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Multiblock Principal Component Analysis Based on a Combined Index for Semiconductor Fault Detection and Diagnosis

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Abstract—The purposes of multivariate statistical process control (MSPC) are to improve process operations by quickly detecting when process abnormalities have occurred and diagnosing the sources of the process abnormalities. In the area of semiconductor manufacturing, increased yield and improved product quality result from reducing the amount of wafers produced under suboptimal operating conditions. This paper presents a complete MSPC application method that combines recent contributions
to the field, including multiway principal component analysis (PCA), recursive PCA, fault detection using a combined index, and fault contributions from Hotelling’s 2 statistic. In addition, a method for determining multiblock fault contributions to the combined index is introduced. The effectiveness of the system is demonstrated using postlithography metrology data and plasma stripper processing tool data.

Index Terms—Combined index, contribution plots, fault detection, fault diagnosis, multiblock principal component analysis, recursive principal component analysis.

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Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING, VOL. 19, NO. 2, MAY 2006

No. Panggil

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Penerbit

: IEEE., 2006

Deskripsi Fisik

IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING, VOL. 19, NO. 2, MAY 2006

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. 19, NO. 2, MAY 2006

Subjek

SEMIKONDUKTOR

Info Detail Spesifik

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Pernyataan Tanggungjawab

ETY

Versi lain/terkait

Lampiran Berkas

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