Mohamed S. E. Habib, Hossam Fahmy and Mohamed Abu-ElYazeed
Adv. Artif. Intell. Mach. Learn., 4 (1):1925-1942
1. Mohamed S. E. Habib: Cairo University
2. Hossam Fahmy: Cairo University
3. Mohamed Abu-ElYazeed: Cairo University
DOI: 10.54364/AAIML.2024.41110
Article History: Received on: 08-Jan-24, Accepted on: 30-Jan-24, Published on: 06-Feb-24
Corresponding Author: Mohamed S. E. Habib
Email: mohamed1611071@eng1.cu.edu.eg
Citation: Mohamed Habib, et al. Novel End-to-End Production-Ready Machine Learning Flow for Nanolithography Modeling and Correction. Advances in Artificial Intelligence and Machine Learning. 2024;4(1):110..
Mask optimization for optical lithography 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 ML-RETs are still not enabled for IC production flows yet. In this study, we analyze the reasons holding back ML computational lithography from being production ready. We present a novel flow that enables end-to-end mask optimization in addition to high scalability and consistency.