Skip to main content
Cyber Security and Resilience Community
Cyber Security and Resilience Community
People
Faculty
Research Scientists
Postdoctoral Fellows
All People
Research Groups
Cyber Security and Resilience Technology (CyberSaR)
Roberto Di Pietro Research Group (R-Pietro)
Security Research Bearing Experimental Results (SeRBER)
Research Strategy
extreme gradient boosting
Wide Bandgap Semiconductor Device Design via Machine Learning
Rongyu Lin, Ph.D., Electrical and Computer Engineering
Nov 2, 15:30
-
17:30
B2 L5 R5220
machine learning
light gradient boosting machine
extreme gradient boosting
This dissertation presents novel approaches to the design of electrical and optical wide bandgap semiconductor devices, which opens new avenues for future research. It is possible that it might be used in a broad variety of sectors, including illumination, sensing, disinfection, and power devices by using TCAD and machine learning to deliver quick forecasts of the III-nitride semiconductor device.
