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Data Visualization of Anomaly Detection in Semiconductor Processing Tools

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.

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Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING; Vol.35 No.2 May 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 186-197

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

MACHINE LEARNING
ANOMALY DETECTION
DATA VISUALIZATION
SEMICONDUCTOR MANUFACTURING
ONE-CLASS MODEL

Info Detail Spesifik

DOI: 10.1109/TSM.2021.3137982

Pernyataan Tanggungjawab

Shu-Kai S. Fan, Du-Ming Tsai, Chih-Hung Jen, Chia-Yu Hsu, Fei He, Li-Ting Juan

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