Understanding Semiconductors
Episode 6
Modern metrology from Lab to Fab by Rigaku
A podcast for engineering leaders in characterization, metrology, process, and analytics, looking for discussion around semiconductor metrology challenges.
In this episode: Dr. Diebold returns! A deep dive into his thoughts on Near-Fab, Lab-to-Fab, and artificial intelligence
They discuss:
- Examples of Lab-to-Fab transitions
- Machine learning: How to deal with big data
- Key takeaways from the 2022 Frontiers Conference: Machine Learning vs. Regular Algorithms.
- How to address the talent gap in the semiconductor industry
Visit our LinkedIn Semiconductor Metrology Solutions Showcase
Contact Markus Kuhn on LinkedIn for any potential guest requests or episode ideas.
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Host: Markus Kuhn
Ph.D. in Chemistry, University of Western Ontario, Canada BS Honors, University of Western Ontario, Canada. Markus is a semiconductor technology expert with a proven track record in developing, managing, and implementing novel metrology strategies and programs in support of advanced semiconductor process and architectural technology development. During a 25-year career with Intel and Digital Equipment Corporation, Markus was responsible for the development and implementation of a broad range of analytical capabilities to help meet semiconductor technology goals and was a key technical contributor to Intel's breakthrough strain, high K/metal gate, FinFET, and advanced memory programs. Currently, he is a Senior Director for Semiconductor Technology and a Fellow for Rigaku Corporation. His interests include the advancement of analytical capabilities for nanoscale devices, and he has a broader interest in the synergies between analytical characterization methods, machine learning, and process metrology to help enable emerging nanoscale device technologies. He has published 100+ refereed papers and holds 30+ patents relating to semiconductor technology. 
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