Internship in Machine Learning for Semiconductor Packaging (F/M/D)

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

Help us grow and be more impactful! The

Facing the challenges of our time

Help us grow and be more impactful! The Predictive Analytics group is offering a student project or internship to develop and validate an adaptive equipment-intelligence framework for semiconductor manufacturing using machine telemetry and high-frequency sensing.

Using semiconductor die bonding as the experimental platform, the project will investigate whether acoustic emission and other sensor signals can provide robust information about equipment and process state across changing recipes and operating conditions.

Machine-learning methods will be developed for:

• Quality prediction
• Anomaly detection and virtual metrology, with emphasis on generalization across recipes and operating conditions
• Uncertainty-aware adaptation using limited data from new process conditions

The project will quantify which sensing modalities and signal representations remain informative under process changes and determine how much new data is required to adapt the monitoring system to a new operating regime.

The project is intended for a minimum duration of six months and requires regular on-site work in Alpnach, Switzerland.

Your Responsibilities

• Design and instrument an experimental setup for semiconductor die-bonding measurements.
• Acquire, synchronize, document, and preprocess machine telemetry, acoustic-emission data, and other high-frequency sensor signals.
• Develop machine-learning approaches for quality prediction, anomaly detection, and virtual metrology.
• Evaluate the robustness and generalization of sensing modalities and signal representations 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

Your profile

Bachelor's or Master's studies in Computer Science, Data Science, Electrical Engineering, Mechanical Engineering, Robotics, Microengineering, or a related engineering field, with a focus on machine learning.

Know-how

• Good knowledge of machine learning and engineering fundamentals
• Proficiency in Python
• Familiarity with time-series and sensor data
• Interest in industrial experimentation
• Hands-on skills to build and instrument an experimental setup
• Familiarity with Git and Linux is an advantage
• Knowledge of acoustic sensing or virtual metrology is an advantage
• Strong English communication skills; German is an advantage

Interpersonal Skills

• Driven and motivated
• Practical, hands-on working style
• Good communication skills
• Highly collaborative
• Flexible and adaptable thinking and operating style

CSEM mission and values

We believe in a future that is imagined, shared, and created TOGETHER.

At CSEM, we don’t just innovate, we envision technologies that shape tomorrow.

Since 1984, our purpose has remained constant: to harness science and technology for the benefit of society. Today, with over 650 people across Switzerland, we remain committed to our mission: to develop and transfer world-class solutions in digital technologies, precision manufacturing, and sustainable energy systems. In doing so, we empower Swiss industry to innovate and remain competitive.

Our strength is the excellence of our people. We believe that strong values support the successful development of our organization as well as the harmonious and balanced development of all our employees.

We are

• A unique place between research and industry at the cutting edge of new technologies
• An innovative, non-profit, and employee-driven company
• A dynamic, multidisciplinary, and multicultural environment
• A solar team focused on enabling solutions to energy challenges for a sustainable world

Working@CSEM means

• Being part of a passionate community.
• Incredible flexibility, attractive working conditions, and great opportunities of development.
• Benefit from a management style based on trust & feedback and that favors a work-life balance.

We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity.