@article{NnAaaa,
	author="G.A. Susto and M. Maggipinto and F. Zocco and S. McLoone",
	title="Induced Start Dynamic Sampling for Wafer Metrology Optimization",
	journal="IEEE Transactions on Automation Science and Engineering",
	year="2020",
	month="January",
	pages="418-432",
	volume="17",
	number="1",
	note="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."
}