Automatic Head Overcoat Thickness Measure with NASNet-Large-Decoder Net
This work addresses a domain-specific need in the magnetic hard disk drive industry for precise and automated film thickness measurement, representing an incremental improvement over existing methods.
The paper tackles the problem of automating head overcoat (HOC) thickness measurement from TEM images, which is time-consuming and subjective when done manually, by proposing a segmentation method using NASNet-Large with a decoder and a post-processing layer, achieving a higher dice score and lower mean squared error compared to state-of-the-art manual measurement.
Transmission electron microscopy (TEM) is one of the primary tools to show microstructural characterization of materials as well as film thickness. However, manual determination of film thickness from TEM images is time-consuming as well as subjective, especially when the films in question are very thin and the need for measurement precision is very high. Such is the case for head overcoat (HOC) thickness measurements in the magnetic hard disk drive industry. It is therefore necessary to develop software to automatically measure HOC thickness. In this paper, for the first time, we propose a HOC layer segmentation method using NASNet-Large as an encoder and then followed by a decoder architecture, which is one of the most commonly used architectures in deep learning for image segmentation. To further improve segmentation results, we are the first to propose a post-processing layer to remove irrelevant portions in the segmentation result. To measure the thickness of the segmented HOC layer, we propose a regressive convolutional neural network (RCNN) model as well as orthogonal thickness calculation methods. Experimental results demonstrate a higher dice score for our model which has lower mean squared error and outperforms current state-of-the-art manual measurement.