Soft Voting-Based Ensemble Approach to Predict Early Stage DRC Violations
Soft Voting-Based Ensemble Approach to Predict Early Stage DRC Violations
复制标题
基于软投票的集成方法来预测早期 DRC 违规
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Md Asif Shahjalal
中科院分区：
文献类型：
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作者：
Riadul Islam;Md Asif Shahjalal
减少人力和设计进度时间正面临着来自尖端技术节点的巨大挑战，这阻碍了IC制造的盈利能力。 DARPA IDEA 等计划旨在实现 24 小时设计周转时间、最大限度地提高资源利用率和 IC 生产率，从而应对了这一挑战。在本文中，我们提出了一种鲁棒的集成学习模型，可以从布局阶段预测 DRC，该模型可以精确预测设计的可布线性和设计的 DRC 热点。该算法使用软投票分类器来结合随机森林和梯度增强算法。我们的方法实现了最大精度、召回率和 F1 分数分别为 97%、97% 和 96%，这明显优于最先进的基于支持向量机 (SVM) 的预测方案。
Reducing human effort and design schedule time is facing a tremendous challenge from the cutting- edge technology node, which hinders profitability from IC manufacturing. Initiatives like DARPA IDEA have addressed this challenge by aiming for a 24-hour design turnaround time, maximum resource utilization, and productivity of ICs. In this paper, we proposed a robust ensemble learning model to predict DRC from the placement stage, which precisely predicts design routability and DRC hotspots of a design. The proposed algorithm uses a soft voting classifier to combine random forest and gradient boosting algorithms. Our approach achieved a maximum precision, recall, and F1 score of 97%, 97%, and 96%, respectively, which are significantly better than the state- of-the-art support-vector machine (SVM)-based prediction scheme.