Cycle time analysis of dual-arm cluster tools for wafer fabrication processes with multiple wafer revisiting times

2015 ◽  
Vol 53 ◽  
pp. 252-260 ◽  
Author(s):  
Yan Qiao ◽  
NaiQi Wu ◽  
QingHua Zhu ◽  
LiPing Bai
Keyword(s):  
Cycle Time ◽  
Wafer Fabrication ◽  
Time Analysis ◽  
Cluster Tools ◽  
Dual Arm

Capacity Planning for Cluster Tools of Wafer Fabrication Plant

2010 ◽  
Vol 450 ◽  
pp. 365-368
Author(s):  
James C. Chen ◽  
Chia Wen Chen ◽  
Kou Huang Chen ◽  
Chien Hsin Lin
Keyword(s):  
Capacity Planning ◽  
Production Costs ◽  
Wafer Fabrication ◽  
Total Production ◽  
Output Rate ◽  
Cluster Tools ◽  
And Performance ◽  
Using Data ◽  
Capital Intensive

Wafer fabrication is a capital intensive industry. A 12-inch wafer fabrication plant needs a typical investment of US$ 3 billion, and the equipment cost constitutes about two-thirds to three-quarters of the total production costs. Therefore, capacity planning is crucial to the investment and performance of wafer fabrication plants. Several formulae are presented to calculate the required number of machines with sequential, parallel, and batch processing characteristics, respectively. An AutoSched AP simulation model using data from real foundry fabrication plants is used in a case study to evaluate the performance of the proposed formulae. Simulation results indicate that the proposed formulae can quickly and accurately calculate the required number of cluster tools leading to the required monthly output rate.

Fuzzy-neural-network-based fluctuation smoothing rule for reducing the cycle times of jobs with various priorities in a wafer fabrication plant: A simulation study

2009 ◽  
Vol 223 (8) ◽  
pp. 1033-1043 ◽  
Author(s):  
T Chen
Keyword(s):  
Neural Network ◽  
Cycle Time ◽  
Fuzzy Neural Network ◽  
Optimal Solution ◽  
Wafer Fabrication ◽  
Estimation Accuracy ◽  
Cycle Times ◽  
Fuzzy Neural ◽  
The Difference ◽  
Fluctuation Smoothing

This paper presents a fuzzy-neural-network-based fluctuation smoothing rule to further improve the performance of scheduling jobs with various priorities in a wafer fabrication plant. The fuzzy system is modified from the well-known fluctuation smoothing policy for a mean cycle time (FSMCT) rule with three innovative treatments. First, the remaining cycle time of a job is estimated by applying an existing fuzzy-neural-network-based approach to improve the estimation accuracy. Second, the components of the FSMCT rule are normalized to balance their importance. Finally, the division operator is applied instead of the traditional subtraction operator in order to magnify the difference in the slack and to enhance the responsiveness of the FSMCT rule. To evaluate the effectiveness of the proposed methodology, production simulation is applied to generate some test data. According to the experimental results, the proposed methodology outperforms six existing approaches in the reduction of the average cycle times. In addition, the new rule is shown to be a Pareto optimal solution for scheduling jobs in a semiconductor manufacturing plant.

Wafer Residency Time Analysis for Time-Constrained Single-Robot-Arm Cluster Tools With Activity Time Variation

2020 ◽  
Vol 28 (4) ◽  
pp. 1177-1188 ◽  
Author(s):  
Fajun Yang ◽  
Xin Tang ◽  
Naiqi Wu ◽  
Chunjiang Zhang ◽  
Liang Gao
Keyword(s):  
Time Variation ◽  
Robot Arm ◽  
Time Analysis ◽  
Activity Time ◽  
Cluster Tools ◽  
Residency Time
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