未知工艺角下时序违反的机器学习预测

Machine learning prediction of timing violation under unknown corners

集成电路设计复杂性的增长以及工艺尺寸的持续缩减给静态时序分析以及设计周期带来了新的严峻挑战。为了提升静态时序分析效率、缩短设计周期,充分考虑FinFET工艺特性以及静态时序分析原理,提出了未知工艺角下时序违反的机器学习预测方法,实现了基于部分工艺角的时序特性来预测另外一部分工艺角的时序特性的目标。基于某工业设计进行实验,结果表明,提出的方法利用5个工艺角时序预测另外31个工艺角时序,可达到小于2 ps的平均绝对误差,远远优于传统方法所需的21个工艺角,显著改善了预测精度和减少了静态时序分析工作量。

The increase of IC design complexity and the continuous reduction of process feature size bring new severe challenges to static timing analysis(STA)and chip design cycle.In order to improve the efficiency of STA and shorten the chip design cycle,this paper fully considers the FinFET process characteristics and the principle of STA,and predicts the timing characteristics of another part of corners by introducing machine learning methods based on the timing characteristics of some corners.The experiment is based on an industrial design,and the results show that the proposed method uses 5 corners to predict the timing of other 31 corners,which can achieve an average absolute error of less than 2 ps,far better than the 21 process angles required by traditional methods.Thus,the proposed method significantly improves the prediction accuracy and significantly reduces the workload of static time series analysis.