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Special Presentation - Dr. Dren Qerimi

Event Type
Seminar/Symposium
Sponsor
The Department of Nuclear, Plasma, and Radiological Engineering
Location
2018 Campus Instructional Facility, 1405 Springfield Avenue, Urbana
Date
Dec 12, 2023   4:00 - 4:50 pm  
Speaker
Dr. Dren Qerimi, R&D Engineer, LAM Research
Cost
Free and Open to the Public
E-Mail
nuclear@illinois.edu
Phone
217-333-2295
Views
21
Originating Calendar
NPRE seminars

Computational Modeling Challenges in Semiconductor Fabrication

Abstract: One of the bottlenecks to building semiconductor chips is the increasing cost required to develop chemical plasma processes that form the transistors and memory storage cells1,2. These processes are still developed manually using highly trained engineers searching for a combination of tool parameters that produces an acceptable result on the silicon wafer3 . The challenge for computer algorithms is the availability of limited experimental data owing to the high cost of acquisition, making it difficult to form a predictive model with accuracy to the atomic scale. Here we study Bayesian optimization algorithms to investigate how artificial intelligence (AI) might decrease the cost of developing complex semiconductor chip processes. In particular, we create a controlled virtual process game to systematically benchmark the performance of humans and computers for the design of a semiconductor fabrication process. We find that human engineers excel in the early stages of development, whereas the algorithms are far more cost-efficient near the tight tolerances of the target. Furthermore, we show that a strategy using both human designers with high expertise and algorithms in a human first–computer last strategy can reduce the cost-to-target by half compared with only human designers. Finally, we highlight cultural challenges in partnering humans with computers that need to be addressed when introducing artificial intelligence in developing semiconductor processes.

Bio: Born and raised in Kosovo. Dren Qerimi is a R&D Engineer specializing in semiconductor manufacturing and machine learning. Currently with working at LAM Research in Oregon, focusing on optimizing fabrication processes, reducing costs, and improving tool availability through innovative machine learning models and hardware configurations. 

With a background in surface wave plasma technology, Dren's career includes groundbreaking work in damage-free processing and tin mitigation in EUV sources. Dren earned his Ph.D., Master's, and Bachelor's degrees in Nuclear, Plasma, and Radiological Engineering from the University of Illinois, where his research focused on tin etching in EUV sources and racial probe development. Married and I have two beautiful daughters.

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