Dr. James P. Shiely
at Siemens EDA
SPIE Involvement:
Author | Instructor
Publications (36)

Proceedings Article | 26 May 2022 Presentation + Paper
Yi-Ting Lin, Sean Shang-En Tseng, Iris Hui-Ru Jiang, James Shiely
Proceedings Volume 12052, 120520X (2022) https://doi.org/10.1117/12.2613681
KEYWORDS: SRAF, Photovoltaics, Machine learning, Lithography, Source mask optimization, Optical proximity correction

Proceedings Article | 20 March 2019 Paper
Proceedings Volume 10962, 109620A (2019) https://doi.org/10.1117/12.2517194
KEYWORDS: Detection and tracking algorithms, Integrated circuits, Lithography, Pattern recognition

Proceedings Article | 20 March 2019 Presentation + Paper
Sheng-Wei Chien, Jia-Syun Cai, Chien-Lin Lee, Kuen-Yu Tsai, James Shiely, Matt St. John
Proceedings Volume 10961, 1096107 (2019) https://doi.org/10.1117/12.2515414
KEYWORDS: Optical proximity correction, Lithography, Photomasks, Machine learning, Computational lithography

Proceedings Article | 20 March 2019 Presentation + Paper
Sean Shang-En Tseng, Wei-Chun Chang, Iris Hui-Ru Jiang, Jun Zhu, James Shiely
Proceedings Volume 10961, 109610B (2019) https://doi.org/10.1117/12.2514818
KEYWORDS: Scanning electron microscopy, Image processing, Lithography, Silicon, Semiconducting wafers

Proceedings Article | 3 October 2018 Presentation + Paper
Proceedings Volume 10810, 1081004 (2018) https://doi.org/10.1117/12.2504982
KEYWORDS: Computer programming, Neural networks, Visualization, Neurons, Machine learning, Data modeling, Photomasks, Binary data, Lithography, Information theory

Showing 5 of 36 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 classifiers, multilayer perceptrons and deep neural networks.
SIGN IN TO:
  • View contact details

UPDATE YOUR PROFILE
Is this your profile? Update it now.
Don’t have a profile and want one?

Advertisement
Advertisement
Back to Top