🍩 Database of Original & Non-Theoretical Uses of Topology

(found 2 matches in 0.001159s)

  1. A Novel Approach for Wafer Defect Pattern Classification Based on Topological Data Analysis (2023)

    Seungchan Ko, Dowan Koo
    Abstract In semiconductor manufacturing, wafer map defect pattern provides critical information for facility maintenance and yield management, so the classification of defect patterns is one of the most important tasks in the manufacturing process. In this paper, we propose a novel way to represent the shape of the defect pattern as a finite-dimensional vector, which will be used as an input for a neural network algorithm for classification. The main idea is to extract the topological features of each pattern by using the theory of persistent homology from topological data analysis (TDA). Through some experiments with a simulated dataset, we show that the proposed method is faster and much more efficient in training with higher accuracy, compared with the method using convolutional neural networks (CNN) which is the most common approach for wafer map defect pattern classification. Moreover, it was shown that our method outperforms the CNN-based method when the number of training data is not enough and is imbalanced.

  2. A Novel Quality Clustering Methodology on Fab-Wide Wafer Map Images in Semiconductor Manufacturing (2022)

    Yuan-Ming Hsu, Xiaodong Jia, Wenzhe Li, Jay Lee
    Abstract Abstract. In semiconductor manufacturing, clustering the fab-wide wafer map images is of critical importance for practitioners to understand the subclusters of wafer defects, recognize novel clusters or anomalies, and develop fast reactions to quality issues. However, due to the high-mix manufacturing of diversified wafer products of different sizes and technologies, it is difficult to cluster the wafer map images across the fab. This paper addresses this challenge by proposing a novel methodology for fab-wide wafer map data clustering. In the proposed methodology, a well-known deep learning technique, vision transformer with multi-head attention is first trained to convert binary wafer images of different sizes into condensed feature vectors for efficient clustering. Then, the Topological Data Analysis (TDA), which is widely used in biomedical applications, is employed to visualize the data clusters and identify the anomalies. The TDA yields a topological representation of high-dimensional big data as well as its local clusters by creating a graph that shows nodes corresponding to the clusters within the data. The effectiveness of the proposed methodology is demonstrated by clustering the public wafer map dataset WM-811k from the real application which has a total of 811,457 wafer map images. We further demonstrate the potential applicability of topology data analytics in the semiconductor area by visualization.