Skip to Main Content
Skip Nav Destination
ASME Press Select Proceedings

Intelligent Engineering Systems through Artificial Neural Networks

Editor
Cihan H. Dagli
Cihan H. Dagli
Search for other works by this author on:
K. Mark Bryden
K. Mark Bryden
Search for other works by this author on:
Steven M. Corns
Steven M. Corns
Search for other works by this author on:
Mitsuo Gen
Mitsuo Gen
Search for other works by this author on:
Kagan Tumer
Kagan Tumer
Search for other works by this author on:
Gürsel Süer
Gürsel Süer
Search for other works by this author on:
ISBN:
9780791802953
No. of Pages:
636
Publisher:
ASME Press
Publication date:
2009

Product types in the hard disk drive (HDD) industry have different specifications depending on the customer orders. These specifications along with the machine parameters have a direct impact on the production yield. The problems on the manufacturing line are called the “root cause”. By accurately identifying the root cause, the engineers can suggest yield improvement solutions. Thus, the overall framework for the automatic solution generation is needed by several HDD companies. Our research is a part of such a framework. In this paper, we focus on the prediction technique required at the end of the analysis steps in order to validate the suggested solution by simulation. We adapt the Stochastic Neural Networks (SNNs) for using with the HGA yield prediction. The inputs of our model consist of several machine parameters and specification attributes. Our version of SNNs can approximate a complex non—linear system. The genetic algorithm is used as a learning algorithm instead of the back-propagation method in order to handle the non-linear and stochastic relationships between input parameters. Our prediction model can then be used to validate and revise the yield improvement plan. The output of the prediction model is the yield rate. The model can be used as a simulation tool for yield improvement without having to actually implement the solution on the production line.

Abstract
Introduction
Related Research
The Yield Prediction Model
Experiments and Results
Conclusions and Future Work
Acknowledgements
References
This content is only available via PDF.
You do not currently have access to this chapter.
Close Modal

or Create an Account

Close Modal
Close Modal