{\"name\":\"2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)\",\"volume\":\"71 1\",\"pages\":\"236-240\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-08-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MWSCAS47672.2021.9531884\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MWSCAS47672.2021.9531884","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Pre-placement evaluation of Standard Cell Library compliance to Process Density constraints
Improving design manufacturability and yield requires microchip designs to satisfy process density constraints for pre-defined windows in the die. Iterations in DFM convergence poses a key challenge to physical verification which heavily depends on the compliance of standard cell library to process density requirements. The proposed novel technique involves training a classifier model using supervised reinforced learning to evaluate layer wise density margin as a function of cell area, process window overlaps and usage at block-level thus facilitating ease of use. Process density compliance is analyzed for library offerings on Intel 10nm node demonstrating 85% success in predicting failing cells