Dr. Luigi Capodieci
CTO at Motivo Inc
SPIE Involvement:
Fellow status | Conference Program Committee | Conference Chair | Conference Co-Chair | Author | Editor | Instructor
Publications (49)

PROCEEDINGS ARTICLE | October 16, 2017
Proc. SPIE. 10451, Photomask Technology
KEYWORDS: Analytics, Data modeling, Silicon, Manufacturing, Data processing, Design for manufacturing, Photomasks, Machine learning, Optical proximity correction, Systems modeling

PROCEEDINGS ARTICLE | March 16, 2016
Proc. SPIE. 9781, Design-Process-Technology Co-optimization for Manufacturability X
KEYWORDS: Lithography, Metals, Manufacturing, Design for manufacturing, Photomasks, Image classification, Double patterning technology, Optical proximity correction, Overlay metrology, 193nm lithography, Library classification systems, Design for manufacturability

PROCEEDINGS ARTICLE | March 26, 2015
Proc. SPIE. 9427, Design-Process-Technology Co-optimization for Manufacturability IX
KEYWORDS: Statistical analysis, Data modeling, Metals, Manufacturing, Design for manufacturing, Image classification, Raster graphics, Optimization (mathematics), Current controlled current source, Design for manufacturability

Showing 5 of 49 publications
Conference Committee Involvement (10)
Design-Process-Technology Co-optimization for Manufacturability XIII
27 February 2019 | San Jose, California, United States
Design-Process-Technology Co-optimization for Manufacturability XII
28 February 2018 | San Jose, California, United States
Design-Process-Technology Co-optimization for Manufacturability XI
1 March 2017 | San Jose, California, United States
Design-Process-Technology Co-optimization for Manufacturability X
24 February 2016 | San Jose, California, United States
Design-Process-Technology Co-optimization for Manufacturability IX
25 February 2015 | San Jose, California, United States
Showing 5 of 10 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.
SC540: Applying Optical Proximity Correction and Design for Manufacturability to Product Designs
Optical proximity correction (OPC) is now a requirement for advanced semiconductor manufacturing. OPC alters the designed layout to compensate for systematic patterning distortions and/or to implement process latitude improving methods. Accurate and practical model-based OPC implementation is needed with essentially all lithography resolution enhancement techniques (RET) on complex real world designs. This practical example-oriented class will prepare attendees to implement manufacturable rule and model-based OPC on their product designs and introduce them to optimized OPC, design & process solution methods known as lithographic Design for Manufacturability (DFM).
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