Semiconductor manufacturing process monitoring using gaussian mixture model and Bayesian method with local and nonlocal information

IEEE Transactions on Semiconductor Manufacturing

Volumn 25, Issue 3, 2012, Pages 480-493

  Yu, Jianbo ^a  

^a SHANGHAI UNIVERSITY   (China)

Author keywords

Bayesian inference; fault detection; Gaussian mixture model (GMM); local and nonlocal preserving projection (LNPP); semiconductor manufacturing process

Indexed keywords

ADVANCED PROCESS CONTROL; BATCH PROCESS; BAYESIAN INFERENCE; BAYESIAN METHODS; CONTRIBUTION ANALYSIS; ETCH PROCESS; EUCLIDEAN SPACES; GAUSSIAN MIXTURE MODEL; GLOBAL STRUCTURE; HIGH-DIMENSIONAL; LINEAR EMBEDDING; MAHALANOBIS DISTANCES; MANIFOLD LEARNING ALGORITHM; MONITORING MODELS; MULTI-MODAL; MULTIMODAL FEATURES; NON-LINEARITY; NONLOCAL; NONLOCAL INFORMATION; PROCESS DATA; PROCESS FAILURE; PROCESS FAULTS; PROCESS STATE; PROCESS VARIABLES; PRODUCT QUALITY; SEMICONDUCTOR INDUSTRY; SEMICONDUCTOR MANUFACTURING; SEMICONDUCTOR MANUFACTURING PROCESS;

BATCH DATA PROCESSING; BAYESIAN NETWORKS; FEATURE EXTRACTION; INDUSTRIAL ENGINEERING; INFERENCE ENGINES; INTELLIGENT CONTROL; LEARNING ALGORITHMS; PRINCIPAL COMPONENT ANALYSIS; PROCESS CONTROL; PROCESS MONITORING; PRODUCTION ENGINEERING; SEMICONDUCTOR DEVICE MANUFACTURE;

FAULT DETECTION;

EID: 84864696252     PISSN: 08946507     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSM.2012.2192945     Document Type: Article

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