[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122755-en":3,"doc-seo-122755-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":21,"html_lang":23,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},122755,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Data Augmentation GUI Tool for Machine Learning Models - Master Thesis","Semiconductor assembly production requires rigorous component testing and quality assurance, but manual QA increases costs over time. Machine-learning methods can automate part of this testing by enabling effective image classification with deep neural networks, particularly convolutional neural networks. However, limited datasets cause overfitting, and domain-specific constraints may restrict conventional augmentation methods. This thesis builds a MATLAB-based data augmentation GUI using ultrasonic microscope images provided by Infineon Technologies AG to expand training data while improving robustness and accuracy-time trade-offs, supporting accessible QA workflows.","Data Augmentation GUI Tool for Machine  \nLearning Models  \nMaster Thesis  \nSubmitted in Fulfilment of the Requirements for the Academic Degree  \nM.Sc. Embedded Systems  \nDept. of Computer Science Chair of Computer Engineering  \nSubmitted by: Sweta Sharma  \nStudent ID: 565350  \nDate: 07.12.2022  \nSupervising tutor: Prof. Dr. W. Hardt Julkar Nine  \nAbstract  \nThe 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.  \nThe accomplishment of the project, the efficacy with which it is carried out, and its level of quality may be dependent on a number offactors; 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 easyto-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.  \nKeywords: Graphical User Interface (GUI), MATLAB, Data Augmentation, Image Processi","cbCainqX2gvA2UJz","https://ap.wps.com/l/cbCainqX2gvA2UJz","pdf",5233202,1,105,"English","en","# Introduction\n## Infineon Technologies AG\n## Description of Manufacturing process\n## Motivation\n## Problem\n# Data Augmentation and ML Approach\n## GUI Design for Image Processing\n## Augmentation Techniques (Zoom, Rotation, GAN, Autoencoder)\n# Results and Quality Assurance Integration\n## Dataset Expansion and Model Robustness","[{\"question\":\"Why is data augmentation needed for the semiconductor image classification task?\",\"answer\":\"Deep neural networks require sufficient training data, and small datasets can lead to overfitting. Data augmentation artificially creates additional training images to improve generalization when real data is limited in semiconductor production contexts.\"},{\"question\":\"What dataset was used for this thesis?\",\"answer\":\"Infineon Technologies AG provided ultrasonic microscope images of semiconductor components. The images include a sufficient number of good components and a minority of rejected components, where rejected components contain defects that failed quality control.\"},{\"question\":\"What is the main contribution of the thesis?\",\"answer\":\"The thesis demonstrates and develops a user-friendly MATLAB graphical user interface that incorporates traditional, advanced, and smart data augmentation methods and image-processing/ML workflows to streamline quality assurance and classification.\"}]","Data Augmentation GUI Tool for Machine Learning Models - Master Thesis | PDF",1785812734,265,{"code":4,"msg":30,"data":31},"ok",{"site_id":21,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"data-augmentation-gui-tool-for-machine-learning-models-master-thesis","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/data-augmentation-gui-tool-for-machine-learning-models-master-thesis/122755/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why is data augmentation needed for the semiconductor image classification task?","Question",{"text":74,"@type":75},"Deep neural networks require sufficient training data, and small datasets can lead to overfitting. Data augmentation artificially creates additional training images to improve generalization when real data is limited in semiconductor production contexts.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What dataset was used for this thesis?",{"text":79,"@type":75},"Infineon Technologies AG provided ultrasonic microscope images of semiconductor components. The images include a sufficient number of good components and a minority of rejected components, where rejected components contain defects that failed quality control.",{"name":81,"@type":72,"acceptedAnswer":82},"What is the main contribution of the thesis?",{"text":83,"@type":75},"The thesis demonstrates and develops a user-friendly MATLAB graphical user interface that incorporates traditional, advanced, and smart data augmentation methods and image-processing/ML workflows to streamline quality assurance and classification.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":21},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":105,"slug":137},19,"General","general"]