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Title: A step towards automated TEM and SEM characterisation using deep learning : nano-tetrahedrons
Authors: Tang, Si An
Keywords: Engineering::Materials
Issue Date: 2019
Abstract: Convolutional neural networks (CNNs) have attracted huge amount of attentions since the emergence of deep learning, because of their ability to learn and adapt features directly from the input data, and obtain accurate classification. Although CNNs have resulted in a variety of advances in fields particular for image recognition, microscopic images such as TEM and SEM images in materials science are essentially different problem compared to other applications of deep learning. In addition, limited research studies exist to direct the design of CNN architectures for such microscopic images, and it is unclear that whether CNN can be used for this application. In this work, we have investigated the design of a CNN model for images of common nanostructures, in particular, tetrahedron. We have shown that CNN is capable of classifying tetrahedron images out of similar shapes, which is cube and octahedron. Through the project, the important and commonly tuned parameters, such as batch size, number of convolutional layers, pooling layers, activation layers were exploited and discussed with the aim of getting an optimised model. Additionally, we have concluded the optimal number of iterations or epochs to train a CNN model for the application. Apart from that, we have proven that a dropout layer is necessary at the end of the model. We have also concluded that RMSprop is a better choice of optimiser compared to Stochastic Gradient Descend (SGD). This work suggested a CNN model for the classification of tetrahedrons, which serves as an extremely crucial starting point of realisation of automated characterisation in TEM and SEM equipment in the near future.
Schools: School of Materials Science and Engineering 
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:MSE Student Reports (FYP/IA/PA/PI)

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