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Feature Selection to Improve Performance of Yield Prediction in Hard Disk Drive Manufacturing

Anusara Hirunyawanakul1, Nuntawut Kaoungku 2, Nittaya Kerdprasop2, and Kittisak Kerdprasop 2
1. School of Computer Engineering, Suranaree University of Technology, Thailand
2. Data and Knowledge Engineering Research Unit, Suranaree University of Technology, Thailand

Abstract—Hard Disk Drive (HDD) manufacturing is one real-world application area that machine learning has been extensively adopted for problem solving. However, most problem solving activities in HDD industry tackle on failure root-cause analysis task. Machine learning is rarely applied in a task of yield prediction. This research presents the application of machine learning and statistical techniques to select appropriate features to be used in yield prediction for the HDD manufacturing process. The seven well-known algorithms are used in the feature selection step. These algorithms are decision tree (C5 and CART), Support Vector Machine (SVM), stepwise regression, Genetic Algorithm (GA), chi-square and information gain. The two prominent learning algorithms, Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN), are used in the yield prediction modeling step. Yield prediction performance has been assessed based on the two evaluation metrics: Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Yield prediction with MLR shows higher accuracy than yield estimation traditionally performed by human engineers. Resulting to conclusion that the proposed novel learning steps can help HDD process engineers to predict yield with the better performance, especially on applying GA as feature selection tool, the MAE is reduced from 0.014 (yield estimated by human engineer) to 0.0059 (yield predicted by MLR). That means error reduction is about 60%.

Index Terms—Artificial neural network, feature selection, genetic algorithm, hard disk drive, multiple linear regression, yield prediction

Cite: Anusara Hirunyawanakul, Nuntawut Kaoungku, Nittaya Kerdprasop, and Kittisak Kerdprasop, "Feature Selection to Improve Performance of Yield Prediction in Hard Disk Drive Manufacturing," International Journal of Electrical and Electronic Engineering & Telecommunications, Vol. 9, No. 6, pp. 420-428, November 2020. Doi: 10.18178/ijeetc.9.6.420-428

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.