Neural network modeling of fabrication yield using manufacturing data

Mevawalla, Z. N.; May, G.S.; Honjo, Masahiro; Kiehlbauch, Mark

doi:10.1109/asmc.2011.5898198

Cited by 4 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some researches focus on how to establish an exact prediction model for KPI. Mevawalla et al used three neural networks to forecast yield [1] , it could get better forecasting accuracy compared with the traditional statistical methods. But the sequential neural network model, which was constituted by two neural networks, was too complex.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Prediction for Multiple Key Performance Indicators in Semiconductor Wafer Fabrication

2016

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Simultaneous Prediction for Multiple Key Performance Indicators in Semiconductor Wafer Fabrication

2016

View full text Add to dashboard Cite

“…ANNs have been used to solve numerous problems associated with manufacturing operations. Application of ANN in manufacturing system design [2]- [6], manufacturing process control [7], robot scheduling [8], industrial pattern recognition [9], and manufacturing operational decision [10] is reported.…”

Section: Introductionmentioning

confidence: 99%

Application of a Coupled Simulation-Optimization System Called AnyPLOS in a Cold Foam Production Line

Jahanpour¹,

Tahouri²

2013

IJMO

View full text Add to dashboard Cite

Abstract-A prototype simulation-optimization system called AnyPLOS, which couples an Artificial Neural Network (ANN) based simulation model with a genetic algorithm optimization model, is presented. AnyPLOS is designed to discover value of effective input parameters of a production line so that all required quality control tests on the output product is satisfied. First an ANN was trained and tested to provide an acceptable level of accuracy in prediction of production line outputs, and then it was coupled with a GA optimization module to find desired solutions. A real world case study, in Erish Khodro manufacturing company, was set up and the foam production process input parameters were optimized so that the produced samples satisfied quality requirements. In order to verify the results, discovered solutions were used to produce real foam samples in the production line. After that, quality control tests were performed on samples. Quality test results were, as predicted by ANN, within the desired range. In order to estimate the performance of the trained ANN, experimental observations were compared to values which were predicted by ANN. A convincing correlation was found between ANN predictions and experimental values.

show abstract

An overview of the use of neural networks for data mining tasks

Stahl

Jordanov

2012

WIREs Data Min & Knowl

View full text Add to dashboard Cite

In the recent years, the area of data mining has been experiencing considerable demand for technologies that extract knowledge from large and complex data sources. There has been substantial commercial interest as well as active research in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from large datasets. Artificial neural networks (NNs) are popular biologically-inspired intelligent methodologies, whose classification, prediction, and pattern recognition capabilities have been utilized successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction, and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks.

show abstract

Neural network modeling of fabrication yield using manufacturing data

Cited by 4 publications

References 26 publications

Simultaneous Prediction for Multiple Key Performance Indicators in Semiconductor Wafer Fabrication

Simultaneous Prediction for Multiple Key Performance Indicators in Semiconductor Wafer Fabrication

Application of a Coupled Simulation-Optimization System Called AnyPLOS in a Cold Foam Production Line

An overview of the use of neural networks for data mining tasks

Contact Info

Product

Resources

About