Novel End-to-End Production-Ready Machine Learning Flow for Nanolithography Modeling and Correction
This addresses the need for scalable ML solutions in semiconductor manufacturing to reduce runtime and computational power, but it appears incremental as it builds on existing ML approaches.
The study tackled the problem of making machine learning-based computational lithography production-ready by analyzing barriers and presenting a novel end-to-end flow, though no concrete results or numbers are provided.
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.