Curriculum
Computer Science for Societal Challenges and Innovation, XXXIX series
Grant sponsor
MUR PNRR DM117 and Statwolf srl
Supervisor

Gian Antonio Susto
Co-supervisor
s
TBD
Contact
manuel.barusco@studenti.unipd.it

Project description
The manufacturing industry is undergoing a transformative phase, propelled by advancements in data accessibility, computational power, storage capacity, and breakthroughs in machine learning (ML) and artificial intelligence (AI). This revolution is profoundly impacting semiconductor manufacturing, where ML-based technologies, such as Predictive Maintenance, Fault/Anomaly Detection, and Soft Sensing/Virtual Metrology, are being integrated to enhance efficiency and quality in the production. However, overcoming challenges related to models scalability, data heterogeneity, and models interpretability is essential for their effective implementation in this intricate industrial domain. Models interpretability is particularly crucial as it mitigates the "black-box" perception of ML models and fosters broader adoption. My research project focuses on developing tailored ML models and algorithms for the semiconductor manufacturing that can be interpretable, scalable and that can overcome the limitations of the actual solutions.