Dr. Yuri Granik
Chief Engineering Scientist at Mentor, a Siemens Business
SPIE Involvement:
Fellow status | Senior status | Conference Program Committee | Author | Instructor
Publications (79)

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

Proceedings Article | 20 March 2019
Proc. SPIE. 10961, Optical Microlithography XXXII
KEYWORDS: Polymers, Printing, Photoresist materials, Photomasks, Optical proximity correction, SRAF, Convolution, Process modeling

Proceedings Article | 18 March 2019
Proc. SPIE. 10961, Optical Microlithography XXXII
KEYWORDS: Satellites, Complex systems, Semiconductor manufacturing, Optical proximity correction, Process modeling

Proceedings Article | 18 March 2019
Proc. SPIE. 10961, Optical Microlithography XXXII
KEYWORDS: Lithography, Optical lithography, Computer simulations, 3D modeling, Photoresist materials, Finite element methods, Photoresist processing, Photoresist developing, Process modeling, Chemically amplified resists

Proceedings Article | 20 March 2018
Proc. SPIE. 10587, Optical Microlithography XXXI
KEYWORDS: Lithography, Optical lithography, Photomasks, Source mask optimization

SPIE Journal Paper | 25 January 2018
JM3 Vol. 17 Issue 01
KEYWORDS: Critical dimension metrology, Photoresist materials, Reflection, Printing, Silicon, Cadmium, Solids, 3D modeling

Showing 5 of 79 publications
Conference Committee Involvement (9)
Optical Microlithography XXXIII
23 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
Optical Microlithography XXIX
23 February 2016 | San Jose, California, United States
Showing 5 of 9 published special sections
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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