LAUSR

With the rapid development of the semiconductor industry, fault diagnosis is an important task in routine operations to determine the root cause for faults that occur. A tool in manufacturing processes is equipped with a wide variety of sensors that record different types of process data. Wafers being processed are accompanied by a considerable amount of process data as a multivariate time series. Identifying the key process parameters and the corresponding process operations where faults occur can be used to facilitate the tasks of monitoring the process, maintaining the stability of the process, and stabilizing wafer production yield. This article proposes a novel solution procedure for fault diagnosis of wafer acceptance test (WAT) and chip probing (CP) using machine learning (ML). Based on the process flow of wafers and the corresponding process data, a sampling method, called synthetic minority oversampling technique (SMOTE), is first used to augment classification models with an imbalanced process dataset, and the best-practice SMOTE ratio is sought by using four competitive classifiers. By means of principal component analysis (PCA), the original data are transformed into visualizations to explore the distributions of good and bad lots of wafers. Based on the comparison of the four classifiers used in the test, the proposed logistic regression with data augmentation (LR-SMOTE) performs best. The ten most important features identified by using the LR are collected to determine potential failure operations. The identified failures in operations returned by using the proposed solution procedure for fault diagnosis of WAT and CP were confirmed by the engineers who work in the domain. Note to Practitioners—In semiconductor manufacturing practice, during the WAT and CP, domain engineers need to trace upstream the key process parameters and the corresponding key operations in the front-end-of-line (FEOL) process if any faults are detected in the back-end-of-line (BEOL) process. In fact, the FEOL and the BEOL are located in completely separate plants with different clean room standards. Bridging the information gap between these two different processes merits thorough scrutiny. Another challenge posed by fault diagnosis of WAT and CP is that the total number of features (i.e., process parameters) involved in the FEOL can be tens of thousands, leading to an adverse effect of data sparsity on model building.