Dr. Yuri Granik
Chief Engineering Scientist at Mentor a Siemens Business
SPIE Involvement:
Conference Program Committee | Author | Instructor
Publications (83)

Proceedings Article | 23 March 2020
Proc. SPIE. 11327, Optical Microlithography XXXIII
KEYWORDS: Data modeling, Polymers, Atomic force microscopy, Scanning electron microscopy, Printing, Numerical analysis, Finite element methods, Optical proximity correction, Convolution, Photoresist processing

SPIE Journal Paper | 22 February 2020
JM3 Vol. 19 Issue 01
KEYWORDS: Convolution, Process modeling, Photoresist materials, Optical simulations, Optical proximity correction, Source mask optimization, System on a chip, Printing, Photoresist processing, Geometrical optics

Proceedings Article | 17 October 2019
Proc. SPIE. 11148, Photomask Technology 2019
KEYWORDS: Mathematical modeling, Lithography, Diffusion, 3D modeling, Photoresist materials, Finite element methods, Critical dimension metrology, Photoresist processing, Photoresist developing, Process modeling

Proceedings Article | 10 October 2019
Proc. SPIE. 11148, Photomask Technology 2019
KEYWORDS: Switches, Printing, Photoresist materials, Optical simulations, Optical proximity correction, Convolution, Geometrical optics, Photoresist processing, System on a chip, Process modeling

Proceedings Article | 4 April 2019
Proc. SPIE. 10962, Design-Process-Technology Co-optimization for Manufacturability XIII
KEYWORDS: Lithography, Coastal modeling, Cadmium, Data modeling, Calibration, Neural networks, Machine learning, Optical proximity correction, Systems modeling, Process modeling

Showing 5 of 83 publications
Conference Committee Involvement (10)
Optical Lithography XXXIV
21 February 2021 | San Jose, California, United States
Optical Microlithography XXXIII
25 February 2020 | San Jose, California, United States
Optical Microlithography XXXII
26 February 2019 | San Jose, California, United States
Optical Microlithography XXXI
27 February 2018 | San Jose, California, United States
Optical Microlithography XXX
28 February 2017 | San Jose, California, United States
Showing 5 of 10 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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