Design hotspots are layout patterns which may cause defects due to complex design and process interactions. Several machine learning and pattern matching-based methods have been proposed to identify and correct them early during design stages. However, almost all of them suffer from high false-alarm rates, mainly because they are oblivious to the root causes of hotspots. In this work, we seek to address this limitation by using a novel database enhancement approach through synthetic pattern generation based on a carefully crafted design of experiments. We evaluate the effectiveness of the proposed method using industry-standard tools and designs and demonstrate more than $3\times $ reduction in classification error in comparison to the state-of-the-art.

中文翻译：

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基于综合模式的数据库增强改进热点检测

设计热点是由于复杂的设计和流程交互而可能导致缺陷的布局模式。已经提出了几种基于机器学习和模式匹配的方法来在设计阶段的早期识别和纠正它们。然而，几乎所有人都遭受高误报率的困扰，主要是因为他们忘记了热点的根本原因。在这项工作中，我们试图通过基于精心设计的实验设计生成合成模式，使用一种新的数据库增强方法来解决这一限制。我们使用行业标准工具和设计评估所提出方法的有效性，并展示了超过 $3\times $ 与最先进的技术相比，减少了分类错误。