IDENTIFICATION OF RISK DEVICES USING INDEPENDENT COMPONENT ANALYSIS FOR SEMICONDUCTOR MEASUREMENT DATA
Keywords:Screening, Independent Component Analysis, Outlier Detection, Semiconductors
Semiconductor devices must fulfill highest quality standards since they are used in safety relevant applications. Bad devices, namely devices which are not fully functional after the production are scrapped immediately and not delivered to the customer. Unfortunately, among the remaining ones there are still devices with increased risk, prone to infant mortality. To minimize the chance of delivering such risk devices, statistical screening methods are applied to detect suspicious devices, represented as statistical outliers, based on Front-End data. Dependent on the technology, different measurements are suitable for screening. Nevertheless, it is assumed that the measurements contain not only signal noise but also a hidden signal identifying risk devices. Therefore, this paper proposes to use the Independent Component Analysis as a data transformation to separate informative and non-informative content from the measurements.
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