Data Balancing and Aggregation Strategy to Predict Yield in Hard Disk Drive Manufacturing

Abstract

Hard disk drive manufacturing is complicated and involves several steps of assembling and testing. Poor yield in one step can result in fail product of the whole lot. Accurate yield prediction is thus important to product monitoring and management. This paper presents a novel idea of data preparation and modeling to predict yield in the process of hard disk drive production. Data balancing technique based on clustering and re-sampling is introduced to make the proportion of the pass and fail products comparable. Then, we propose a strategy to aggregate manufacturing data to be in a reasonable group size and efficient for the subsequent step of yield predictive model creation. Experimental results reveal that grouping data into a constant size of 10,000 records can lead to the more accurate yield prediction as compared to the intuitive idea of weekly grouping.