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Data Visualization of Anomaly Detection in Semiconductor Processing Tools.
- Source :
-
IEEE Transactions on Semiconductor Manufacturing . May2022, Vol. 35 Issue 2, p186-197. 12p. - Publication Year :
- 2022
-
Abstract
- Semiconductor manufacturing plays a crucial role in the world’s economic growth and technology development and is the backbone of the high value-added electronic device manufacturing industry. In this paper, a new anomaly detection framework by means of data visualization is proposed for semiconductor manufacturing. Firstly, t-Distributed Stochastic Neighbor Embedding (t-SNE) in unsupervised learning is used to transform the high-dimensional raw trace data, corresponding to normal wafers, into a two-dimensional map, with the purpose of visually observing the distribution of normal wafers. The t-SNE algorithm cannot be used at run time for a new test sample since it requires the whole dataset for the embedding transformation, and is computationally very expensive. The Multilayer Perceptron (MLP) neural network is then applied as a regressor for the real-time t-SNE embedding of a new test data. The envelope of t-SNE score estimates for a set of normal wafers is circumscribed and used as the 2D control boundary based on the Delaunay Triangulation (D.T.). A new test sample with its MLP estimated embedding points outside the D.T boundary is identified as defective. Lastly, a real-world dataset in semiconductor manufacturing is used to illustrate the proposed data visualization tool for anomaly detection. The experimental results show that a multilayer perceptron in combination with t-SNE and Delaunay Triangulation performs very well for data visualization and automated detection of anomalies. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 08946507
- Volume :
- 35
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Semiconductor Manufacturing
- Publication Type :
- Academic Journal
- Accession number :
- 156741929
- Full Text :
- https://doi.org/10.1109/TSM.2021.3137982