research-article

Efficient bayesian yield analysis and optimization with active learning

Authors:

Wei W. XingAuthors Info & Claims

DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference

Pages 1195 - 1200

https://doi.org/10.1145/3489517.3530607

Published: 23 August 2022 Publication History

Abstract

Yield optimization for circuit design is computationally intensive due to the expensive yield estimation based on Monte Carlo methods and the difficult optimization process. In this work, a uniform framework to solve these problems simultaneously is proposed. Firstly, a novel efficient Bayesian yield analysis framework, BYA, is proposed by deriving a Bayesian estimation for the yield and introducing active learning based on reductions of integral entropy. A tractable convolutional entropy infill technique is then proposed to efficiently solve the entropy reduction problem. Lastly, we extend BYA for yield optimization by transforming knowledge across the design space and variational space. Experimental results based on SRAM and adder circuits show that BYA is 410x faster (in terms of the number of simulations) than standard MC and averagely 10x (up to 10000x) more accurate than the state-of-the-art method for yield estimation, and is about 5x faster than the SOTA yield optimization methods.

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Cited By

Dou ZCheng MJia MWang PDe V(2024)BNN-YEO: an efficient Bayesian Neural Network for yield estimation and optimizationProceedings of the 61st ACM/IEEE Design Automation Conference10.1145/3649329.3658242(1-6)Online publication date: 23-Jun-2024
https://dl.acm.org/doi/10.1145/3649329.3658242
Xing WLiu YFan WHe LDe V(2024)Every Failure Is A Lesson: Utilizing All Failure Samples To Deliver Tuning-Free Efficient Yield EvaluationProceedings of the 61st ACM/IEEE Design Automation Conference10.1145/3649329.3657381(1-6)Online publication date: 23-Jun-2024
https://dl.acm.org/doi/10.1145/3649329.3657381
Liu YXing WKim T(2024)CIS: Conditional Importance Sampling for Yield Optimization of Analog and SRAM CircuitsProceedings of the 29th Asia and South Pacific Design Automation Conference10.1109/ASP-DAC58780.2024.10473819(386-391)Online publication date: 22-Jan-2024
https://dl.acm.org/doi/10.1109/ASP-DAC58780.2024.10473819
Show More Cited By

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cover image ACM Conferences

DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference

July 2022

1462 pages

ISBN:9781450391429

DOI:10.1145/3489517

General Chair:
Rob Oshana
NXP

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGDA: ACM Special Interest Group on Design Automation
IEEE CEDA

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New York, NY, United States

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Published: 23 August 2022

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NSFC
Beijing Nova Program from Beijing Municipal Science and Technology Commission

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DAC '22

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SIGDA

DAC '22: 59th ACM/IEEE Design Automation Conference

July 10 - 14, 2022

California, San Francisco

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Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

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DAC '25

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62nd ACM/IEEE Design Automation Conference

June 22 - 26, 2025

San Francisco , CA , USA

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Cited By

Dou ZCheng MJia MWang PDe V(2024)BNN-YEO: an efficient Bayesian Neural Network for yield estimation and optimizationProceedings of the 61st ACM/IEEE Design Automation Conference10.1145/3649329.3658242(1-6)Online publication date: 23-Jun-2024
https://dl.acm.org/doi/10.1145/3649329.3658242
Xing WLiu YFan WHe LDe V(2024)Every Failure Is A Lesson: Utilizing All Failure Samples To Deliver Tuning-Free Efficient Yield EvaluationProceedings of the 61st ACM/IEEE Design Automation Conference10.1145/3649329.3657381(1-6)Online publication date: 23-Jun-2024
https://dl.acm.org/doi/10.1145/3649329.3657381
Liu YXing WKim T(2024)CIS: Conditional Importance Sampling for Yield Optimization of Analog and SRAM CircuitsProceedings of the 29th Asia and South Pacific Design Automation Conference10.1109/ASP-DAC58780.2024.10473819(386-391)Online publication date: 22-Jan-2024
https://dl.acm.org/doi/10.1109/ASP-DAC58780.2024.10473819
Liu YDai GCheng YKang WXing W(2023)OPT: Optimal Proposal Transfer for Efficient Yield Optimization for Analog and SRAM Circuits2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD)10.1109/ICCAD57390.2023.10323689(1-9)Online publication date: 28-Oct-2023
https://doi.org/10.1109/ICCAD57390.2023.10323689
Liu YDai GXing W(2023)Seeking the Yield Barrier: High-Dimensional SRAM Evaluation Through Optimal Manifold2023 60th ACM/IEEE Design Automation Conference (DAC)10.1109/DAC56929.2023.10247952(1-6)Online publication date: 9-Jul-2023
https://doi.org/10.1109/DAC56929.2023.10247952

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