Dr. Yuri Granik
at Siemens EDA
SPIE Involvement:
Conference Program Committee | Author | Instructor
Publications (86)

SPIE Journal Paper | 14 March 2024
JM3, Vol. 23, Issue 02, 021302, (March 2024) https://doi.org/10.1117/12.10.1117/1.JMM.23.2.021302
KEYWORDS: SRAF, Lithography, Optical proximity correction, Printing, Photomasks, Manufacturing, Photovoltaics, Design, Extreme ultraviolet, Convolution

Proceedings Article | 28 April 2023 Presentation + Paper
Proceedings Volume 12495, 1249504 (2023) https://doi.org/10.1117/12.2658771
KEYWORDS: SRAF, Lithography, Printing, Photomasks, Optical proximity correction, Photovoltaics, Manufacturing, Image quality, Extreme ultraviolet, Binary data

Proceedings Article | 22 February 2021 Paper
Proceedings Volume 11613, 116130H (2021) https://doi.org/10.1117/12.2584771
KEYWORDS: SRAF, 3D modeling, Printing, Data modeling, 3D printing

Proceedings Article | 23 March 2020 Presentation + Paper
Proceedings Volume 11327, 113270G (2020) https://doi.org/10.1117/12.2552527
KEYWORDS: Optical proximity correction, Finite element methods, Printing, Convolution, Atomic force microscopy, Scanning electron microscopy, Polymers, Data modeling, Photoresist processing, Numerical analysis

SPIE Journal Paper | 22 February 2020
JM3, Vol. 19, Issue 01, 013502, (February 2020) https://doi.org/10.1117/12.10.1117/1.JMM.19.1.013502
KEYWORDS: Convolution, Process modeling, Photoresist materials, Optical simulations, Optical proximity correction, Source mask optimization, System on a chip, Printing, Photoresist processing, Geometrical optics

Showing 5 of 86 publications
Conference Committee Involvement (14)
DTCO and Computational Patterning IV
23 February 2025 | San Jose, California, United States
DTCO and Computational Patterning III
26 February 2024 | San Jose, California, United States
DTCO and Computational Patterning II
27 February 2023 | San Jose, California, United States
DTCO and Computational Patterning
26 April 2022 | San Jose, California, United States
Optical Lithography XXXIV
22 February 2021 | Online Only, California, United States
Showing 5 of 14 Conference Committees
Course Instructor
SC1159: Optimization Methods for Lithographers
Mathematical Optimization is an empowering and indispensable tool in productive engineering practices. A variety of lithographical applications rely on optimization methods to deliver efficient engineering solutions: Process engineers routinely tune the number of films and optical properties of resist stacks, while lithographers subject the projection illuminator towards a laborious perfection by using Source-Mask Optimization (SMO). The spectrum of methods, which are used in the aforementioned (and numerous other) everyday practices, is broad. Finding a suitable algorithm for a given problem is not always easy. This course classifies lithography-related optimization problems, scrutinizes state-of-the-art optimization algorithms, and then makes recommendations on how to properly match these problems with effective and practical optimization methods. We will start by working with unconstrained and constrained one-dimensional problems, move on to consider linear programming, and then address special types of high-dimensional problems, all illustrated with lithographical examples, including mask-inverse lithography (ILT) and SMO. The course will continue with an outline of modern optimization algorithms and explanation of their properties, strengths, weaknesses, and limitations.
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