e-journal

Semiconductor Manufacturing Process Monitoring Using Gaussian Mixture Model and Bayesian Method With Local and Nonlocal Information

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Abstract—Fault detection has been recognized in the semiconductor industry as an effective component of advanced process control framework in increasing yield and product quality. Principal component analysis (PCA) has been applied widely to semiconductor manufacturing process monitoring. However,
the unique characteristics of semiconductor processes—high dimension of data, nonlinearity in most batch processes, and multimodal batch trajectories due to multiple operating conditions—significantly limit applicability of PCA to semiconductor manufacturing. This paper proposes a manifold learning algorithm, local and nonlocal preserving projection (LNPP), for feature extraction. Different from PCA, which aims to discover the global structure of Euclidean space, LNPP can find a good linear embedding
that preserves local and nonlocal information. This may enable LNPP to find meaningful low-dimensional information hidden in high-dimensional observations. The Gaussian mixture model (GMM) is applied to handle process data with nonlinearity or multimodal features. GMM-based Mahalanobis distance is
proposed to assess process states, and a Bayesian inference-based method is proposed to provide the process failure probability. A variable replacing-based contribution analysis method is developed
to identify the process variables that are responsible for the onset of process fault. The proposed monitoring model is demonstrated through its application to a batch semiconductor etch process.

Index Terms—Bayesian inference, fault detection, Gaussian mixture model (GMM), local and nonlocal preserving projection (LNPP), semiconductor manufacturing process.

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Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING, VOL. 25, NO. 3, AUGUST 2012

No. Panggil

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Penerbit

: IEEE., 2012

Deskripsi Fisik

IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING, VOL. 25, NO. 3, AUGUST 2012

Bahasa

English

ISBN/ISSN

0894-6507

Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

VOL. 25, NO. 3, AUGUST 2012

Subjek

SEMIKONDUKTOR

Info Detail Spesifik

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Pernyataan Tanggungjawab

ETY

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