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. Using machine learning algorithms to identify these anomalies is challenged by this scarcity of defect data. A possible way to overcome this challenge is to use anomaly detectors that train only on the, widely available, non-defective examples. Anomaly detection models capture statistical representations of the good cases, allowing for defective cases to be identified by their distance in a latent space to the good distributions. At ASM we recently experimented with using Generative Adversarial Networks (GANs) for anomaly detection [1][2]. A GAN trained on images of good devices was able to identify defective ones as ‘anomalous’ and not part of its data set. The goal of the new internship project is to broaden the previous study to a wider range of methods, including Bayesian ones, and to experiment with different types of microdevices and defects. The key tasks that the intern will undertake are the following:
- Conduct a literature study of various state of the art anomaly detection algorithms.
- Propose and motivate a choice of a limited number of methods for further experimentation. The purpose is to use ASM use-cases (device inspection using vision and other sensing modalities) to compare the relative performance of different approaches.
- Propose and implement any required improvements to the most relevant SOTA anomaly detection technique and data processing pipelines.
- Compile a mid-term and final reports summarizing results and conclusions.
For more information about this project, please contact fboughorbel@alsi.asmpt.com or call +31 24 204 2824.
References
[1] Schlegl T, Seeböck P, Waldstein SM, Schmidt-Erfurth U, Langs G. Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. InInternational conference on information processing in medical imaging, pp. 146-157, Springer, Cham, Jun 25, 2017.
[2] Zenati H, Foo CS, Lecouat B, Manek G, Chandrasekhar VR. Efficient gan-based anomaly detection. arXiv preprint arXiv:1802.06222. 2018 Feb 17.
[3] Chalapathy, Raghavendra, and Sanjay Chawla. “Deep learning for anomaly detection: A survey.” arXiv preprint arXiv:1901.03407 (2019).
[4] Chandola V, Banerjee A, Kumar V. “Anomaly detection: A survey,” ACM computing surveys (CSUR), 41(3):1-58, Jul 30, 2009.