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 email@example.com , 024 678 2873
 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.
 Radford, Alec, Luke Metz, and Soumith Chintala. “Unsupervised representation learning with deep convolutional generative adversarial networks.” arXiv preprint arXiv:1511.06434 (2015).
 Karras, Tero, et al. “Progressive growing of GANs for improved quality, stability, and variation.” arXiv preprint arXiv:1710.10196 (2017).
 Brock, Andrew, Jeff Donahue, and Karen Simonyan. “Large Scale GAN Training for High Fidelity Natural Image Synthesis.” arXiv preprint arXiv:1809.11096 (2018).