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TestDNA : Novel Wafer Defect Signature for Diagnosis and Pattern Recognition

The spatial failure patterns in wafer defect maps can be related to problems in the manufacturing and test process. Therefore, failure pattern recognition can be used for root cause analysis, which is very important for defect diagnosis resolution improvement and yield learning. However, previous studies show that wafers with recognizable failure patterns only account for a small part of all defective wafers. In order to further improve diagnosis resolution with data collected from wafer test, we propose a novel way to present results of various test items in order to facilitate feature extraction. The test results are exhibited in a DNA-like signature called TestDNA such that sources of wafer defects can be effectively and efficiently identified. Since more defect information can be incorporated into a TestDNA, more defect types of wafer defects can be identified and predicted with higher accuracy. Experimental results show that the proposed TestDNA method can be used to identify and predict more defect types such that both higher diagnosis resolution and prediction accuracy are achieved.

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Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING; Vol.33 No.3 August 2020

No. Panggil

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Penerbit

: .,

Deskripsi Fisik

p. 383 - 390

Bahasa

English

ISBN/ISSN

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Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

-

Edisi

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Subjek

YIELD LEARNING
WAFER TEST
WAFER DEFECT MAP
WAFER DEFECT PATTERN
WAFER DEFECT DIAGNOSIS
TEST PROBE

Info Detail Spesifik

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Pernyataan Tanggungjawab

Katherine Shu-Min Li, Nova Cheng-Yen Tsai, Ken Chau-Cheung Cheng, Xu-Hao Jiang, Peter Yi-Yu Liao, Sying-Jyan Wang, Andrew Yi-Ann Huang, Leon Chou, Chen-Shiun Lee

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