当前芯片参数成品率研究主要局限于单一性能指标成品率估算或对多个单性能指标成品率进行均衡优化.针对此类方法易造成参数成品率缺失的问题,本文提出一种基于Copula理论的芯片多元参数成品率估算方法.该方法首先针对漏电功耗及芯片时...当前芯片参数成品率研究主要局限于单一性能指标成品率估算或对多个单性能指标成品率进行均衡优化.针对此类方法易造成参数成品率缺失的问题,本文提出一种基于Copula理论的芯片多元参数成品率估算方法.该方法首先针对漏电功耗及芯片时延性能指标,构建具有随机相关性的漏电功耗及芯片时延模型;然后利用鞍点线抽样方法对漏电功耗及芯片时延的边缘分布概率进行求解;最后根据Copula理论得到准确的芯片多元参数成品率估算结果.仿真结果表明,相较于蒙特卡罗仿真,本文方法具有较高的仿真效率,仿真时间减少了12%以上,而且在不同国际电路与系统研讨会(International Symposium on Circuits and Systems,ISCAS)基准电路下,该方法与蒙特卡罗仿真结果的相对误差均保持在9%以内,能够在任意性能指标约束下,对芯片多元参数成品率进行有效估算,可为芯片设计人员提供同时考虑多个性能指标的参数成品率信息.展开更多
Due to continuous process scaling, process, voltage, and temperature （PVT） parameter variations have become one of the most problematic issues in circuit design. The resulting correlations among performance metrics ...Due to continuous process scaling, process, voltage, and temperature （PVT） parameter variations have become one of the most problematic issues in circuit design. The resulting correlations among performance metrics lead to a significant parametric yield loss. Previous algorithms on parametric yield prediction are limited to predicting a single-parametric yield or performing balanced optimization for several single-parametric yields. Consequently, these methods fail to predict the multiparametric yield that optimizes multiple performance metrics simultaneously, which may result in significant accuracy loss. In this paper we suggest an efficient multi-parametric yield prediction framework, in which multiple performance metrics are considered as simultaneous constraint conditions for parametric yield prediction, to maintain the correlations among metrics. First, the framework models the performance metrics in terms of PVT parameter variations by using the adaptive elastic net （AEN） method. Then the parametric yield for a single performance metric can be predicted through the computation of the cumulative distribution function （CDF） based on the multiplication theorem and the Markov chain Monte Carlo （MCMC） method. Finally, a copula-based parametric yield prediction procedure has been developed to solve the multi-parametric yield prediction problem, and to generate an accurate yield estimate. Experimental results demonstrate that the proposed multi-parametric yield prediction framework is able to provide the designer with either an accurate value for parametric yield under specific performance limits, or a multi-parametric yield surface under all ranges of performance limits.展开更多
文摘当前芯片参数成品率研究主要局限于单一性能指标成品率估算或对多个单性能指标成品率进行均衡优化.针对此类方法易造成参数成品率缺失的问题,本文提出一种基于Copula理论的芯片多元参数成品率估算方法.该方法首先针对漏电功耗及芯片时延性能指标,构建具有随机相关性的漏电功耗及芯片时延模型;然后利用鞍点线抽样方法对漏电功耗及芯片时延的边缘分布概率进行求解;最后根据Copula理论得到准确的芯片多元参数成品率估算结果.仿真结果表明,相较于蒙特卡罗仿真,本文方法具有较高的仿真效率,仿真时间减少了12%以上,而且在不同国际电路与系统研讨会(International Symposium on Circuits and Systems,ISCAS)基准电路下,该方法与蒙特卡罗仿真结果的相对误差均保持在9%以内,能够在任意性能指标约束下,对芯片多元参数成品率进行有效估算,可为芯片设计人员提供同时考虑多个性能指标的参数成品率信息.
基金Project supposed by the Natural Science Foundation of Jiangsu Province （Nos. BK20161072, BK20150785, and BK20130877） and the National Natural Science Foundation of China （Nos. 61502234 and 71301081）
文摘Due to continuous process scaling, process, voltage, and temperature （PVT） parameter variations have become one of the most problematic issues in circuit design. The resulting correlations among performance metrics lead to a significant parametric yield loss. Previous algorithms on parametric yield prediction are limited to predicting a single-parametric yield or performing balanced optimization for several single-parametric yields. Consequently, these methods fail to predict the multiparametric yield that optimizes multiple performance metrics simultaneously, which may result in significant accuracy loss. In this paper we suggest an efficient multi-parametric yield prediction framework, in which multiple performance metrics are considered as simultaneous constraint conditions for parametric yield prediction, to maintain the correlations among metrics. First, the framework models the performance metrics in terms of PVT parameter variations by using the adaptive elastic net （AEN） method. Then the parametric yield for a single performance metric can be predicted through the computation of the cumulative distribution function （CDF） based on the multiplication theorem and the Markov chain Monte Carlo （MCMC） method. Finally, a copula-based parametric yield prediction procedure has been developed to solve the multi-parametric yield prediction problem, and to generate an accurate yield estimate. Experimental results demonstrate that the proposed multi-parametric yield prediction framework is able to provide the designer with either an accurate value for parametric yield under specific performance limits, or a multi-parametric yield surface under all ranges of performance limits.
