Bottleneck Prediction Method Based on Improved Adaptive Network-based Fuzzy Inference System (ANFIS) in Semiconductor Manufacturing System<sup>*</sup>

曹政才; 邓积杰; 刘民; 王永吉

Your Location：

Home >

Browse articles >

Bottleneck Prediction Method Based on Improved Adaptive Network-based Fuzzy Inference System (ANFIS) in Semiconductor Manufacturing System^*

Updated：2026-01-06

- Bottleneck Prediction Method Based on Improved Adaptive Network-based Fuzzy Inference System (ANFIS) in Semiconductor Manufacturing System^*
- Chinese Journal of Chemical Engineering Vol. 20, Issue 6, Pages: 1081-1088(2012)
- Affiliations：
  
  1. College of Information Science and Technology, Beijing University of Chemical Technology,Beijing,China,100029
  2. Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University,Nanjing,China,210096
  3. Tsinghua National Laboratory for Information Science and Technology,Beijing,China,100084
  4. State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences,Beijing,China,100190
- Author bio：
- Funds：
- DOI：
  CLC：
- Published：2012
- Accepted：
Scan QR Code
[J]. Chinese Journal of Chemical Engineering, 2012, 20(6): 1081-1088.
DOI：

[J]. Chinese Journal of Chemical Engineering, 2012, 20(6): 1081-1088. DOI：

摘要

Abstract

Semiconductor manufacturing (SM) system is one of the most complicated hybrid processes involved continuously variable dynamical systems and discrete event dynamical systems.The optimization and scheduling of semiconductor fabrication has long been a hot research direction in automation.Bottleneck is the key factor to a SM system

which seriously influences the throughput rate

cycle time

time-delivery rate

etc

.Efficient prediction for the bottleneck of a SM system provides the best support for the consequent scheduling.Because categorical data (product types

releasing strategies) and numerical data (work in process

processing time

utilization rate

buffer length

etc

.) have significant effect on bottleneck

an improved adaptive network-based fuzzy inference system (ANFIS) was adopted in this study to predict bottleneck since conventional neural network-based methods accommodate only numerical inputs.In this improved ANFIS

the contribution of categorical inputs to firing strength is reflected through a transformation matrix.In order to tackle high-dimensional inputs

reduce the number of fuzzy rules and obtain high prediction accuracy

a fuzzy c-means method combining binary tree linear division method was applied to identify the initial structure of fuzzy inference system.According to the experimental results

the main-bottleneck and sub-bottleneck of SM system can be predicted accurately with the proposed method.

关键词

Keywords

references

Views

250

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

Unraveling the methane decomposition mechanism on Fe-Ni-Ca-O composite catalysts: A combined density functional theory and microdynamic study

CO2 leakage behavior from CO2 hydrate-liquid CO2-seawater coexistence system in unsealed sediments

CO2 capture by ternary ionic liquid non-aqueous phase change solvents

Mechanochemical preparation and properties of B-N doped carbon materials based on calcium carbide

Controllable synthesis of Ni3S2/V3S4@NF-xF(x=0, 4, 8) catalysts for efficient hydrogen production in seawater and urea electrolyte

Related Author

No data

Related Institution

No data

⁰