Metrology, which plays an important role in ensuring production quality in modern manufacturing industries, incurs substantial costs, both in terms of the infrastructure required, and the time needed to perform measurements. In particular, in the semiconductor manufacturing industry, measuring fundamental quantities on different sites of a wafer surface is associated with increased production time. To increase metrology efficiency, a typical strategy is to limit the number of sites measured and to exploit statistical models (soft sensing) to reconstruct the wafer profile. Moreover, for quality reasons, spatial dynamic sampling strategies may be employed to ensure that all regions of a wafer surface are checked periodically during production. In this work, we propose a new sampling strategy, called Induced Start Dynamic Sampling (ISDS), that adapts greedy feature selection algorithms to the spatial dynamic sampling problem such that the number of measured sites at each process run is minimized while achieving good wafer profile reconstruction accuracy and process visibility. The superiority of the proposed strategy with respect to the state-of-the-art is demonstrated using both simulated data and an industrial chemical vapour deposition case study.
|Number of pages||15|
|Journal||IEEE Transactions of Automation Science and Engineering|
|Early online date||31 Jul 2019|
|Publication status||Early online date - 31 Jul 2019|
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Advances in machine learning for sustainable manufacturingAuthor: Zocco, F., Dec 2021
Supervisor: Liu, X. (Supervisor) & McLoone, S. (Supervisor)
Student thesis: Doctoral Thesis › Doctor of Philosophy