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.
Such model re-training/fine-tuning usually requires skilled operators for performing hyperparameters optimization and for introducing modifications in the deployed architectures when needed. This project aims at exploring applications to ASM manufacturing tasks of state the art Automated ML and Meta-learning methods. The goal is to achieve a high degree of autonomy in the optimization of both model structure and related hyperparameters. Meta-learning, Neural Architecture Search (NAS), and other AutoML approaches are expected to play a critical role in advanced manufacturing, enabling both inspection and complex process control. During this project we will introduce the intern to real-world use-cases in microdevice inspection and other ASM tasks, using these as a testbed for the target Meta-learning/AutoML methods. The intern is expected to perform the following tasks:
- Survey the state of the art in Meta-learning and related techniques.
- Select, with support from company advisers, ASM PT applications that are highly relevant to Meta-learning.
- Conduct experiments to demonstrate increased automation and quality in DL model production.
- Summarize the conclusions of the research in a report.
For more information about this project, please contact fboughorbel@alsi.asmpt.com or call +31 24 204 2824.
References
[1] Vanschoren J. Meta-learning: A survey. arXiv preprint arXiv:1810.03548. Oct 8, 2018.
[2] He X, Zhao K, Chu X. AutoML: A Survey of the State-of-the-Art. arXiv preprint arXiv:1908.00709. Aug 2, 2019.
[3] Elsken T, Metzen JH, Hutter F. Neural architecture search: A survey. arXiv preprint arXiv:1808.05377. Aug 16, 2018.