Novel End-to-End Production-Ready Machine Learning Flow for
  Nanolithography Modeling and Correction
Mohamed S. E. Habib Hossam A. H. Fahmy Mohamed F. Abu-ElYazeed
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.