Dr. Jason P. Cain
Principal Member of the Technical Staff at Advanced Micro Devices
SPIE Involvement:
Fellow status | Senior status | Conference Program Committee | Conference Chair | Conference Co-Chair | Editor | Author | Instructor
Websites:
Publications (35)

PROCEEDINGS ARTICLE | April 10, 2018
Proc. SPIE. 10588, Design-Process-Technology Co-optimization for Manufacturability XII
KEYWORDS: Lithography, Data modeling, Manufacturing, Feature extraction, Design for manufacturing, Machine learning, Testing and analysis, Model-based design

PROCEEDINGS ARTICLE | April 4, 2018
Proc. SPIE. 10588, Design-Process-Technology Co-optimization for Manufacturability XII
KEYWORDS: Lithography, Optical lithography, Metals, Pattern recognition, Manufacturing, Design for manufacturing, Machine learning, Image classification, Library classification systems

PROCEEDINGS ARTICLE | March 30, 2017
Proc. SPIE. 10148, Design-Process-Technology Co-optimization for Manufacturability XI
KEYWORDS: Analytics, Logic, Optical lithography, Statistical analysis, Databases, Metals, Silicon, Manufacturing, Computer simulations, Design for manufacturing, Charge-coupled devices, Optical proximity correction, Digital electronics, Product engineering, Yield improvement, Model-based design, Process modeling, Design for manufacturability

Showing 5 of 35 publications
Conference Committee Involvement (21)
Design-Process-Technology Co-optimization for Manufacturability XIII
24 February 2019 | San Jose, California, United States
Metrology, Inspection, and Process Control for Microlithography XXXIII
24 February 2019 | San Jose, California, United States
Design-Process-Technology Co-optimization for Manufacturability XII
28 February 2018 | San Jose, California, United States
Metrology, Inspection, and Process Control for Microlithography XXXII
26 February 2018 | San Jose, California, United States
Design-Process-Technology Co-optimization for Manufacturability XI
1 March 2017 | San Jose, California, United States
Showing 5 of 21 published special sections
Course Instructor
SC1209: Data Analytics and Machine Learning in Semiconductor Manufacturing: Applications for Physical Design, Process and Yield Optimization
This course provides an introduction to methodologies and techniques in Data Analytics and Machine Learning, with specific applications to semiconductor manufacturing, from physical design characterization to process and yield optimization. While the growth of (Big) Data Analytics and Machine Learning continues to increase across virtually every industrial sector, the semiconductor space has seen only a modest adoption. This course aims at lowering the entry barrier, by providing both foundational and practical skills for semiconductor engineers and practitioners. Following a comprehensive survey of the state-of-the-art and current developments in Data Analytics and Machine Learning, the course describes how functional interactions and data information flows in the Design-to-Manufacturing chain can be enhanced by analytics algorithmic methodologies. Quantitative definitions of physical design space coverage and process space learning are introduced as the unifying abstraction, allowing for the construction of a computational application framework. Design-Technology-Co-Optimization (DTCO) is then extended with the novel paradigm of DFM-as-Search. Examples from this new DFM computational toolkit, are used to demonstrate how the advanced IC technology nodes (14, 10, 7 and 5nm) not only benefit from, but actually require the use of a new class of correlation extraction algorithms for heterogeneous data sets.
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