Utilizing Generative Adversarial Networks for Image Data Augmentation and Classification of Semiconductor Wafer Dicing Induced Defects

• URL: http://arxiv.org/abs/2407.20268v1
• Date: Wed, 24 Jul 2024 20:44:16 GMT
• Title: Utilizing Generative Adversarial Networks for Image Data Augmentation and Classification of Semiconductor Wafer Dicing Induced Defects
• Authors: Zhining Hu, Tobias Schlosser, Michael Friedrich, André Luiz Vieira e Silva, Frederik Beuth, Danny Kowerko,
• Abstract summary: In semiconductor manufacturing, the wafer dicing process is central yet vulnerable to defects that significantly impair yield. Deep neural networks are the current state of the art in (semi-)automated visual inspection. We explore the application of generative adversarial networks (GAN) for image data augmentation and classification of semiconductor wafer dicing induced defects.
• Score: 0.21990652930491852
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Leveraging Latent Diffusion Models for Training-Free In-Distribution Data Augmentation for Surface Defect Detection [9.784793380119806]
  We introduce DIAG, a training-free Diffusion-based In-distribution Anomaly Generation pipeline for data augmentation. Unlike conventional image generation techniques, we implement a human-in-the-loop pipeline, where domain experts provide multimodal guidance to the model. We demonstrate the efficacy and versatility of DIAG with respect to state-of-the-art data augmentation approaches on the challenging KSDD2 dataset.
  arXiv  Detail & Related papers  (2024-07-04T14:28:52Z)
• DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perception [78.26734070960886]
  Current perceptive models heavily depend on resource-intensive datasets. We introduce perception-aware loss (P.A. loss) through segmentation, improving both quality and controllability. Our method customizes data augmentation by extracting and utilizing perception-aware attribute (P.A. Attr) during generation.
  arXiv  Detail & Related papers  (2024-03-20T04:58:03Z)
• Forgery-aware Adaptive Transformer for Generalizable Synthetic Image Detection [106.39544368711427]
  We study the problem of generalizable synthetic image detection, aiming to detect forgery images from diverse generative methods. We present a novel forgery-aware adaptive transformer approach, namely FatFormer. Our approach tuned on 4-class ProGAN data attains an average of 98% accuracy to unseen GANs, and surprisingly generalizes to unseen diffusion models with 95% accuracy.
  arXiv  Detail & Related papers  (2023-12-27T17:36:32Z)
• DifAugGAN: A Practical Diffusion-style Data Augmentation for GAN-based Single Image Super-resolution [88.13972071356422]
  We propose a diffusion-style data augmentation scheme for GAN-based image super-resolution (SR) methods, known as DifAugGAN. It involves adapting the diffusion process in generative diffusion models for improving the calibration of the discriminator during training. Our DifAugGAN can be a Plug-and-Play strategy for current GAN-based SISR methods to improve the calibration of the discriminator and thus improve SR performance.
  arXiv  Detail & Related papers  (2023-11-30T12:37:53Z)
• Attention Modules Improve Image-Level Anomaly Detection for Industrial Inspection: A DifferNet Case Study [2.2942964892621807]
  We propose a DifferNet-based solution enhanced with attention modules: AttentDifferNet. It improves image-level detection and classification capabilities on three visual anomaly detection datasets for industrial inspection. Our evaluation shows an average improvement - compared to DifferNet - of 1.77 +/- 0.25 percentage points in overall AUROC.
  arXiv  Detail & Related papers  (2023-11-05T19:48:50Z)
• Leveraging Neural Radiance Fields for Uncertainty-Aware Visual Localization [56.95046107046027]
  We propose to leverage Neural Radiance Fields (NeRF) to generate training samples for scene coordinate regression. Despite NeRF's efficiency in rendering, many of the rendered data are polluted by artifacts or only contain minimal information gain.
  arXiv  Detail & Related papers  (2023-10-10T20:11:13Z)
• Wafer Map Defect Patterns Semi-Supervised Classification Using Latent Vector Representation [8.400553138721044]
  The demand for defect detection during integrated circuit fabrication stages is becoming increasingly critical. Traditional wafer map defect pattern detection methods involve manual inspection using electron microscopes. We propose a model capable of automatically detecting defects as an alternative to manual operations.
  arXiv  Detail & Related papers  (2023-10-06T08:23:36Z)
• Automated Semiconductor Defect Inspection in Scanning Electron Microscope Images: a Systematic Review [4.493547775253646]
  Machine learning algorithms can be trained to accurately classify and locate defects in semiconductor samples. Convolutional neural networks have proved to be particularly useful in this regard. This systematic review provides a comprehensive overview of the state of automated semiconductor defect inspection on SEM images.
  arXiv  Detail & Related papers  (2023-08-16T13:59:43Z)
• SEMI-DiffusionInst: A Diffusion Model Based Approach for Semiconductor Defect Classification and Segmentation [0.11999555634662631]
  This work is the first demonstration to accurately detect and precisely segment semiconductor defect patterns by using a diffusion model. Our proposed approach outperforms previous work on overall mAP and performs comparatively better or as per for almost all defect classes.
  arXiv  Detail & Related papers  (2023-07-17T17:53:36Z)
• Defect Classification in Additive Manufacturing Using CNN-Based Vision Processing [76.72662577101988]
  This paper examines two scenarios: first, using convolutional neural networks (CNNs) to accurately classify defects in an image dataset from AM and second, applying active learning techniques to the developed classification model. This allows the construction of a human-in-the-loop mechanism to reduce the size of the data required to train and generate training data.
  arXiv  Detail & Related papers  (2023-07-14T14:36:58Z)
• FD-GAN: Generative Adversarial Networks with Fusion-discriminator for Single Image Dehazing [48.65974971543703]
  We propose a fully end-to-end Generative Adversarial Networks with Fusion-discriminator (FD-GAN) for image dehazing. Our model can generator more natural and realistic dehazed images with less color distortion and fewer artifacts. Experiments have shown that our method reaches state-of-the-art performance on both public synthetic datasets and real-world images.
  arXiv  Detail & Related papers  (2020-01-20T04:36:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.