ML Internship: Predictive Analytics in Semic. Packaging

CSEM; Alpnach; Vor Ort; CHF 17.000 - 29.000; Vollzeit

Zusammenfassung

CSEM in Alpnach, Switzerland, offers a student project to develop an adaptive equipment-intelligence framework for semiconductor manufacturing using machine telemetry and high-frequency sensing. The internship focuses on die-bonding experiments and collecting telemetry and sensor data.

You will build ML approaches for quality prediction, anomaly detection, and virtual metrology, and ensure robustness across recipes and operating conditions, with regular on-site work.

Qualifikationen

• Bachelor's or Master's studies in Computer Science, Data Science, Electrical Engineering, Mechanical Engineering, Robotics, Microengineering, or related field with a focus on machine learning.
• Strong knowledge of machine learning and fundamentals of engineering.
• Proficiency in Python; familiarity with time-series and sensor data.

Aufgaben

• Design and instrument an experimental setup for semiconductor die-bonding measurements.
• Acquire, synchronize, document, and preprocess machine telemetry and high-frequency sensor signals.
• Develop machine-learning approaches for quality prediction, anomaly detection, and virtual metrology.
• Evaluate robustness/generalization of sensing modalities across recipes and operating conditions.
• Develop uncertainty-aware adaptation methods using limited data from new process conditions.
• Document methods and results and communicate findings to the Predictive Analytics team.
• Assist the team in various tasks.

Kenntnisse

Machine learning basics

Engineering fundamentals

Python

Time-series data

Sensor data

Industrial experimentation

Hardware instrumentation

Git & Linux

Acoustic sensing

Virtual metrology

English communication

German knowledge

Ausbildung

ML/engineering degree

Jobbeschreibung

CSEM in Alpnach, Switzerland, offers a student project to develop an adaptive equipment-intelligence framework for semiconductor manufacturing using machine telemetry and high-frequency sensing. The internship focuses on die-bonding experiments and collecting telemetry and sensor data.

You will build ML approaches for quality prediction, anomaly detection, and virtual metrology, and ensure robustness across recipes and operating conditions, with regular on-site work.