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

TL;DR

This paper introduces an automated method for measuring head overcoat thickness in TEM images using a NASNet-Large-based segmentation model and a novel post-processing step, significantly improving accuracy and efficiency over manual methods.

Contribution

It presents the first use of NASNet-Large for HOC segmentation and introduces a post-processing layer to enhance segmentation accuracy, along with a regressive CNN for thickness measurement.

Findings

Higher dice score achieved compared to previous methods

Lower mean squared error in thickness estimation

Outperforms manual measurement in accuracy

Abstract

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.