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Transmission electron microscopy (TEM) is one of the primary tools to showmicrostructural characterization of materials as well as film thickness.However, manual determination of film thickness from TEM images istime-consuming as well as subjective, especially when the films in question arevery thin and the need for measurement precision is very high. Such is the casefor head overcoat (HOC) thickness measurements in the magnetic hard disk driveindustry. It is therefore necessary to develop software to automaticallymeasure HOC thickness. In this paper, for the first time, we propose a HOClayer segmentation method using NASNet-Large as an encoder and then followed bya decoder architecture, which is one of the most commonly used architectures indeep learning for image segmentation. To further improve segmentation results,we are the first to propose a post-processing layer to remove irrelevantportions in the segmentation result. To measure the thickness of the segmentedHOC layer, we propose a regressive convolutional neural network (RCNN) model aswell as orthogonal thickness calculation methods. Experimental resultsdemonstrate a higher dice score for our model which has lower mean squarederror and outperforms current state-of-the-art manual measurement.

Automatic Head Overcoat Thickness Measure with NASNet-Large-Decoder Net