Bottleneck is the key factor to semiconductor wafer fabrication system (SWFS)

which seriously influences the level of work-in-process

cycle time

time-delivery rate

etc.Efficient analysis for the bottleneck of SWFS can promote various performances.In modern SWFS

present analysis methods usually regard bottleneck device as static bottleneck without taking bottleneck shifting into consideration in the uncertain environment

which leads to scheduling algorithm that always treat the bottleneck device as the core lack of flexibility and real-time performance.Therefore

dynamic bottleneck analysis method for the SWFS based on growing and pruning neural networks (GPNN) was adopted in this study to acquire the dynamic bottleneck characteristic.Firstly

in this paper

the way of composite definition is used to calculate comprehensive bottleneck degree of the devices form the perspectives of relative production load

utilization rate and length of the buffer queue to indicate bottleneck based on bottleneck identification mechanism;Secondly

establish the model of growing and pruning neural networks to predict the future bottleneck and adjust the network structure in view of closed-loop control.Thirdly

in order to analyze the key factors relative to bottleneck devices and the dynamic bottleneck characteristic quantitatively

the single factor test method was applied in this paper.Lastly

the experiments show that this dynamic bottleneck analysis method is testified the feasibility and availability.