High-volume manufacturers produce and ship large quantities of products to customers, and very high quality is expected in a competitive marketplace where customer satisfaction is an important metric. One key aspect of quality is uniformity in product characteristics over time. Uniformity can be especially important when the product is used as a component in a larger system; the compounding effects of small deviations in many components can have a large effect on ultimate system performance.
With this in mind, it can be important to put in place advanced analytical solutions to automatically detect product anomalies and prevent these products from being shipped out, even though their variation(s) may still be within specifications. The last chance to do this is at the final product test point before shipping.
This paper describes a multivariate machine learning solution that has been developed for semiconductor customers using the TIBCO Connected Intelligence Platform. The solution alerts when there are multiple clusters of product with distinctly different test characteristics. The approach outlined has applicability to detect anomalous equipment, processes, and products beyond this specific example.