ANFIS and SA Based Approach to Prediction, Scheduling, and Performance Evaluation for Semiconductor Wafer Fabrication

CAO Zhengcai; ZHAO Huidan; WANG Yongji

Article Contents

Article Navigation > Chinese Journal of Electronics > 2013 > 22(1): 25-30

CAO Zhengcai, ZHAO Huidan, WANG Yongji. ANFIS and SA Based Approach to Prediction, Scheduling, and Performance Evaluation for Semiconductor Wafer Fabrication[J]. Chinese Journal of Electronics, 2013, 22(1): 25-30.

Citation:

CAO Zhengcai, ZHAO Huidan, WANG Yongji. ANFIS and SA Based Approach to Prediction, Scheduling, and Performance Evaluation for Semiconductor Wafer Fabrication[J]. Chinese Journal of Electronics, 2013, 22(1): 25-30.

Citation:

PDF( 357 KB)

ANFIS and SA Based Approach to Prediction, Scheduling, and Performance Evaluation for Semiconductor Wafer Fabrication

1.
College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China;
2.
State Key Laboratory of Manufacturing Systems Engineering, Xi’an Jiaotong University Xi’an 710054, China;
3.
State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China

Funds: This work is supported by the New Scholars Program of Doctoral Fund of Ministry of Education (No.20090010120011), the Open Project Program of the State Key of CAD&CG (No.A1120); the Open Project Program of the State Key Lab of Industrial Control Technology (No.ICT1108) and the Foundation of Key Laboratory of System Control and Information Processing, Ministry of Education of China (No.SCIP2011005).

Received Date: 2011-11-01
Rev Recd Date: 2012-01-01
Publish Date: 2013-01-05

Abstract

Abstract

In this paper, an optimized mechanism for Semiconductor wafer fabrication (SWF) by integrating the Adaptive neuro-fuzzy inference system (ANFIS) with Simulated annealing (SA) algorithm is proposed. In this approach, aiming to solve the rush order problem which significantly affect the cycle time and impact the Work in process (WIP) of lots due to the high priority, we build an ANFIS based prediction model which will be embedded into releasing to forecast product codes and quantities of the contingent rush orders. Then a scheduler based on SA algorithm is constructed, the coding of which represents a combination of scheduling policies, including lot releasing policies, dispatching rules, batching rules and settingup rules. When the SA finished its optimization process, an optimal scheduling policy is produced. By using the proposed approach??we will find that the system can be optimized to a large extent and give a better performance.
- Semiconductor manufacturing system,
- Scheduling,
- Adaptive neuro-fuzzy inference system,
- Simulated annealing algorithm

FullText(HTML)

References(11)

References

Z.C. Cao, H.X. Yu, F. Qiao, “Petri-net and GA-based approachto modeling and optimize for semiconductor wafer fabrication”,Acta Electronica Sinica, Vol.38, No.2, pp.340-244, 2010. (inChinese)

T. Chen, “An optimized tailored nonlinear fluctuation smoothingrule for scheduling a semiconductor manufacturing factory”,Computers and Industrial Engineering, Vol.58, No.2, pp.317-325, 2010.

C.L Chen, “A heuristic model for justifying the acceptance ofrush orders in multi-plants manufacturing supply chain withoutsourcing”, Proceedings of 8th IEEE International Conferenceon Industrial Informatics, Osaka, pp.607-611, 2010.

C.Y.Wang, G.S. Liu, M.C. Chen et al., “A supply chain networksystem optimum model for rush orders production decision inglobal manufacturing”, Proceedings of 8th IEEE InternationalConference on Industrial Informatics, Osaka, pp.617-622, 2010.

Y.F. Lee, Z.B. Jiang, H.R. Liu, “Multiple-objective schedulingand real-time dispatching for the semiconductor manufacturingsystem”, Computers and Industrial Engineering, Vol.36, No.3,pp.866-884, 2009.

M. Liu, C. Wu, “Genetic algorithm using sequence rule chainfor multi-objective optimization in re-entrant micro-electronicproduction line”, Robotics and Computer-Integrated Manufacturing,Vol.20, No.3, pp.225-236, 2004.

Q. Kang, H. Xiao, L. Wang, “A swarm-dynamic schedulingmethod for semiconductor assembly production line”, Proceedingsof 4th IEEE Conference on Automation Science and Engineering,Washington, DC, pp.91-96, 2008.

K. Woo, S. Park, S. Fujimura, “Real-time buffer managementmethod for DBR scheduling”, International Journal of ManufacturingTechnology and Management, Vol.16, No.1-2, pp.42-57, 2009.

B. Naderi, R.T. Moghaddm, M. Khalili, “Electromagnetismlikemechanism and simulated annealing algorithms for flowshopscheduling problems minimizing the total weighted tardinessand makespan”, Knowledge-Based Systems, Vol.23, No.2,pp.77-85, 2010.

M. Liu, C. Wu, “Identical parallel machine scheduling problemfor minimizing the makespan using genetic algorithm combinedby simulated annealing”, Chinese Journal of Electronics, Vol.7,No.4, pp.317-321, 1998.

F.D. Chou, H.M. Wang, P.C. Chang, “A simulated annealingapproach with probability matrix for semiconductor dynamicscheduling problem”, Expert Systems with Applications, Vol.35,No.4, pp.1889-1898, 2008.

Relative Articles

Supplements(0)

Cited By

Proportional views