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Related Concept Videos

Lumber Defects01:23

Lumber Defects

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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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Consider two point charges, each exerting Coulomb force on the other. It is possible to describe the Coulomb interaction via an intermediate step by defining a new physical quantity called the electric field.
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Magnetic Fields

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A moving charge or a current creates a magnetic field in the surrounding space, in addition to its electric field. The magnetic field exerts a force on any other moving charge or current that is present in the field. Like an electric field, the magnetic field is also a vector field. At any position, the direction of the magnetic field is defined as the direction in which the north pole of a compass needle points.
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Related Experiment Video

Updated: Feb 14, 2026

Light Enhanced Hydrofluoric Acid Passivation: A Sensitive Technique for Detecting Bulk Silicon Defects
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BWD-DETR: A Robust Framework for Bright-Field Wafer Defect Detection.

Ruilou Zhang1,2,3, Xiangji Guo1,2, Yuankang Xu1,2

  • 1Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China.

Sensors (Basel, Switzerland)
|February 13, 2026
PubMed
Summary

This study introduces BWD-DETR, a new framework for detecting small wafer surface defects using bright-field imaging. The method significantly improves the detection of sub-micron defects, enhancing accuracy in semiconductor manufacturing.

Keywords:
bright-field imagingdefect detectionwafer

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Area of Science:

  • Semiconductor Manufacturing
  • Optical Inspection
  • Computer Vision

Background:

  • Bright-field optical imaging is standard for wafer defect detection.
  • Small particle defects on patterned wafers are challenging due to background noise and texture.
  • Existing methods struggle with detectability and positioning accuracy for sub-micron defects.

Purpose of the Study:

  • To develop an advanced detection framework for wafer surface defects under bright-field imaging.
  • To improve the detection and positioning accuracy of small and sub-micron defects.
  • To overcome limitations of current bright-field imaging techniques.

Main Methods:

  • Proposed BWD-DETR framework based on the RT-DETR baseline.
  • Integration of a wavelet backbone for enhanced feature extraction.
  • Inclusion of SMFI and CAS-Fusion modules for improved defect signal processing.

Main Results:

  • Achieved 96.56% AP50 and 54.94% AP50:95 in bright-field wafer defect detection.
  • Demonstrated performance improvements of 1.64% (AP50) and 2.17% (AP50:95) over the baseline.
  • Successfully enhanced detection capabilities for sub-micron defects.

Conclusions:

  • The BWD-DETR framework offers superior performance for bright-field wafer defect detection.
  • The proposed method effectively addresses challenges posed by small defects and complex backgrounds.
  • This approach advances automated inspection in semiconductor fabrication.