IEEE Transactions on Semiconductor Manufacturing · 2000 · 230 citations · 22 references
ComputingComputer ScienceManufacturing EngineeringArtificial IntelligenceComputer VisionMachine LearningDefect DetectionDefectsFault DetectionFault DiagnosisNeural NetworksPattern RecognitionFabricationMachine Learning-based Defect DetectionNeural Network ClassificationNeural Network-based DiagnosisSelf-organizing MapsSemiconductor Wafer InspectionSpatial Distribution PatternsUnsupervised Neural Learning
Yield enhancement is critical in semiconductor fabrication, and wafer defects—especially when they form spatial patterns—are a major cause of yield loss and signal equipment or process issues. The study aims to develop an intelligent system that recognizes defect spatial patterns to diagnose failure causes. The system uses an adaptive resonance theory network (ART1) trained on real wafer defect data from a Taiwanese manufacturer and is compared with a self‑organizing map (SOM). ART1 outperforms SOM in accurately recognizing similar defect spatial patterns.
Yield enhancement in semiconductor fabrication is important. Even though IC yield loss may be attributed to many problems, the existence of defects on the wafer is one of the main causes. When the defects on the wafer form spatial patterns, it is usually a clue for the identification of equipment problems or process variations. This research intends to develop an intelligent system, which will recognize defect spatial patterns to aid in the diagnosis of failure causes. The neural-network architecture named adaptive resonance theory network 1 (ART1) was adopted for this purpose. Actual data obtained from a semiconductor manufacturing company in Taiwan were used in experiments with the proposed system. Comparison between ART1 and another unsupervised neural network, self-organizing map (SOM), was also conducted. The results show that ART1 architecture can recognize the similar defect spatial patterns more easily and correctly.
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