Fault detection using principal components-based gaussian mixture model for semiconductor manufacturing processes

IEEE Transactions on Semiconductor Manufacturing

Volumn 24, Issue 3, 2011, Pages 432-444

  Yu, Jianbo ^a  

^a SHANGHAI UNIVERSITY   (China)

Author keywords

Fault detection; Gaussian mixture model; multivariate statistical process control; principal component analysis; semiconductor manufacturing process

Indexed keywords

ADVANCED PROCESS CONTROL; BATCH PROCESS; CALCULATION METHODS; EARLY DETECTION; GAUSSIAN COMPONENTS; GAUSSIAN MIXTURE MODEL; LOG LIKELIHOOD; MAHALANOBIS DISTANCES; MANUFACTURING PROCESS; MONITORING MODELS; MULTI-MODAL; MULTIMODAL FEATURES; MULTIVARIATE STATISTICAL PROCESS CONTROL; NON-LINEARITY; OPERATING CONDITION; PRINCIPAL COMPONENTS; PROCESS FAILURE; PROCESS STATE; PRODUCT QUALITY; SEMICONDUCTOR INDUSTRY; SEMICONDUCTOR MANUFACTURING; SEMICONDUCTOR MANUFACTURING PROCESS; SEMICONDUCTOR PROCESS; SIMULATION PROCESS;

BATCH DATA PROCESSING; BAYESIAN NETWORKS; COMPUTER SIMULATION; FAULT DETECTION; GAUSSIAN DISTRIBUTION; INDUSTRIAL APPLICATIONS; INDUSTRIAL ENGINEERING; INFERENCE ENGINES; INTELLIGENT CONTROL; MULTIVARIANT ANALYSIS; NANOELECTRONICS; PROCESS MONITORING; PRODUCTION ENGINEERING; QUALITY CONTROL; SEMICONDUCTOR DEVICE MANUFACTURE; STATISTICAL PROCESS CONTROL;

PRINCIPAL COMPONENT ANALYSIS;

EID: 80051513708     PISSN: 08946507     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSM.2011.2154850     Document Type: Article

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