WebAbout - HOME Web14 mrt. 2024 · This talk will present our recent results leveraging modern AI and machine learning with domain-specific customizations for agile IC design and manufacturing, including DREAMPlace (DAC’19 and TCAD’21 Best Paper Awards) and its various extensions, MAGICAL for analog/mixed-signal layout automation, LithoGAN for design …

Machine Learning for Mask Synthesis and Verification

WebLithography simulation is one of the most fundamental steps in process modeling and physical verification. Conventional simulation methods suffer from a tremendous … Weblight intensity information. LithoGAN [17] is a very early attempt to use condi-tional generative adversarial networks (cGAN) for end-to-end modeling. The major component of LithoGAN is a standard cGAN generator, which takes the input of a mask with the target shape located in the center of the mask. cGAN can then gener- software house authorized dealers

AI-Enabled Agile IC Design and Manufacturing

WebLithoGAN: End-to-end Lithography Modeling with Generative Adversarial Networks High Distinction American University of Beirut Jun 2014 Dean's Honor List in all terms ... WebDOI: 10.1145/3316781.3317852 Corpus ID: 163165056; LithoGAN: End-to-End Lithography Modeling with Generative Adversarial Networks @article{Ye2024LithoGANEL, title={LithoGAN: End-to-End Lithography Modeling with Generative Adversarial Networks}, author={Wei Ye and Mohamed Baker Alawieh and Yibo Lin and David Z. Pan}, … Web06/2024: My co-authored paper “LithoGAN: End-to-end Lithography Modeling with Generative Adversarial Networks” was selected as a Best Paper Award Candidate @ DAC'19! 03/2024: My co-authored paper “Litho-GPA: Gaussian Process Assurance for Lithography Hotspot Detection” was accepted @ DATE 2024. software host system name