SoulNet:利用生成神经网络对先进光刻技术进行超快光源优化

IF 1.5 2区 物理与天体物理 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Ying Chen, Yibo Lin, Lisong Dong, Tianyang Gai, Rui Chen, Yajuan Su, Yayi Wei, D. Pan
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引用次数: 2

摘要

摘要优化后的光源能够改善半导体制造中光刻的工艺窗口。源优化一直是提高打印性能的关键技术。传统的源优化依赖于数学-物理模型校准,这在计算上是昂贵的,而且非常耗时。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提高源优化过程的速度和高质量的成像结果。我们还做了额外的技术工作来改进我们的工作性能,包括数据增强和批处理规范化。实验结果表明,与传统的基于模型的源优化方法相比,基于自编码器的源优化速度提高了约105倍,焦距(DOF)降低了4.67%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SoulNet: ultrafast optical source optimization utilizing generative neural networks for advanced lithography
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.
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来源期刊
CiteScore
3.40
自引率
30.40%
发文量
0
审稿时长
6-12 weeks
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