IH-ViT: Vision Transformer-based Integrated Circuit Appear-ance Defect Detection

• URL: http://arxiv.org/abs/2302.04521v1
• Date: Thu, 9 Feb 2023 09:27:40 GMT
• Title: IH-ViT: Vision Transformer-based Integrated Circuit Appear-ance Defect Detection
• Authors: Xiaoibin Wang, Shuang Gao, Yuntao Zou, Jianlan Guo and Chu Wang
• Abstract summary: We propose an IC appearance defect detection algorithm-rithm IH-ViT. Our proposed model takes advantage of the strengths of CNN and ViT to acquire image features from both local and global aspects. After testing, our proposed hybrid IH-ViT model achieved 72.51% accuracy, which is 2.8% and 6.06% higher than ResNet50 and ViT models alone.
• Score: 5.4641726517633025
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For the problems of low recognition rate and slow recognition speed of traditional detection methods in IC appearance defect detection, we propose an IC appearance defect detection algo-rithm IH-ViT. Our proposed model takes advantage of the respective strengths of CNN and ViT to acquire image features from both local and global aspects, and finally fuses the two features for decision making to determine the class of defects, thus obtaining better accuracy of IC defect recognition. To address the problem that IC appearance defects are mainly reflected in the dif-ferences in details, which are difficult to identify by traditional algorithms, we improved the tra-ditional ViT by performing an additional convolution operation inside the batch. For the problem of information imbalance of samples due to diverse sources of data sets, we adopt a dual-channel image segmentation technique to further improve the accuracy of IC appearance defects. Finally, after testing, our proposed hybrid IH-ViT model achieved 72.51% accuracy, which is 2.8% and 6.06% higher than ResNet50 and ViT models alone. The proposed algorithm can quickly and accurately detect the defect status of IC appearance and effectively improve the productivity of IC packaging and testing companies.

Related papers

• Defect Detection Network In PCB Circuit Devices Based on GAN Enhanced YOLOv11 [1.6775954077761863]
  This study proposes an advanced method for surface defect detection in printed circuit boards (PCBs) using an improved YOLOv11 model enhanced with a generative adversarial network (GAN) The approach focuses on identifying six common defect types: missing hole, rat bite, open circuit, short circuit, burr, and virtual welding. The enhanced YOLOv11 model is evaluated on a PCB defect dataset, demonstrating significant improvements in accuracy, recall, and robustness.
  arXiv  Detail & Related papers  (2025-01-12T17:26:24Z)
• Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models [68.90917438865078]
  Deepfake techniques for facial synthesis and editing pose serious risks for generative models. In this paper, we investigate how detection performance varies across model backbones, types, and datasets. We introduce Contrastive Blur, which enhances performance on facial images, and MINDER, which addresses noise type bias, balancing performance across domains.
  arXiv  Detail & Related papers  (2024-11-28T13:04:45Z)
• 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)
• CINFormer: Transformer network with multi-stage CNN feature injection for surface defect segmentation [73.02218479926469]
  We propose a transformer network with multi-stage CNN feature injection for surface defect segmentation. CINFormer presents a simple yet effective feature integration mechanism that injects the multi-level CNN features of the input image into different stages of the transformer network in the encoder. In addition, CINFormer presents a Top-K self-attention module to focus on tokens with more important information about the defects.
  arXiv  Detail & Related papers  (2023-09-22T06:12:02Z)
• Hybrid-Supervised Dual-Search: Leveraging Automatic Learning for Loss-free Multi-Exposure Image Fusion [60.221404321514086]
  Multi-exposure image fusion (MEF) has emerged as a prominent solution to address the limitations of digital imaging in representing varied exposure levels. This paper presents a Hybrid-Supervised Dual-Search approach for MEF, dubbed HSDS-MEF, which introduces a bi-level optimization search scheme for automatic design of both network structures and loss functions.
  arXiv  Detail & Related papers  (2023-09-03T08:07:26Z)
• 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)
• Lightweight wood panel defect detection method incorporating attention mechanism and feature fusion network [9.775181958901326]
  We propose a lightweight wood panel defect detection method called YOLOv5-LW, which incorporates attention mechanisms and a feature fusion network. The proposed method achieves a 92.8% accuracy rate, reduces the number of parameters by 27.78%, compresses computational volume by 41.25%, improves detection inference speed by 10.16%.
  arXiv  Detail & Related papers  (2023-06-21T08:55:45Z)
• Automatic Error Detection in Integrated Circuits Image Segmentation: A Data-driven Approach [6.420068890493833]
  We present the first data-driven automatic error detection approach targeting two types of IC segmentation errors: wire errors and via errors. On an IC image dataset collected from real industry, we demonstrate that, by adapting existing CNN-based approaches of image classification and image translation with additional pre-processing and post-processing techniques, we are able to achieve recall/precision of 0.92/0.93 in wire error detection and 0.96/0.90 in via error detection, respectively.
  arXiv  Detail & Related papers  (2022-11-08T00:58:10Z)
• Q-ViT: Accurate and Fully Quantized Low-bit Vision Transformer [56.87383229709899]
  We develop an information rectification module (IRM) and a distribution guided distillation scheme for fully quantized vision transformers (Q-ViT) Our method achieves a much better performance than the prior arts.
  arXiv  Detail & Related papers  (2022-10-13T04:00:29Z)
• A New Knowledge Distillation Network for Incremental Few-Shot Surface Defect Detection [20.712532953953808]
  This paper proposes a new knowledge distillation network, called Dual Knowledge Align Network (DKAN) The proposed DKAN method follows a pretraining-finetuning transfer learning paradigm and a knowledge distillation framework is designed for fine-tuning. Experiments have been conducted on the incremental Few-shot NEU-DET dataset and results show that DKAN outperforms other methods on various few-shot scenes.
  arXiv  Detail & Related papers  (2022-09-01T15:08:44Z)
• Improved Transformer for High-Resolution GANs [69.42469272015481]
  We introduce two key ingredients to Transformer to address this challenge. We show in the experiments that the proposed HiT achieves state-of-the-art FID scores of 31.87 and 2.95 on unconditional ImageNet $128 times 128$ and FFHQ $256 times 256$, respectively.
  arXiv  Detail & Related papers  (2021-06-14T17:39:49Z)

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.