ISSN :2582-9793

Novel End-to-End Production-Ready Machine Learning Flow for Nanolithography Modeling and Correction

Original Research (Published On: 06-Feb-2024 )
Novel End-to-End Production-Ready Machine Learning Flow for Nanolithography Modeling and Correction

Mohamed S. E. Habib, Hossam A. H. Fahmy and Mohamed F. Abu-ElYazeed

Adv. Artif. Intell. Mach. Learn., 4 (1):1925-1942

Mohamed S. E. Habib : Cairo University

Hossam A. H. Fahmy : Cairo University

Mohamed F. Abu-ElYazeed : Cairo University

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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 S. E. Habib, Hossam A. H. Fahmy, Mohamed F. Abu-ElYazeed, (2024). Novel End-to-End Production-Ready Machine Learning Flow for Nanolithography Modeling and Correction. Adv. Artif. Intell. Mach. Learn., 4 (1 ):1925-1942


Abstract

    

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.

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