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Data storage capacity of hard drive disks (HDD) has been increased over time. Partly this has been achieved by shrinking the dimensions of magnetic writing device also called the “main Pole”, a key magnetic flux emanating component in the slider that hovers over the magnetic media disk writing bits in nanoscale magnetic domains along the circular tracks. Even though the writer pole device is a single isolated device, its 3D shape and material composition are quite complex and critical dimensions are sub-50 nm. The reliability and accuracy of writing data in bits of magnetic nano-domains depends on the precise control of the pole shape and dimensions. Its fabrication requires over thousand process steps with many of them being various types of metrology steps. Final shape or back-end shape of the pole is influenced by various process steps and related information is captured by the subsequent metrology. Overlay, optical (thin film thickness using ellipsometry, topography using white light interferometry), CD-SEM, CD-AFM and inline FIB cross sections based metrology is commonly used. All this metrology data during fabrication process is called wafer metrology data. The data about the shape of the pole after device fabrication (lapping) is called back-end data and is obtained by cross section using FIB and SEM. Few device chips are sent ahead to determine the lapping parameters which takes time. This paper is about predicting the back-end shape parameters namely, pole height (PH), pole width (PW), and pole angle (PA), based on wafer data so that the backend metrology process requiring send-ahead can be optimized or even eliminated. Predictive models using machine learning and analytics techniques (neural networks, multivariate regression, and principal components analysis) have been studied and results will be presented and discussed in this paper. In this metrology centric data science study various steps have been pursued from the beginning to retrieve, integrate, transform, model, visualize data and understand the outcomes. Wafer data corresponding to more than seventy different parameters was used in this study. It is not possible to have all metrology data for each device as some techniques are destructive, imputation technique based on nearest neighbour data points is used. Model was trained and validated on a set of data and tested on an independent new data. Based on testing on independent data, study concludes that it is possible to train the model on data from few wafers for technology in production and predict the back-end pole shape parameters with acceptable accuracy for upcoming wafers. This could be very useful in reducing cycle time by minimizing the need for send ahead wafer components and optimizing the tuning of lapping process. Correlation of predicted (Y-axis) and measured (X-axis) values of PW parameters for four wafers is plotted in figure 1 at device level, flash field or exposure field level, and wafer level. The correlation between predicted and measured PW is reasonable for making use of the predictive model at the flash field level and wafer level to optimize the need for send ahead and determine the lapping parameters at back-end process.
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