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Data Analytics in Test: Recognizing and Reducing Subjectivity


Applying data analytics in production test has become a widely adopted industrial practice in recent years. As the complexity of semiconductor devices scales and the amounts of available test data continue to grow, the research direction in this field is forced to shift away from solving specific problems with ad hoc approaches and demands for deeper understanding of the fundamental issues. Two data-driven test applications where this shift is apparent are production yield optimization and defect screening, where the respective underlying data analytics approaches are correlation analysis and outlier analysis. A core issue present in these two approaches stems from the subjectivity that is inherent to data analytics. This dissertation delves into how subjectivity manifests itself and what can be done to reduce it with respect to the two test applications.

Outlier analysis is an approach used for identifying anomalies. The main goal of outlier analysis in test is to capture statistically outlying parts with the hope that their abnormal behavior is attributed to some defectivity. During creation of an outlier model, the decisions about outlying behavior in the existing data are made by utilizing known failures and the test engineer's best judgment. In practice, outlier screening methods are simply used for transforming data into an outlier score space. Even if outlier analysis techniques are able to successfully classify a dataset into inliers and outliers, outlier models require thresholds to be decided. A concept called Consistency is introduced to provide an objective data-driven way to evaluate outlier models by utilizing all available data. The key observation underlying this concept is that outlier analysis should be immune to noise introduced by sources of systematic variation.

Correlation analysis is a process comprising a search for related variables. The application of production yield optimization involves searching for correlation between the yield and various controllable parameters. The goal of this process is to uncover parameters that, when adjusted, can result in yield improvement. This analytics process is subjective to the perspective of the analyst and the quality of the result is highly dependent on the analyst’s previous experiences. In order to reduce the subjectivity in this application, a process mining methodology is introduced to learn from the experiences of analysts. The key advantage of this methodology is that in addition to having the capability to record and reproduce these analyses, it can also generalize to analytics processes not contained in the learned experiences.

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