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A perfect crystal, in theory, has a uniform structure with the same unit cell and lattice points throughout. However, any deviation from this periodic arrangement is known as an imperfection or defect. These defects can be categorized into three types: point, line, and plane defects.Point defects occur when there is a deviation from the ideal due to missing atoms, displaced atoms, or additional atoms. These imperfections might occur due to imperfect packing during crystallization or because of...
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Lumber defects, which can affect both the appearance and structural integrity of wood, include a variety of growth and manufacturing flaws. Growth defects such as knots and knotholes occur where branches were once attached to the tree trunk, with knotholes forming when these knots fall out. Other natural defects include decay and insect damage, which compromise the wood's strength and durability.
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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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Schottky defects arise when some lattice points in a crystal, such as those in NaCl, remain unoccupied, creating lattice vacancies without disturbing the overall electrical neutrality of the crystal. This defect is common in ionic crystals where the positive and negative ions are similar in size, as seen in sodium chloride and cesium chloride. The presence of Schottky defects enables the crystal to conduct electricity to a small extent through an ionic mechanism. Electric fields cause nearby...
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Non-stoichiometric defects refer to a type of defect in the crystal structure of a compound where the ratio of its constituent elements deviates from the ideal stoichiometric ratio. There are two main types of non-stoichiometric defects: metal excess defects and metal deficiency defects.Metal excess defects occur when there is a slight surplus of metal ions than what is required by the stoichiometric ratio of the compound. For example, heating a sodium chloride crystal in sodium vapor results...
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Wafer Defect Recognition for Industrial Inspection: FCS-VMamba Model and Experimental Validation.

Yijia Zhang1, Ziyi Ma2, Tongji Cui3

  • 1School of Materials Science and Engineering, Hebei University of Technology, Tianjin 300401, China.

Journal of Imaging
|April 27, 2026
PubMed
Summary
This summary is machine-generated.

A new FCS-VMamba model improves semiconductor wafer defect classification using Frequency Attention, Cross-Layer Cross-Attention, and Saliency Feature Suppression. This parameter-efficient approach enhances chip manufacturing yield and reliability.

Keywords:
cross-layer cross-attentionfrequency attentionparameter-efficient networksaliency feature suppressionvisual state space modelwafer defect recognition

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Area of Science:

  • Computer Vision
  • Materials Science
  • Semiconductor Manufacturing

Background:

  • Semiconductor wafer defect classification is vital for chip yield and reliability.
  • Challenges include weak imaging, detail loss, complex backgrounds, and edge device constraints.
  • Existing Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) struggle with long-range dependencies and deployment costs.

Purpose of the Study:

  • To develop a parameter-efficient model for industrial wafer defect recognition.
  • To address limitations of traditional models in handling complex imaging scenarios.
  • To leverage the Visual State Space Model (VMamba) architecture for improved performance.

Main Methods:

  • Proposed FCS-VMamba, a domain-adapted model based on the VMamba architecture.
  • Integrated Frequency Attention (FA) for enhanced feature extraction.
  • Incorporated Cross-Layer Cross-Attention (CLCA) and Saliency Feature Suppression (SFS) modules.
  • Utilized VMamba for global contextual modeling with linear computational complexity.

Main Results:

  • FCS-VMamba achieved 86.06% macro-precision.
  • Achieved 87.91% Top-1 accuracy.
  • Model has only 1.2 million parameters, demonstrating parameter efficiency.

Conclusions:

  • FCS-VMamba offers a practical and efficient solution for industrial wafer defect recognition.
  • The model effectively addresses challenges in complex imaging and edge deployment.
  • Demonstrates the potential of VMamba-based architectures in specialized industrial applications.