Engineers are continually faced with decisions about how much data they can collect. In this work, we present a statistically-based smart sampling methodology which can be used to target data collection and ensure that the risk to the product is clearly understood. Smart sampling combines knowledge of the distributions of control statistics with knowledge of the run length distributions they induce to balance the cost of information against the ability to respond to anomalies. We define and explore three characteristics that any sampling plan deemed “smart” must explicitly address: control errors that are associated with basing decisions on sample data, jeopardy that is associated with uncertainty about the true condition of the process, and the switching mechanism that controls the dynamic response to the latest information about the process. We show how the interplay of these characteristics can be exploited to comprehend the merits of a sampling plan. Practical examples of optical product inspection and process defectivity control are presented and explained.
Jeffrey Weintraub and Scott Warrick, "Smart sampling for process control," Proc. SPIE 10145, Metrology, Inspection, and Process Control for Microlithography XXXI, 101450S (Presented at SPIE Advanced Lithography: February 28, 2017; Published: 28 March 2017); https://doi.org/10.1117/12.2258031.
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