Optical lithography is the main enabler to semiconductor manufacturing. It requires extensive processing to perform the Resolution Enhancement Techniques (RETs) required to transfer the design data to a working Integrated Circuits (ICs). The processing power and computational runtime for RETs tasks is ever increasing due to the continuous reduction of the feature size and the expansion of the chip area. State-of-the-art research sought Machine Learning (ML) technologies to reduce runtime and computational power, however they are still not used in production yet. In this study, we analyze the reasons holding back ML computational lithography from being production ready and present a novel highly scalable end-to-end flow that enables production ready ML-RET correction.

翻译：光学光刻是半导体制造的主要推动技术。它需要大量处理过程来执行分辨率增强技术（RETs），从而将设计数据转化为可工作的集成电路（ICs）。由于特征尺寸的持续减小和芯片面积的扩大，RET任务所需的处理能力和计算运行时不断增长。前沿研究致力于采用机器学习（ML）技术来降低运行时与计算功耗，但这些技术尚未应用于生产工艺中。本研究分析了阻碍ML计算光刻达到生产就绪状态的原因，并提出了一种高度可扩展的端到端流程，该流程能够实现面向生产的ML-RET修正。