Utilizing Generative Adversarial Networks for Image Data Augmentation and Classification of Semiconductor Wafer Dicing Induced Defects
In semiconductor manufacturing, the wafer dicing process is central yet vulnerable to defects that significantly impair yield - the proportion of defect-free chips. Deep neural networks are the current state of the art in (semi-)automated visual inspection. However, they are notoriously known to require a particularly large amount of data for model training. To address these challenges, we explore the application of generative adversarial networks (GAN) for image data augmentation and classification of semiconductor wafer dicing induced defects to enhance the variety and balance of training data for visual inspection systems. With this approach, synthetic yet realistic images are generated that mimic real-world dicing defects. We employ three different GAN variants for high-resolution image synthesis: Deep Convolutional GAN (DCGAN), CycleGAN, and StyleGAN3. Our work-in-progress results demonstrate that improved classification accuracies can be obtained, showing an average improvement of up to 23.1 % from 65.1 % (baseline experiment) to 88.2 % (DCGAN experiment) in balanced accuracy, which may enable yield optimization in production.
Code (0)
등록된 구현이 없습니다.
Tasks
Data AugmentationImage GenerationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Improving Android Malware Detection Through Data Augmentation Using Wasserstein Generative Adversarial Networks
Generative Adversarial Networks (GANs) have demonstrated their versatility across various applications, including data augmentation and malware detection. This research explores the effectiveness of utilizing GAN-generat…
Android Malware DetectionData AugmentationGenerative Adversarial NetworkMalware DetectionCode-switching Sentence Generation by Generative Adversarial Networks and its Application to Data Augmentation
Code-switching is about dealing with alternative languages in speech or text. It is partially speaker-depend and domain-related, so completely explaining the phenomenon by linguistic rules is challenging. Compared to mos…
Data AugmentationGenerative Adversarial NetworkSentenceTTIDA: Controllable Generative Data Augmentation via Text-to-Text and Text-to-Image Models
Data augmentation has been established as an efficacious approach to supplement useful information for low-resource datasets. Traditional augmentation techniques such as noise injection and image transformations have bee…
Data AugmentationDiversitydomain classificationImage CaptioningA Review on Generative Adversarial Networks for Data Augmentation in Person Re-Identification Systems
Interest in automatic people re-identification systems has significantly grown in recent years, mainly for developing surveillance and smart shops software. Due to the variability in person posture, different lighting co…
Data AugmentationPerson Re-IdentificationPose TransferStyle TransferEvaluation of Deep Convolutional Generative Adversarial Networks for data augmentation of chest X-ray images
Medical image datasets are usually imbalanced, due to the high costs of obtaining the data and time-consuming annotations. Training deep neural network models on such datasets to accurately classify the medical condition…
Data AugmentationGenerative Adversarial NetworkMedical Image GenerationTranslation