Deep Learning based Defect classification and detection in SEM images: A Mask R-CNN approach

• URL: http://arxiv.org/abs/2211.02185v1
• Date: Thu, 3 Nov 2022 23:26:40 GMT
• Title: Deep Learning based Defect classification and detection in SEM images: A Mask R-CNN approach
• Authors: Bappaditya Dey, Enrique Dehaerne, Kasem Khalil, Sandip Halder, Philippe Leray, and Magdy A. Bayoumi
• Abstract summary: We have demonstrated the application of Mask-RCNN (Regional Convolutional Neural Network), a deep-learning algorithm for computer vision. We are aiming at detecting and segmenting different types of inter-class defect patterns such as bridge, break, and line collapse.
• Score: 2.7180863515048674
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this research work, we have demonstrated the application of Mask-RCNN (Regional Convolutional Neural Network), a deep-learning algorithm for computer vision and specifically object detection, to semiconductor defect inspection domain. Stochastic defect detection and classification during semiconductor manufacturing has grown to be a challenging task as we continuously shrink circuit pattern dimensions (e.g., for pitches less than 32 nm). Defect inspection and analysis by state-of-the-art optical and e-beam inspection tools is generally driven by some rule-based techniques, which in turn often causes to misclassification and thereby necessitating human expert intervention. In this work, we have revisited and extended our previous deep learning-based defect classification and detection method towards improved defect instance segmentation in SEM images with precise extent of defect as well as generating a mask for each defect category/instance. This also enables to extract and calibrate each segmented mask and quantify the pixels that make up each mask, which in turn enables us to count each categorical defect instances as well as to calculate the surface area in terms of pixels. We are aiming at detecting and segmenting different types of inter-class stochastic defect patterns such as bridge, break, and line collapse as well as to differentiate accurately between intra-class multi-categorical defect bridge scenarios (as thin/single/multi-line/horizontal/non-horizontal) for aggressive pitches as well as thin resists (High NA applications). Our proposed approach demonstrates its effectiveness both quantitatively and qualitatively.

Related papers

• LADMIM: Logical Anomaly Detection with Masked Image Modeling in Discrete Latent Space [0.0]
  Masked image modeling is a self-supervised learning technique that predicts the feature representation of masked regions in an image. We propose a novel approach that leverages the characteristics of MIM to detect logical anomalies effectively. We evaluate the proposed method on the MVTecLOCO dataset, achieving an average AUC of 0.867.
  arXiv  Detail & Related papers  (2024-10-14T07:50:56Z)
• AnomalySD: Few-Shot Multi-Class Anomaly Detection with Stable Diffusion Model [7.942354689705658]
  Anomaly detection is a critical task in industrial manufacturing, aiming to identify defective parts of products. Most industrial anomaly detection methods assume the availability of sufficient normal data for training. We propose a few-shot multi-class anomaly detection framework that adopts Stable Diffusion model.
  arXiv  Detail & Related papers  (2024-08-04T08:33:44Z)
• Feature Attenuation of Defective Representation Can Resolve Incomplete Masking on Anomaly Detection [1.0358639819750703]
  In unsupervised anomaly detection (UAD) research, it is necessary to develop a computationally efficient and scalable solution. We revisit the reconstruction-by-inpainting approach and rethink to improve it by analyzing strengths and weaknesses. We propose Feature Attenuation of Defective Representation (FADeR) that only employs two layers which attenuates feature information of anomaly reconstruction.
  arXiv  Detail & Related papers  (2024-07-05T15:44:53Z)
• Improving Vision Anomaly Detection with the Guidance of Language Modality [64.53005837237754]
  This paper tackles the challenges for vision modality from a multimodal point of view. We propose Cross-modal Guidance (CMG) to tackle the redundant information issue and sparse space issue. To learn a more compact latent space for the vision anomaly detector, CMLE learns a correlation structure matrix from the language modality.
  arXiv  Detail & Related papers  (2023-10-04T13:44:56Z)
• COMICS: End-to-end Bi-grained Contrastive Learning for Multi-face Forgery Detection [56.7599217711363]
  Face forgery recognition methods can only process one face at a time. Most face forgery recognition methods can only process one face at a time. We propose COMICS, an end-to-end framework for multi-face forgery detection.
  arXiv  Detail & Related papers  (2023-08-03T03:37:13Z)
• MMNet: Multi-Collaboration and Multi-Supervision Network for Sequential Deepfake Detection [81.59191603867586]
  Sequential deepfake detection aims to identify forged facial regions with the correct sequence for recovery. The recovery of forged images requires knowledge of the manipulation model to implement inverse transformations. We propose Multi-Collaboration and Multi-Supervision Network (MMNet) that handles various spatial scales and sequential permutations in forged face images.
  arXiv  Detail & Related papers  (2023-07-06T02:32:08Z)
• Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection [122.4894940892536]
  We present a novel self-supervised masked convolutional transformer block (SSMCTB) that comprises the reconstruction-based functionality at a core architectural level. In this work, we extend our previous self-supervised predictive convolutional attentive block (SSPCAB) with a 3D masked convolutional layer, a transformer for channel-wise attention, as well as a novel self-supervised objective based on Huber loss.
  arXiv  Detail & Related papers  (2022-09-25T04:56:10Z)
• Deep Learning-Based Defect Classification and Detection in SEM Images [1.9206693386750882]
  In particular, we train RetinaNet models using different ResNet, VGGNet architectures as backbone. We propose a preference-based ensemble strategy to combine the output predictions from different models in order to achieve better performance on classification and detection of defects.
  arXiv  Detail & Related papers  (2022-06-20T16:34:11Z)
• Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection [97.93062818228015]
  We propose to integrate the reconstruction-based functionality into a novel self-supervised predictive architectural building block. Our block is equipped with a loss that minimizes the reconstruction error with respect to the masked area in the receptive field. We demonstrate the generality of our block by integrating it into several state-of-the-art frameworks for anomaly detection on image and video.
  arXiv  Detail & Related papers  (2021-11-17T13:30:31Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)
• Efficient detection of adversarial images [2.6249027950824506]
  Some or all pixel values of an image are modified by an external attacker, so that the change is almost invisible to the human eye. This paper first proposes a novel pre-processing technique that facilitates the detection of such modified images. An adaptive version of this algorithm is proposed where a random number of perturbations are chosen adaptively.
  arXiv  Detail & Related papers  (2020-07-09T05:35:49Z)

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.