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首页> 外文期刊>Computer-Aided Design of Integrated Circuits and Systems, IEEE Transactions on >High Performance Lithography Hotspot Detection With Successively Refined Pattern Identifications and Machine Learning
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High Performance Lithography Hotspot Detection With Successively Refined Pattern Identifications and Machine Learning

机译：高性能光刻热点检测，具有连续改进的模式识别和机器学习功能

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摘要

Under the real and evolving manufacturing conditions, lithography hotspot detection faces many challenges. First, real hotspots become hard to identify at early design stages and hard to fix at post-layout stages. Second, false alarms must be kept low to avoid excessive and expensive post-processing hotspot removal. Third, full chip physical verification and optimization require very fast turn-around time. Last but not least, rapid technology advancement favors generic hotspot detection methodologies to avoid exhaustive pattern enumeration and excessive development/update as technology evolves. To address the above issues, we propose a high performance hotspot detection methodology consisting of: 1) a fast layout analyzer; 2) powerful hotspot pattern identifiers; and 3) a generic and efficient flow with successive performance refinements. We implement our algorithms with industry-strength engine under real manufacturing conditions and show that it significantly outperforms state-of-the-art algorithms in false alarms (2.4X to 2300X reduction) and runtime (5X to 237X reduction), meanwhile achieving similar or better hotspot accuracies. Compared with pattern matching, our method achieves higher prediction accuracy for hotspots that are not previously characterized, therefore, more detection generality when exhaustive pattern enumeration is too expensive to perform a priori. Such high performance hotspot detection is especially suitable for lithography-friendly physical design.
机译：在实际和不断发展的制造条件下，光刻热点检测面临许多挑战。首先，真正的热点在设计的早期阶段就很难识别，而在布局后的阶段就很难修复。其次，必须将错误警报保持在较低水平，以避免过多且昂贵的后处理热点移除。第三，全芯片物理验证和优化需要非常快的周转时间。最后但并非最不重要的一点是，快速的技术进步倾向于使用通用的热点检测方法，以避免随着技术的发展而进行详尽的模式枚举和过度的开发/更新。为了解决上述问题，我们提出了一种高性能的热点检测方法，包括：1）快速布局分析仪； 2）强大的热点模式标识符；和3）具有连续性能改进的通用高效流程。我们在真正的制造条件下使用具有行业实力的引擎来实现我们的算法，并证明它在误报（减少2.4倍至2300倍）和运行时间（减少5倍至237倍）方面明显优于最新算法。更好的热点准确性。与模式匹配相比，我们的方法对以前没有特征的热点实现了更高的预测精度，因此，当穷举模式枚举过于昂贵而无法进行先验时，检测的通用性更高。这种高性能的热点检测尤其适合于光刻友好的物理设计。

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