Wafer

This dataset was originally formatted by R. Olszewski as part of his thesis Generalized feature extraction for structural pattern recognition in time-series data at Carnegie Mellon University, 2001. Wafer data relates to semi-conductor microelectronics fabrication. A collection of inline process control measurements recorded from various sensors during the processing of silicon wafers for semiconductor fabrication constitute the wafer database; each data set in the wafer database contains the measurements recorded by one sensor during the processing of one wafer by one tool. The two classes are normal and abnormal.

This is a modified, multivariate version of the dataset saved in numpy format. The original dataset is univariate and it was obtained from here.

The dataset consists of multi-variate time series contained in an array of shape [n_samples, time_steps, n_variables] associated with class labels contained in an array of shape [n_samples, 1]. It can be loaded as follows:

loaded_data = np.load("Wafer.npz")
Xtr = loaded_data['Xtr'] # Training data of shape (298, 198, 6)
Ytr = loaded_data['Ytr'] # Training labels of shape (289, 1)
Xte = loaded_data['Xte'] # Test data of shape (896, 198, 6)
Yte = loaded_data['Yte'] # Test labels of shape (896, 1)