Bilinear Lithography Hotspot Detection

Hang Zhang, Fengyuan Zhu, Haocheng Li, Evangeline F. Y. Young, Bei Yu
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引用次数: 23

Abstract

Advanced semiconductor process technologies are producing various circuit layout patterns, and it is essential to detect and eliminate problematic ones, which are called lithography hotspots. These hotspots are formed due to light diffraction and interference, which induces complex intrinsic structures within the formation process. Though various machine learning based methods have been proposed for this problem, most of them cannot capture the intrinsic structure of each data. In this paper, we propose a novel feature extraction by representing each data sample in matrix form. We argue that this method can well preserve the intrinsic feature of each sample, leading to better performance.We then further propose a bilinear lithography hotspot detector, which can tackle data in matrix form directly to preserve the hidden structural correlations in the lithography process. Experimental results show that the proposed method outperforms state-of-the-art ones with remarkably large margin in both false alarms and runtime, with 98.16% detection accuracy.
双线性光刻热点检测
先进的半导体工艺技术正在产生各种各样的电路布局,检测和消除有问题的电路布局是至关重要的,这些电路布局被称为光刻热点。这些热点是由于光的衍射和干涉而形成的,在形成过程中产生了复杂的本征结构。尽管针对该问题提出了各种基于机器学习的方法,但大多数方法都无法捕获每个数据的内在结构。在本文中,我们提出了一种新的特征提取方法,即用矩阵形式表示每个数据样本。我们认为这种方法可以很好地保留每个样本的固有特征,从而获得更好的性能。然后,我们进一步提出了一种双线性光刻热点检测器,它可以直接处理矩阵形式的数据,以保持光刻过程中隐藏的结构相关性。实验结果表明,该方法在虚警和运行时间上均优于现有方法,检测准确率为98.16%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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