Machine learning-based novelty detection for faulty wafer detection in semiconductor manufacturing

Expert Systems with Applications

Volumn 39, Issue 4, 2012, Pages 4075-4083

  Kim, Dongil ^a   Kang, Pilsung ^d   Cho, Sungzoon ^a   Lee, Hyoung Joo ^b   Doh, Seungyong ^c  

^a Seoul National University   (South Korea)

^b UNIVERSITY OF OXFORD   (United Kingdom)

^c SAMSUNG Electronics   (South Korea)

^d Seoul National University of Science and Technology   (South Korea)

Author keywords

Dimensionality reduction; Faulty wafer detection; Novelty detection; Semiconductor manufacturing; Virtual metrology

Indexed keywords

DETECTION SYSTEM; DIMENSIONALITY REDUCTION; DIMENSIONALITY REDUCTION METHOD; FAULT DETECTION AND CLASSIFICATION; INPUT VARIABLES; NOVELTY DETECTION; REAL WORLD DATA; SEMICONDUCTOR FABS; SEMICONDUCTOR MANUFACTURING; SINGLE VARIABLE; TRUE POSITIVE RATES; UNIMODAL; VIRTUAL METROLOGY;

FAULT DETECTION; LEARNING SYSTEMS; MANUFACTURE; REAL VARIABLES; SEMICONDUCTOR DEVICE MANUFACTURE; STATISTICAL PROCESS CONTROL; UNITS OF MEASUREMENT;

SILICON WAFERS;

EID: 82255179131     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2011.09.088     Document Type: Article

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