  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.
