This research program offers an exciting opportunity for students to explore the integration of machine learning (ML) techniques with compact modeling of advanced transistors, including Gate-All-Around (GAA) and FinFET technologies. Conventional device models face challenges related to accuracy, speed, and parameter extraction, especially as device complexity increases. Students will assist in developing ML-based modeling approaches to address these challenges.

Eligibility & Participation Requirements Target Students: Students pursuing a Master's or Bachelor's degree with a background in electrical or electronic engineering, semiconductor physics, or related fields.

Preferred Skills: Familiarity with machine learning, artificial neural networks, Python, device modeling, and SPICE simulations is beneficial but not mandatory.

Language Requirement: English proficiency.

Duration: 6 months preferred (minimum 2 months)

Project Outcomes Gain hands-on experience in ML-driven transistor modeling, work on leading semiconductor technologies, and contribute to the future of AI-driven IC design.

This program provides a unique interdisciplinary research experience, bridging semiconductor physics, ML, and device modeling, preparing students for careers in academia and industry.

Interested students can apply or inquire for further details by contacting Prof. Girish Pahwa (girish@nycu.edu.tw).