ASM Pacific is a world-leader in the field of semiconductor equipment. At ASM 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

Master Thesis Projects:

Internship: 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, not to mention their important role in various factory automation and related robotics tasks. Recent progresses in deep learning allow for more reliable visual recognition capabilities. State of the art object detection and segmentation methods are being employed at ASM for various assembly and packaging workflows. Nonetheless, supervised deep learning, based mostly on convolutional neural networks, is proving data and labor intensive for many real-world applications. In a semiconductor manufacturing environment, for example, the configuration of assembled devices can frequently change, and the need for relabeling new datasets on the factory floor can be a bottleneck in a practical deployment scenario. While techniques such as transfer learning are alleviating such bottlenecks, developing a capacity for generalization in machine learning workflows will be greatly beneficial in advanced manufacturing. ASM Pacific Technology is currently exploring data augmentation and generalization techniques based on realistic simulations. To complement this effort, we aim at using Generative models, such as those based on GANs, for enabling prediction from limited labeled data. Such models are showing promise in various applications and would potentially enable the synthesis of large-scale datasets and a better prediction quality in various applications. The goal of this Thesis internship is to explore the use of Generative models for improving the visual recognition of semiconductor device components and for enhancing the reasoning performance of defect detection modules. During his work, the intern will undertake the following tasks:

  • Literature search on the state of the art (deep) generative models and their application to realistic image synthesis and data augmentation (focusing mainly on GANs.)
  • Make a choice of a limited number of models for experimentation and further development.
  • Understand the inclusion of various physical constraints in the Generative models.
  • Train the models on ASM device images and demonstrate the image synthesis results.
  • Analyze the generalization capabilities of the investigated models on object detection and semantic segmentation tasks, working with the ASM center of competency staff.
  • Summarize the results and analysis in a final and mid-term reports.

To apply for this internship please contact , 024 678 2873


[1] Goodfellow, Ian, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. “Generative adversarial nets.” In Advances in neural information processing systems, pp. 2672-2680. 2014.

[2] Radford, Alec, Luke Metz, and Soumith Chintala. “Unsupervised representation learning with deep convolutional generative adversarial networks.” arXiv preprint arXiv:1511.06434 (2015).

[3] Karras, Tero, et al. “Progressive growing of GANs for improved quality, stability, and variation.” arXiv preprint arXiv:1710.10196 (2017).

[4] Brock, Andrew, Jeff Donahue, and Karen Simonyan. “Large Scale GAN Training for High Fidelity Natural Image Synthesis.” arXiv preprint arXiv:1809.11096 (2018).

Internship: Micro-device 3D structure inference using deep learning

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.

The use of 3D micro-scale imaging is increasingly popular for several critical tasks. Cost, throughput, and space limitations can however restrict the integration of complex 3D capture devices for certain applications. An attractive option is to use the already available optics and 2D sensors and reconstruct 3D information computationally. Conventionally, in such approach one would need to acquire multiple 2D images, from different viewpoints, to obtain a good reconstruction. Other techniques can be used as well, including depth recovery from focus stacks. These methods are, however, considered too slow for online semiconductor manufacturing applications. More recently, the task of reconstructing 3D from single images regained attention due to the possibilities offered by deep convolutional networks. A CNN model can be trained on pairs of 2D and 3D images, learning the geometric structure of the objects of interest. This can potentially turn ordinary industrial vision cameras into 3D acquisition devices. The goal of this Master thesis project is to:

  • Perform a literature search on the latest deep-learning based 3D reconstruction techniques, with a focus on monocular methods.
  • Select two or three methods for evaluation and further development.
  • Acquire 2D/3D training pairs using available ASM equipment (confocal microscopes, stereo cameras, and regular 2D cameras.) The imagery will be acquired on ASM application-related devices.
  • Train suitable 3D reconstruction models, using internal and external datasets, and employing transfer learning when relevant.
  • Explore the incorporation of prior knowledge about device geometry and materials in the reconstruction process.
  • Compile the results and analysis in a final and mid-term reports.

The intern will be supported and advised in his various tasks by ASM Pacific Technology staff, interacting with the local team in the Netherlands, as well as with the Asia-based groups.

To apply for this internship please contact , 024 678 2873


[1] Eigen, David, Christian Puhrsch, and Rob Fergus. “Depth map prediction from a single image using a multi-scale deep network.” In Advances in neural information processing systems, pp. 2366-2374. 2014.

[2] Garg, Ravi, Vijay Kumar BG, Gustavo Carneiro, and Ian Reid. “Unsupervised cnn for single view depth estimation: Geometry to the rescue.” In European Conference on Computer Vision, pp. 740-756. Springer, Cham, 2016.

[3] Rezende, Danilo Jimenez, SM Ali Eslami, Shakir Mohamed, Peter Battaglia, Max Jaderberg, and Nicolas Heess. “Unsupervised learning of 3d structure from images.” In Advances in Neural Information Processing Systems, pp. 4996-5004. 2016.

[4] Fan, Haoqiang, Hao Su, and Leonidas J. Guibas. “A Point Set Generation Network for 3D Object Reconstruction from a Single Image.” CVPR. Vol. 2. No. 4. 2017.

[5] Wu, Jiajun, Chengkai Zhang, Tianfan Xue, Bill Freeman, and Josh Tenenbaum. “Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling.” In Advances in Neural Information Processing Systems, pp. 82-90. 2016.

[6] Zhou, Tinghui, Matthew Brown, Noah Snavely, and David G. Lowe. “Unsupervised learning of depth and ego-motion from video.” In CVPR, vol. 2, no. 6, p. 7. 2017.