Elman Neural Network–Based Dynamic Scheduling of Wafer Photolithography Process

Bing Hai Zhou

doi:10.4028/www.scientific.net/AMR.186.36

Paper Titles

Surface Texture Detection for Composite Material Placement Based on Vision
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Simulation on Electro-Hydraulic Proportional Position Control System under Pressure Boundary Conditions
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Study in Stereovision Obstacle Avoidance for Adsorption Type Wall-Climbing Robot with Several Legs
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Empirically-Sourced Mechanical Behaviors of Pneumatic Artificial Muscles for Compensation-Based Controls
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Elman Neural Network–Based Dynamic Scheduling of Wafer Photolithography Process
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Analysis of Ceramic Pattern Case
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PSO and WCR-Based Scheduling Algorithm for Non-Identical Semiconductor Batch Processing Devices
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Dynamic Analysis of Cutter Head System in Tunnel Boring Machine
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Video Vehicle Detection Based on Local Feature
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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 186Elman Neural Network–Based Dynamic Scheduling of...

Elman Neural Network–Based Dynamic Scheduling of Wafer Photolithography Process

Abstract:

Photolithography area is usually a bottleneck area in a semiconductor wafer manufacturing system (SWMS). It is difficult to schedule photolithography area on real-time optimally. Here, an Elman neural network (ENN)-based dynamic scheduling method is proposed. An ENN-based sample learning algorithm is proposed for selecting best combination of scheduling rules. To illustrate the feasibility and practicality of the presented method, the simulation experiment is developed. A numerical example is use to evaluate the proposed method. Results of simulation experiments show that the proposed method is effective to schedule a complex wafer photolithography process.