A set of comprehensive test patterns is important for a number of lithography applications. Pattern diversity is, however, hard to achieve either from parametric patterns or from actual patterns even though they are carefully extracted and classified. Automatic layout pattern synthesis is proposed in this paper. A generative adversarial network (GAN) is employed to generate a new set of discrete cosine transform (DCT) signals. It is converted to an image format through inverse DCT (IDCT). The image is blurred since output DCT signals from GAN correspond to lower frequency region. Another GAN, this time a conditional GAN (cGAN), is introduced to get sharpened layout pattern. A key in this process is to train the two GANs in such a way that generated patterns are different from existing actual patterns while they are still valid layouts. Experiments indicate that synthetic patterns are less redundant and cover 76% more space in image parameter set space than actual patterns. We choose a machine-learning guided OPC as an example application: when synthetic patterns are included to train OPC model, edge proximity error decreases by 21%. Resist model calibration is chosen as a second example: when synthetic patterns are combined with parametric- and real-patterns, CD RMSE decreases by 10.3%.

中文翻译：

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用于光刻优化的布局图案合成

一组全面的测试模式对于许多光刻应用很重要。然而，模式多样性很难从参数模式或实际模式中实现，即使它们被仔细提取和分类。本文提出了自动布局模式合成。生成对抗网络 (GAN) 用于生成一组新的离散余弦变换 (DCT) 信号。它通过逆 DCT (IDCT) 转换为图像格式。由于来自 GAN 的输出 DCT 信号对应于较低频率区域，因此图像模糊。另一个 GAN，这次是条件 GAN (cGAN)，被引入以获得锐化的布局模式。这个过程的一个关键是以这样一种方式训练两个 GAN，即生成的模式与现有的实际模式不同，但它们仍然是有效的布局。实验表明，合成模式的冗余更少，并且在图像参数集空间中比实际模式多覆盖 76% 的空间。我们选择机器学习引导的 OPC 作为示例应用：当包含合成模式来训练 OPC 模型时，边缘邻近误差降低了 21%。选择抗蚀剂模型校准作为第二个示例：当合成图案与参数和真实图案相结合时，CD RMSE 降低了 10.3%。