Machine learning-based lithography hotspot detection has been deeply studied recently, from varies feature extraction techniques to efficient learning models. It has been observed that such machine learning-based frameworks are providing satisfactory metal layer hotspot prediction results on known public metal layer benchmarks. In this work, we seek to evaluate how these machine learning-based hotspot detectors generalize to complicated patterns. We first introduce a automatic layout generation tool that can synthesize varies layout patterns given a set of design rules. The tool currently supports both metal layer and via layer generation. As a case study, we conduct hotspot detection on the generated via layer layouts with representative machine learning-based hotspot detectors, which shows that continuous study on model robustness and generality is necessary to prototype and integrate the learning engines in DFM flows. The source code of the layout generation tool will be available at https://github. com/phdyang007/layout-generation.
翻译:最近深入研究了机器学习的平面热点探测方法,从不同的地物提取技术到有效的学习模式,发现这种机器学习框架正在以已知的公共金属层基准提供令人满意的金属层热点预测结果。在这项工作中,我们力求评估这些机器学习的热点探测器如何概括到复杂的模式。我们首先引入一个自动布局生成工具,根据一套设计规则,可以综合不同的布局模式。该工具目前既支持金属层,也支持通过层生成。作为案例研究,我们用具有代表性的机器学习的热点探测器对通过层布局产生的热点探测,这表明对模型坚固性和一般性进行持续研究对于在DFM流中的学习引擎进行原型和整合是必要的。布局生成工具的源代码将在https://github.com/phdyang007/layout生成中提供。