e-journal

Wafer Map Failure Pattern Recognition and Similarity Ranking for Large-Scale Data Sets

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Abstract—Wafer maps can exhibit specific failure patterns that provide crucial details for assisting engineers in identifying the cause of wafer pattern failures. Conventional approaches of wafer map failure pattern recognition (WMFPR) and wafer map similarity ranking (WMSR) generally involve applying raw wafer map data (i.e., without performing feature extraction). However, because increasingly more sensor data are analyzed during semiconductor fabrication, currently used approaches can be inadequate in processing large-scale data sets. Therefore, a set of novel rotation- and scale-invariant features is proposed for obtaining a reduced representation of wafer maps. Such features are crucial when employing WMFPR and WMSR to analyze large-scale data sets. To validate the performance of the proposed
system, the world’s largest publicly accessible data set of wafer maps was built, comprising 811 457 real-world wafer maps. The experimental results show that the proposed features and overall system can process large-scale data sets effectively and efficiently, thereby meeting the requirements of current
semiconductor fabrication.

Index Terms—Data models, image recognition, information retrieval, pattern recognition, semiconductor defects.

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Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING, VOL. 28, NO. 1, FEBRUARY 2015

No. Panggil

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Penerbit

: IEEE., 2015

Deskripsi Fisik

IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING, VOL. 28, NO. 1, FEBRUARY 2015

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. 28, NO. 1, FEBRUARY 2015

Subjek

SEMIKONDUKTOR

Info Detail Spesifik

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Pernyataan Tanggungjawab

ETY

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