Nanlin Guo, Fulin Peng, Jiahe Shi, Fan Yang, Jun Tao, Xuan Zeng
{"title":"基于贝叶斯神经网络的多拐角模拟电路成品率优化:提高环境变化下电路的可靠性","authors":"Nanlin Guo, Fulin Peng, Jiahe Shi, Fan Yang, Jun Tao, Xuan Zeng","doi":"10.1145/3626321","DOIUrl":null,"url":null,"abstract":"The reliability of circuits is significantly affected by process variations in manufacturing and environmental variation during operation. Current yield optimization algorithms take process variations into consideration to improve circuit reliability. However, the influence of environmental variations (e.g., voltage and temperature variations) is often ignored in current methods because of the high computational cost. In this paper, a novel and efficient approach named BNN-BYO is proposed to optimize the yield of analog circuits in multiple environmental corners. First, we use a Bayesian Neural Network (BNN) to simultaneously model the yields and POIs in multiple corners efficiently. Next, the multi-corner yield optimization can be performed by embedding BNN into Bayesian optimization framework. Since the correlation among yields and POIs in different corners is implicitly encoded in the BNN model, it provides great modeling capabilities for yields and their uncertainties to improve the efficiency of yield optimization. Our experimental results demonstrate that the proposed method can save up to 45.3% of simulation cost compared to other baseline methods to achieve the same target yield. In addition, for the same simulation cost, our proposed method can find better design points with 3.2% yield improvement.","PeriodicalId":50944,"journal":{"name":"ACM Transactions on Design Automation of Electronic Systems","volume":null,"pages":null},"PeriodicalIF":2.2000,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Yield Optimization for Analog Circuits over Multiple Corners via Bayesian Neural Network: Enhancing Circuit Reliability under Environmental Variation\",\"authors\":\"Nanlin Guo, Fulin Peng, Jiahe Shi, Fan Yang, Jun Tao, Xuan Zeng\",\"doi\":\"10.1145/3626321\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The reliability of circuits is significantly affected by process variations in manufacturing and environmental variation during operation. Current yield optimization algorithms take process variations into consideration to improve circuit reliability. However, the influence of environmental variations (e.g., voltage and temperature variations) is often ignored in current methods because of the high computational cost. In this paper, a novel and efficient approach named BNN-BYO is proposed to optimize the yield of analog circuits in multiple environmental corners. First, we use a Bayesian Neural Network (BNN) to simultaneously model the yields and POIs in multiple corners efficiently. Next, the multi-corner yield optimization can be performed by embedding BNN into Bayesian optimization framework. Since the correlation among yields and POIs in different corners is implicitly encoded in the BNN model, it provides great modeling capabilities for yields and their uncertainties to improve the efficiency of yield optimization. Our experimental results demonstrate that the proposed method can save up to 45.3% of simulation cost compared to other baseline methods to achieve the same target yield. In addition, for the same simulation cost, our proposed method can find better design points with 3.2% yield improvement.\",\"PeriodicalId\":50944,\"journal\":{\"name\":\"ACM Transactions on Design Automation of Electronic Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2023-10-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM Transactions on Design Automation of Electronic Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3626321\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Design Automation of Electronic Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3626321","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
Yield Optimization for Analog Circuits over Multiple Corners via Bayesian Neural Network: Enhancing Circuit Reliability under Environmental Variation
The reliability of circuits is significantly affected by process variations in manufacturing and environmental variation during operation. Current yield optimization algorithms take process variations into consideration to improve circuit reliability. However, the influence of environmental variations (e.g., voltage and temperature variations) is often ignored in current methods because of the high computational cost. In this paper, a novel and efficient approach named BNN-BYO is proposed to optimize the yield of analog circuits in multiple environmental corners. First, we use a Bayesian Neural Network (BNN) to simultaneously model the yields and POIs in multiple corners efficiently. Next, the multi-corner yield optimization can be performed by embedding BNN into Bayesian optimization framework. Since the correlation among yields and POIs in different corners is implicitly encoded in the BNN model, it provides great modeling capabilities for yields and their uncertainties to improve the efficiency of yield optimization. Our experimental results demonstrate that the proposed method can save up to 45.3% of simulation cost compared to other baseline methods to achieve the same target yield. In addition, for the same simulation cost, our proposed method can find better design points with 3.2% yield improvement.
期刊介绍:
TODAES is a premier ACM journal in design and automation of electronic systems. It publishes innovative work documenting significant research and development advances on the specification, design, analysis, simulation, testing, and evaluation of electronic systems, emphasizing a computer science/engineering orientation. Both theoretical analysis and practical solutions are welcome.