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Optimal Feature Selection for Defect Classification in Semiconductor Wafers

Semiconductors are essential components in many electronic devices. Because wafers are produced quickly and in large quantities, defects occur that adversely affect semiconductor properties. This makes it necessary to install powerful and robust inspection systems which use artificial intelligence techniques in the early stages of the manufacturing chain in order to detect and classify those defects. This paper proposes a method for defect detection and classification on images of semiconductor wafer materials obtained by means of a scanning electron microscope based in the following stages: (i) use of computer vision techniques to isolate the defect from the background; (ii) use of several descriptors based on shape, size, texture, histogram, and key-points to create a feature vector for the characterization of the defect; (iii) application of an exhaustive search as a feature selection method to determine the optimal subset of feature descriptors; and (iv) evaluation of the feature descriptors by using a support vector machine classifier providing the optimal set with highest F1-score metrics. Finally, the effectiveness of the proposed approach is compared with five popular feature selection methods, reporting better classification results than the latter.

Ketersediaan

Informasi Detail

Judul Seri

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

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 324-331

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

MACHINE LEARNING
FEATURE SELECTION
SUPPORT VECTOR MACHINE
EXHAUSTIVE SEARCH
DEFECT CLASSIFICATION
SEMICONDUCTOR MANUFACTURING

Info Detail Spesifik

DOI: 10.1109/TSM.2022.3146849

Pernyataan Tanggungjawab

Jose L. Gomez-Sirvent, Francisco Lopez de la Rosa, Roberto Sanchez-Reolid, Antonio Fernandez-Caballero, Rafael Morales

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