电子学报 ›› 2017, Vol. 45 ›› Issue (12): 2917-2924.DOI: 10.3969/j.issn.0372-2112.2017.12.013

• 学术论文 • 上一篇    下一篇

基于弹性网稀疏表示的芯片参数成品率估算方法

李鑫1,2,3, 孙晋4, 肖甫3   

  1. 1. 南京邮电大学物联网学院, 江苏南京 210003;
    2. 江苏省安全生产科学研究院科技研发中心, 江苏南京 210042;
    3. 南京邮电大学江苏省无线传感网高技术研究重点实验室, 江苏南京 210013;
    4. 南京理工大学计算机科学与工程学院, 江苏南京 210094
  • 收稿日期:2016-09-05 修回日期:2017-01-16 出版日期:2017-12-25 发布日期:2017-12-25
  • 作者简介:李鑫,男,1983年2月出生于江苏省徐州市.2009年毕业于南京理工大学计算机学院.现为南京邮电大学物联网学院副教授,从事VLSI计算机辅助设计与芯片可靠性估计及优化方面的研究工作.E-mail:lin65002@hotmail.com;孙晋,男,1983年5月出生于江苏省淮安市.2011年毕业于亚利桑那大学电子与计算机工程系.现为南京理工大学计算机科学与工程学院副教授.从事集成芯片鲁棒性设计与多核片上网络低功耗设计方面的研究工作.E-mail:sunj@njust.edu.cn
  • 基金资助:
    国家自然科学基金(No.61502234,No.71301081);江苏省自然科学基金(No.Bk20161072,No.Bk20150785,BK20130877);国家博士后基金(No.2014M551637);江苏省博士后基金(No.1401046C);复杂产品智能制造系统技术国家重点实验室开放基金(No.QYYE1603)

An Efficient Estimation Method for Chip-Level Parametric Yield Based on Elastic Net Sparse Representation

LI Xin1,2,3, SUN Jin4, XIAO Fu3   

  1. 1. School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu 210003, China;
    2. Technology Innovation Center, Jiangsu Acadamy of Safety Science and Technology, Nanjing, Jiangsu 210042, China;
    3. Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu 210013, China;
    4. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, China
  • Received:2016-09-05 Revised:2017-01-16 Online:2017-12-25 Published:2017-12-25

摘要: 当前集成电路芯片参数成品率估算通常预设大量扰动基函数进行芯片性能模型构建,易造成成品率估算方法复杂度过高.而若随意减少扰动基函数数量,则极易造成成品率估算精度缺失.针对此问题,本文提出一种芯片参数成品率稀疏估算方法.该方法首先根据工艺参数扰动建立具有随机不确定性的漏电功耗模型;然后按照关键度高低,利用弹性网自适应选取关键扰动基函数对漏电功耗模型进行稀疏表示建模;最后,利用贝叶斯理论及马尔科夫链方法对漏电功耗成品率进行估算.实验结果表明,该方法不仅可以使所构建的漏电功耗模型具有一般性和稀疏性优点,而且能够对漏电功耗成品率进行准确估算,与蒙特卡罗仿真结果相比估算误差不超过5%.同时,相较于蒙特卡罗采样,该方法还可以大幅减少算法仿真时间,具有更好的仿真效率.

关键词: 参数成品率估算, 稀疏表示, 弹性网, 马尔科夫链, 鞍点估计

Abstract: Previous approaches on integrated circuit parametric yield estimation usually model chip performance by pre-setting variation basis functions.It is easy to result in high complexity.On the other hand,random reduction of the number of the basis functions may result in accuracy loss.In order to avoid the issues,a sparse estimation approach for chip-level parametric yield is proposed.Taking power yield as an instance,the proposed approach models leakage power stochastically.Then according to the importance level,several key basis functions are adaptively selected to constribute a sparse leakage power model based on elastic net.Finally according to Bias theory and Markov chain method,the power yield is estimated efficiently.Experimental results show that the proposed approach not only makes the established power model general and sparse,but estimates the power yield accurately.Comparing to Monte Carlo(MC)simulation,the relative errors of power yield estimation based on proposed method are less than 5%.In addition,this approach can lead to a large cost reduction compared with MC simulation,and thus has higher efficiency.

Key words: parametric yield estimation, sparse representation, elastic net, Markov chain, saddle point estimation

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