A Deep Fuzzy Semi-supervised Approach to Clustering and Fault Diagnosis of Partially Labeled Semiconductor Manufacturing Data

Abstract

In the modern age of data collection in manufacturing industry, the sheer volume of measurement data collected may prove difficult for domain experts to create fully labeled training datasets for supervised learning artificial intelligence methods. Semi-supervised learning methods are useful in the realistic scenario where engineers may only be able to annotate limited partial subsets, but existing approaches are limited in scalability for high-dimensional and imbalanced datasets. To address these challenges, a novel framework for semi-supervised learning is proposed that hybridizes 1) convolutional autoencoder as a deep unsupervised feature learning technique; 2) fault classification using principal component analysis-based anomaly scoring; and 3) fuzzy c-means clustering. Fuzzy c-means allows for transparency in the degree of membership to each cluster via the fuzzy partition matrix, enabling an adjustable, explainable, and computationally efficient approach for separating normal and faulty clusters in the same space. This also allows industry experts to review borderline cases, creating a support system to curtail costly labeling expenses. The approach is applied to real, high-dimensional data from a modern semiconductor manufacturing application in which fewer than 1000 out of over 59,000 samples are labeled, achieving AUC scores of over 0.94 for classifying the two labeled fault types as well as successful fuzzy clustering. These results show promise for deep, fuzzy semi-supervised applications to improve decision-making in manufacturing operations and other engineering disciplines.