标题： PGR-DRC：使用无监督学习的预全局布线DRC违规预测 显示英文标题

标题： PGR-DRC: Pre-Global Routing DRC Violation Prediction Using Unsupervised Learning

摘要： 利用人工智能（AI）驱动的电子设计和自动化（EDA）工具、高性能计算和并行化算法对于下一代微处理器创新至关重要，确保计算、人工智能和半导体技术的持续进步。 基于机器学习的设计规则检查（DRC）和光刻热点检测可以提高首次流片成功率。 然而，传统的机器学习和神经网络（NN）模型使用监督学习，并需要一个大型平衡数据集（在正类和负类方面），并且需要训练时间。 本研究通过提出首个无监督的DRC违规预测方法来解决这些关键挑战。 所提出的模型可以使用任何不平衡数据集，仅使用一个类别并为其设置一个阈值，然后拟合任何新数据，以检查它们是否在模型边界内进行分类。 本研究通过使用CMOS 28 nm技术以及Synopsys Design Compiler和IC Compiler II工具实现不同的计算核心来验证所提出的模型。 然后，将版图分为虚拟网格，收集约60k数据进行分析和验证。 所提出的方法预测测试准确率为99.95%，而现有的支持向量机（SVM）和神经网络（NN）模型的准确率分别为85.44%和98.74%。 此外，与SVM和NN模型相比，所提出的方法的训练时间分别降低了约26.3倍和高达6003倍。 显示英文摘要

摘要： Leveraging artificial intelligence (AI)-driven electronic design and automation (EDA) tools, high-performance computing, and parallelized algorithms are essential for next-generation microprocessor innovation, ensuring continued progress in computing, AI, and semiconductor technology. Machine learning-based design rule checking (DRC) and lithography hotspot detection can improve first-pass silicon success. However, conventional ML and neural network (NN)-based models use supervised learning and require a large balanced dataset (in terms of positive and negative classes) and training time. This research addresses those key challenges by proposing the first-ever unsupervised DRC violation prediction methodology. The proposed model can be built using any unbalanced dataset using only one class and set a threshold for it, then fitting any new data querying if they are within the boundary of the model for classification. This research verified the proposed model by implementing different computational cores using CMOS 28 nm technology and Synopsys Design Compiler and IC Compiler II tools. Then, layouts were divided into virtual grids to collect about 60k data for analysis and verification. The proposed method has 99.95% prediction test accuracy, while the existing support vector machine (SVM) and neural network (NN) models have 85.44\% and 98.74\% accuracy, respectively. In addition, the proposed methodology has about 26.3x and up to 6003x lower training times compared to SVM and NN-models, respectively.