Manufacturing intelligence to forecast and reduce semiconductor cycle time

Journal of Intelligent Manufacturing

Volumn 23, Issue 6, 2012, Pages 2281-2294

  Chien, Chen Fu ^a   Hsu, Chia Yu ^b   Hsiao, Chih Wei ^a  

^a NATIONAL TSING HUA UNIVERSITY   (Taiwan)

^b YUAN ZE UNIVERSITY   (Taiwan)

Author keywords

Back propagation neural network; Cycle time; Gauss Newton regression; Manufacturing intelligence; Semiconductor; Work in process (WIP)

Indexed keywords

ADAPTIVE MODELS; BACK PROPAGATION NEURAL NETWORKS; BASIC MODELS; COMPETITIVE ADVANTAGE; CYCLE TIME; DEMAND CHANGES; DYNAMIC JOB ARRIVAL; EMPIRICAL STUDIES; FABRICATION PROCESS; FORECAST ACCURACY; GAUSS-NEWTON; HSINCHU; INDENTIFYING; INPUT FACTORS; PRODUCTION CYCLE; PRODUCTION LINE; PRODUCTION PROCESS; RAPID RESPONSE; RECIRCULATIONS; REGRESSION METHOD; SCIENCE PARK; SEMI-CONDUCTOR FABRICATION; SEMICONDUCTOR COMPANIES; SEMICONDUCTOR FOUNDRIES; SEMICONDUCTOR MANUFACTURING; SHIFTING BOTTLENECK; TIME TO MARKET; WAFER FABRICATIONS; WORK IN PROCESS;

COMPETITION; CONSUMER ELECTRONICS; FABRICATION; FORECASTING; NEURAL NETWORKS; SEMICONDUCTOR DEVICE MANUFACTURE; SEMICONDUCTOR MATERIALS; TORSIONAL STRESS;

THROUGHPUT;

EID: 84870865605     PISSN: 09565515     EISSN: 15728145     Source Type: Journal    
DOI: 10.1007/s10845-011-0572-y     Document Type: Article

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