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Imperfections in Crystal Structure: Point, Line and Plane Defects01:25

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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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Related Experiment Video

Updated: May 3, 2026

Studying Dynamic Processes of Nano-sized Objects in Liquid using Scanning Transmission Electron Microscopy
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SCRS: Segment Structure with Controllable Realistic Synthetic for Chip Scratch Detection.

Jiaqing Huang1, Jianjun He1, Weihua Gui1

  • 1School of Automation, Central South University, Changsha 410083, China.

Sensors (Basel, Switzerland)
|November 27, 2025
PubMed
Summary

This study introduces a new method for detecting scratches on laser diode chips. The approach uses synthetic data generation to improve scratch detection accuracy, enhancing laser quality and preventing chip burnout.

Keywords:
data synthesisdiffusion modellaser diode chipscratch segmentation

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Area of Science:

  • Materials Science
  • Optoelectronics
  • Computer Vision

Background:

  • Scratches on laser diode chips impair performance and cause burnout.
  • Low-contrast images make scratch detection difficult and hinder dataset creation.
  • Accurate scratch detection is vital for laser quality control.

Purpose of the Study:

  • To develop a robust method for detecting scratches on laser diode chip emitting facets.
  • To address the challenge of limited and low-contrast training data for scratch detection.
  • To improve the accuracy and reliability of scratch segmentation in industrial applications.

Main Methods:

  • Proposed the Segment Structure with Controllable Realistic Synthetic (SCRS) method.
  • Utilized a mask-guided diffusion model to generate realistic synthetic scratch images.
  • Developed a novel TransCNN network combining vision transformers and convolutional decoding for segmentation.

Main Results:

  • Achieved a mean Intersection over Union (mIoU) of 74.4% for deep scratch detection.
  • Achieved a mean Intersection over Union (mIoU) of 75.8% for shallow scratch detection.
  • Demonstrated the effectiveness of SCRS in synthesizing diverse and realistic training data.

Conclusions:

  • The SCRS method significantly enhances scratch detection accuracy on laser diode chips.
  • Synthetic data generation is a viable solution for overcoming data limitations in scratch detection.
  • The proposed TransCNN network provides accurate scratch segmentation, showing industrial application potential.