Data-Driven Optimization of Die Attach Manufacturing Performance

This project focuses on improving the understanding and performance of the Die Attach process within semiconductor manufacturing. The intern will analyze process and equipment data to identify trends, variability drivers, and opportunities for optimization. The project will explore the application of data analytics and AI techniques to enhance process visibility, detect anomalies, and support data-driven decision-making in a high-volume manufacturing environment.

• Identify key factors influencing Die Attach process performance (e.g., yield, cycle time)
• Develop insights into process trends and variability
• Apply analytics or AI techniques to detect anomalies or optimization opportunities
• Propose improvement recommendations based on data analysis