The industrial production of semiconductor assemblies is subject to high requirements. As a result, several tests are needed in terms of component quality. In the long run, manual quality assurance (QA) is often connected with higher expenditures. Using a technique based on machine learning, some of these tests may be carried out automatically. Deep neural networks (NN) have shown to be very effective in a diverse range of computer vision applications. Especially convolutional neural networks (CNN), which belong to a subset of NN, are an effective tool for image classification. Deep NNs have the disadvantage of requiring a significant quantity of training data to reach excellent performance. When the dataset is too small a phenomenon known as overfitting can occur. Massive amounts of data cannot be supplied in certain contexts, such as the production of semiconductors. This is especially true given the relatively low number of rejected components in this field. In order to prevent overfitting, a variety of image augmentation methods may be used to the process of artificially creating training images. However, many of those methods cannot be used in certain fields due to their inapplicability. For this thesis, Infineon Technologies AG provided the images of a semiconductor component generated by an ultrasonic microscope. The images can be categorized as having a sufficient number of good and a minority of rejected components, with good components being defined as components that have been deemed to have passed quality control and rejected components being components that contain a defect and did not pass quality control.
The accomplishment of the project, the efficacy with which it is carried out, and its level of quality may be dependent on a number of factors; however, selecting the appropriate tools is one of the most important of these factors because it enables significant time and resource savings while also producing the best results. We demonstrate a data augmentation graphical user interface (GUI) tool that has been widely used in the domain of image processing. Using this method, the dataset size has been increased while maintaining the accuracy-time trade-off and optimizing the robustness of deep learning models. The purpose of this work is to develop a user-friendly tool that incorporates traditional, advanced, and smart data augmentation, image processing,
and machine learning (ML) approaches. More specifically, the technique mainly uses
are zooming, rotation, flipping, cropping, GAN, fusion, histogram matching,
autoencoder, image restoration, compression etc. This focuses on implementing and
designing a MATLAB GUI for data augmentation and ML models. The thesis was
carried out for the Infineon Technologies AG in order to address a challenge that all
semiconductor industries experience. The key objective is not only to create an easy-
to-use GUI, but also to ensure that its users do not need advanced technical
experiences to operate it. This GUI may run on its own as a standalone application.
Which may be implemented everywhere for the purposes of data augmentation and
classification. The objective is to streamline the working process and make it easy to
complete the Quality assurance job even for those who are not familiar with data
augmentation, machine learning, or MATLAB. In addition, research will investigate the
benefits of data augmentation and image processing, as well as the possibility that
these factors might contribute to an improvement in the accuracy of AI models.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:87790 |
| Date | 30 October 2023 |
| Creators | Sharma, Sweta |
| Contributors | Hardt, Wolfram, Nine, Julkar, Technische Universität Chemnitz |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:masterThesis, info:eu-repo/semantics/masterThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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