Generalized zero-shot learning for classifying unseen wafer map patterns

Abstract

A data-driven approach for classifying defect patterns in wafer maps is crucial for automating the quality assurance process during semiconductor fabrication. Existing works have proposed machine learning-based methods to accomplish this important problem. However, many of these approaches assume model training from a supervised learning perspective, in which a substantial number of annotated wafer maps, each with its actual defect pattern, need to be available. Due to the cost and time required to generate such an annotated dataset, recently a few works started to explore the problem of identifying previously unseen defect patterns. However, the methods proposed in those works are able to only detect the presence of unseen defect patterns without identifying what those defect patterns are. To address this issue, this paper introduces a generalized zero-shot learning (GZSL)-based framework capable of classifying and identifying both seen and unseen defect patterns. It consists of a visual feature extraction phase based on semi-supervised contrastive learning (CL), a clustering phase using distance-based initialization, and an alignment phase based on the Wasserstein-Distance-based Visual Structure Constraint method. Our framework further reduces the need for expensive annotated datasets previously required to train supervised learning-based models, thereby improving the practicality of the data-driven approach for wafer map pattern classification. Furthermore, our framework introduces an effective way of incorporating additional semantic information in wafer map pattern classification that traditionally relied on training models solely from wafer maps. To the best of the authors’ knowledge, our work is the first work to adopt GZSL in wafer map pattern classification.