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Critical Dimension measurement in the final slider-level fabrication is essential in the development and manufacture of magnetic read/write heads for hard disk drives (HDD). It validates the device level geometry that plays a dominant role in the magnetic performance of the writer and provides critical feedback to the wafer-level head fabrication process control. Measurement at the slider-level affords the true Air Bearing Surface (ABS) view of the real device that can only be approached by the destructional cross-section at wafer level [1,2]. While the large set of CDSEM images of writer ABS at slider level enables an excellent statistical view of wafer uniformity, it also poses special challenges to the metrology due to a substantial number of variations from the upstream wafer process. The large structure variations observed at the sliderlevel is particularly prevalent in the initial development phase where large DOE (Design of Experiment) are designed to produce intended structure variations, and low process maturity yields large unintended variations among the devices. A traditional metrology used in such a variant data set requires extensive tuning or even a set of separate solutions with each solution in the set only applicable to a small subset of the variations. However, this approach is inefficient and demands high engineer resources. In this work, we use a machine learning based metrology approach to process the large set of magnetic writer device images at the slider level. For the current study, we use a model-based solution that was trained with deep learning (DL) using a dataset from 4 different head designs. The model aims to retrieve precise boundaries of the head to perform accurate measurements. We demonstrate the progressive robustness of the model-based solution by expanding the training set to measure the CD of writer poles with different designs and large process variations due to the intrinsic wafer level structure variation and the image distortions from the slider fabrication process. In addition, we will demonstrate the efficiency of the Deep Learning (DL) based solution in comparison with traditional metrology and manual measurements on the same set of data.
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