Dr. James P. Shiely
at Synopsys Inc
SPIE Involvement:
Author | Instructor
Publications (32)

PROCEEDINGS ARTICLE | October 3, 2018
Proc. SPIE. 10810, Photomask Technology 2018
KEYWORDS: Lithography, Data modeling, Visualization, Computer programming, Neural networks, Photomasks, Machine learning, Information theory, Binary data, Neurons

PROCEEDINGS ARTICLE | March 31, 2014
Proc. SPIE. 9052, Optical Microlithography XXVII
KEYWORDS: Lithography, Data modeling, Calibration, Etching, 3D modeling, Integrated modeling, Photomasks, Optical proximity correction, Photoresist processing, Semiconducting wafers

PROCEEDINGS ARTICLE | September 16, 2013
Proc. SPIE. 8880, Photomask Technology 2013
KEYWORDS: Atrial fibrillation, Data modeling, Calibration, Etching, Diffusion, 3D modeling, Printing, Photoresist materials, Optical proximity correction, Semiconducting wafers

PROCEEDINGS ARTICLE | March 29, 2013
Proc. SPIE. 8684, Design for Manufacturability through Design-Process Integration VII
KEYWORDS: Lithography, Inspection, Printing, Photomasks, Double patterning technology, Optical proximity correction, Semiconducting wafers, Tolerancing, Statistical modeling, Model-based design

PROCEEDINGS ARTICLE | April 8, 2011
Proc. SPIE. 7969, Extreme Ultraviolet (EUV) Lithography II
KEYWORDS: Lithography, Reticles, Optical lithography, Cadmium, Calibration, Photomasks, Extreme ultraviolet, Extreme ultraviolet lithography, Critical dimension metrology, Semiconducting wafers

PROCEEDINGS ARTICLE | March 23, 2011
Proc. SPIE. 7973, Optical Microlithography XXIV
KEYWORDS: Optical imaging, Lithography, Data modeling, Calibration, 3D modeling, Photomasks, Optical proximity correction, Neodymium, Semiconducting wafers, Performance modeling

Showing 5 of 32 publications
Course Instructor
SC1264: Machine Learning for Lithography
This course provides background on supervised learning applied to microlithography. A primary goal of the course is to illustrate supervised learning, inference, and validation workflow to practitioners of microlithography, using datasets and problems with which they are familiar. Example applications will include photoresist models and inverse lithography models. Example model types include linear regressions, logistic classifiers and deep neural networks. Training methodology will utilize prepared datasets with Jupyter notebooks.
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