| KEYWORD |
AI-Enabled Framework for Smart Semiconductor Manufacturing
keywords ANOMALY DETECTION, GENERATIVE AI, MACHINE LEARNING, NEURAL NETWORKS, SEMICONDUCTOR PRODUCTION
Reference persons SANTA DI CATALDO, SARA VINCO
Research Groups DAUIN - GR-06 - ELECTRONIC DESIGN AUTOMATION - EDA
Thesis type RESEARCH
Description With the rise of Machine Learning (ML) and Artificial Intelligence (AI), the semiconductor industry is undergoing a revolution in how it approaches manufacturing. This thesis works in this direction, by proposing an AI-enabled framework to support the smart monitoring and optimization of the semiconductor manufacturing process. An AI-powered engine examines sensor data recording physical parameters during production (like gas flow, temperature, voltage, etc.) as well as test data. The thesis can evolve in different directions: (1) identification of anomalies in the production chain, either offline from collected data-traces or online from a continuous stream of sensed data; (2) forecasting of new data of the future production; and (3) automatic generation of synthetic traces, to strengthen the data-based algorithms. All such tasks provide valuable information to an advanced Manufacturing Execution System (MES), which reacts by optimizing the production process and management of the equipment maintenance policies. The thesis is supported by companies with industrial expertise and real-world applications.
Required skills Machine Learning; Programming; Python
Deadline 14/03/2025
PROPONI LA TUA CANDIDATURA