Abstract—Deployment of machine learning techniques are prevailing in world-wide problem solving. Hard disk drive manufacturing is another prominent field seeking for the application of these knowledge intensive techniques. The manufacturing tasks that urgently require support from machine learning are in the portions of failure analysis and yield improvement. We focus our research on the yield improvement sector. Manufacturing yield prediction opens big opportunity for machine learning application because yield is a very important metric in many parts of manufacturing process. But, there rarely is research work about yield prediction in hard disk drive manufacturing found until today. So, we introduce yield prediction improvement by statistical analysis and machine learning methods including the multiple linear regression (MLR), artificial neural networks (ANN), classification and regression tree (CART). Moreover, we introduce technique to group quantity of data for yield prediction by considering consistency number, instead of grouping by calendar period as used in traditional method. The result of our technique shows the better performance. Means absolute error (MAE) of our proposal is 0.010 with a tide error rate produced by MLR and CART algorithms. The best performance from traditional calendar-based grouping is ANN algorithm with the error metric 0.017 MAE.
Index Terms—Yield prediction, hard disk drive (HDD), multiple linear regression (MLR), artificial neural network (ANN), classification and regression tree (CART).
The authors are with the School of Computer Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand (e-mail: firstname.lastname@example.org, email@example.com, firstname.lastname@example.org).
Cite: Anusara Hirunyawanakul, Nittaya Kerdprasop, and Kittisak Kerdprasop, "Efficient Machine Learning Methods for Hard Disk Drive Yield Prediction Improvement," International Journal of Machine Learning and Computing vol. 10, no. 2, pp. 240-246, 2020.Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).