ASM Pacific is a world-leader in the field of semiconductor equipment. At ASM Laser Separation International (ALSI) in Beuningen, the Netherlands, we develop an advanced multi-beam laser dicing tool, but we also have a technology development team (ASM Center of Competency) that supports the global ASM company with high-tech innovations in various domains, like artificial intelligence, advanced motion control, industrial vision, machine learning and data science. In both the laser dicing business unit and the Center of competency ASM Pacific Technology provides challenging internship positions for university level students. Both short-term (3 months) as well as full-lengths (9 months) MSc internships are supported. We currently have several master students working on their internships in the fields of advanced motion control and artificial intelligence and are always looking for new projects + students.


ASM offers:

  • A dynamic high-tech industrial environment in which you learn to apply academic knowledge to real-life problems.
  • Exposure to the larger ASM organization (>16000 person), opportunities to interact/present to colleagues in Hong Kong and Singapore.
  • Solid supervision by senior professionals that have a strong academic and industrial background.
  • A market-conform internship compensation and travel expense compensation.
  • Contribute to state-of-the art industrial developments.


AI and Computer Vision Projects:

Within the AI & Vision team we pursue a range of applied AI/ML projects with a focus on innovation and industrial applications. We are currently hosting several Master students from various universities, including Radboud. The interns are advised by PhD-level staff and supported by the entire team in their work. Additionally, a sizeable computing platform is available DL/ML experimentation.

The Vision/AI team currently offers the following internship themes:

  1. Anomaly detectors, and root cause analysis
  2. Meta-learning and AutoML
  3. Simulators and Generative Models for microscale device understanding
  4. Self-supervised/adapting machine learning
  5. Weakly and semi-supervised learning
  6. Tools for high throughput labeling of large datasets
  7. Efficient implementations of machine learning algorithms on different HW
  8. DL for 3D reconstruction
  9. Reinforcement learning for complex process control
  10. Bayesian modelling of complex manufacturing processes
  11. Multimodal ML      


For students interested in one of the listed areas of research, we can provide a more detailed description, please contact  or call  +31 24 204 2824.

Our target applications are:

  1. Defect detection in micro-device assembly processes
  2. ML-driven Automation of complex assembly and packaging processes

Our long-term goal is to employ AI for extending human engineering and production capabilities reaching a “lights-out smart factory”.

Sample Projects

I. Meta-learning and AutoML for Precision Manufacturing

The use of deep learning (DL) and other state of the art machine learning (ML) techniques is expanding to new arenas such as precision manufacturing. At ASM Pacific Technology ML/DL methods are employed for diverse tasks such as visual inspection of microdevices and for complex process control. One of the key bottlenecks in deploying ML solutions at scale is the need for frequent model updating in response to drift in the data obtained from the field and to changes in the required tasks.

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II. Anomaly Detectors for identifying rare defects

In complex micro-scale device manufacturing representative defect examples are rare and are often hard to collect at scale. Such defects, although subtle, can have major consequences on systems assembled from the affected components.

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III. Simulators and Generative Models for understanding micro-scale devices from minimum labeled data

Imaging and computer vision play a key role in micro-scale manufacturing processes. They enable both online assembly processes and subsequent defect inspection steps. Recent progress in deep learning (DL) allows for more reliable visual recognition leading to DL adoption within ASM for various assembly and packaging workflows.

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IV. Deep Learning for recovering the 3D structure of microdevices

In advanced micro-scale manufacturing, such as in the semiconductor assembly and packaging field, machine vision plays a key role in inspection and process control. Cost, throughput, and space limitations can however restrict the integration of complex 3D capture devices for certain applications. Active and passive stereoscopic imaging systems are the primary tools used for 3D inspection, but an attractive option is to obtain the 3D information computationally from conventional 2D cameras.

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