Decision tree algorithm (DT) is a commonly used data mining method for classification and regression. DT repeatedly divides a dataset into pure subsets based on impurity measurements such as entropy and Gini. Then relatively “pure” partitions consisting of observations with the (almost) same class are obtained. Gini index is one of the representative indices for measuring the impurity of data. However, the Gini index does not take into account distances between classes. If the distances between classes are considered when measuring impurity, the decision tree algorithm can distinguish clearly observations with different classes. To the end, a new decision tree algorithm based on Rao-Stirling index is proposed considering distances between classes. Rao-Stirling index considers distances between classes in such a way that weights more to pairs of references in more distant classes when measuring data impurity. Experimental results indicate that the proposed method is superior in terms of accuracy, implying that considering the distances between classes can help improve accuracy in DT.

Semiconductor manufacturing comprises hundreds of consecutive unit processes. A single misprocess could jeopardize the whole manufacturing process. In current manufacturing environments, data monitoring of equipment condition, wafer metrology, and inspection, etc., are used to probe any anomaly during the manufacturing process that could affect the final chip performance and quality. The purpose of investigation is fault detection and classification (FDC). Various methods, such as statistical or data mining methods with machine learning algorithms, have been employed for FDC. In this paper, we propose an artificial immune system (AIS), which is a biologically inspired computing algorithm, for FDC regarding semiconductor equipment. Process shifts caused by parts and modules aging over time are main processes of failure cause. We employ state variable identification (SVID) data, which contain current equipment operating condition, and optical emission spectroscopy (OES) data, which represent plasma process information obtained from faulty process scenario with intentional modification of the gas flow rate in a semiconductor fabrication process. We achieved a modeling prediction accuracy of modeling of 94.69% with selected SVID and OES and an accuracy of 93.68% with OES data alone. To conclude, the possibility of using an AIS in the field of semiconductor process decision making is proposed.

Research papers are a repository of information on the various elements that make up science and technology R&D activities. Generating knowledge maps based on research papers enables identification of specific areas of scientific and technical research as well as understanding of the flow of knowledge between those areas. Recently, as the number of electronic publishing and informatics archives along with the amount of accumulated knowledge related to science and technology has proliferated, the need to utilize the meta-knowledge obtainable from research papers has increased. Therefore, this study devised a model based on meta-knowledge (i.e., text information including citations, abstracts, area codes) for prediction of future growth potential using deep learning algorithms and investigated the applicability of the various forms of meta-knowledge to the prediction of future growth potential. It also proposes how to select the promising technology clusters based on the proposed model.

Classifying wafer defects in the wafer manufacturing process is increasingly critical for ensuring high-quality production, optimizing processes, and reducing costs. Most existing methods for wafer map defect classification primarily rely on images alone for model training and prediction. However, these approaches often lack interpretability, which can hinder process improvement and problem-solving efforts. In other words, existing methods only calculate the probability of a specific image belonging to each class, making it difficult to visually judge why the image belongs to a particular class. Additionally, these methods make it challenging to assess the distance of new images from each class. Furthermore, it is difficult to obtain representative images of each class. To address these limitations, we propose a novel approach for wafer defect classification using contrastive learning with label embedding. The proposed method aims to map label information and wafer defect images into a shared latent space through contrastive learning using label embedding. This not only facilitates defect class prediction from images but also enhances interpretability by visualizing relationships between images and defects (labels) and providing representative defect images. Moreover, compared to previous methods, our approach demonstrates better classification performance and computational efficiency, even in situations with imbalanced labels. This method also shows significant potential in identifying unseen defects not defined in the original classification tasks. Consequently, the proposed approach extends its applicability beyond wafer map defect patterns, showing promising potential for use in various domains.

SeJoon Park, NagYoon Song, Wooyeon Yu, and Dohyun Kim. IJFIS 2019;19:307-14. https://doi.org/10.5391/IJFIS.2019.19.4.307