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A yield-enhanced global optimization methodology for analog circuit based on extreme value theory

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Abstract

The challenge in variation-aware circuit optimization with consideration of yield is the trade-off between optimized performance, yield and optimization runtime. This paper presents a practical variationaware circuit global optimization framework named GOYE, which shows the advantages on performance, yield and runtime. It uses an approach called constraint violation elimination (CVE) in global search phase to prune initial starting points and uses the gradient-based method in local search to locate optimum. The worst-case analysis (WCA), which is necessary for variation-aware circuit optimization, is nested in the local optimization process. The efficiency is significantly improved by a novel method based on extreme value theory (EVT). Our EVT-based method is also the first one that allows users to control the target yield such that under-design or over-design can be avoided. A design example in TSMC 65 nm technology is illustrated in the paper where all performance achieves three-sigma yield with consideration of environmental and inter-die/intra-die process variations.

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Authors and Affiliations

  1. Department of Electrical Engineering, The University of Texas at Dallas, Richardson, TX, 75080, USA

    Minghua Li, Liuxi Qian & Dian Zhou

  2. Synopsys, Inc., Mountain View, CA, 94043, USA

    Guanming Huang

  3. School of Electronic and Information Engineering, Anhui University, Hefei, 230601, China

    Xiulong Wu

  4. State Key Laboratory of ASIC & System, Department of Microelectronics, Fudan University, Shanghai, 200433, China

    Xuan Zeng & Dian Zhou

Authors
  1. Minghua Li
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  2. Guanming Huang
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  3. Xiulong Wu
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  4. Liuxi Qian
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  5. Xuan Zeng
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  6. Dian Zhou
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Correspondence to Dian Zhou.

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Li, M., Huang, G., Wu, X. et al. A yield-enhanced global optimization methodology for analog circuit based on extreme value theory. Sci. China Inf. Sci. 59, 082401 (2016). https://doi.org/10.1007/s11432-015-0471-4

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  • DOI: https://doi.org/10.1007/s11432-015-0471-4

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