Abstract. An optimized source has the ability to improve the process window during lithography in semiconductor manufacturing. Source optimization is always a key technique to improve printing performance. Conventionally, source optimization relies on mathematical–physical model calibration, which is computationally expensive and extremely time-consuming. Machine learning could learn from existing data, construct a prediction model, and speed up the whole process. We propose the first source optimization process based on autoencoder neural networks. The goal of this autoencoder-based process is to increase the speed of the source optimization process with high-quality imaging results. We also make additional technical efforts to improve the performance of our work, including data augmentation and batch normalization. Experimental results demonstrate that our autoencoder-based source optimization achieves about 105  ×   speed up with 4.67% compromise on depth of focus (DOF), when compared to conventional model-based source optimization method.

中文翻译：

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SoulNet：利用生成神经网络进行高级光刻的超快光源优化

摘要。优化的源能够改善半导体制造中光刻期间的工艺窗口。源优化始终是提高打印性能的关键技术。传统上，源优化依赖于数学物理模型校准，这在计算上是昂贵的并且非常耗时。机器学习可以从现有数据中学习，构建预测模型，加快整个过程。我们提出了第一个基于自动编码器神经网络的源优化过程。这种基于自动编码器的过程的目标是通过高质量的成像结果来提高源优化过程的速度。我们还做出了额外的技术努力来提高我们工作的性能，包括数据增强和批量标准化。