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Vector Algebra: Method of Components01:08

Vector Algebra: Method of Components

It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...

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Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
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In-TFT-array-process micro defect inspection using nonlinear principal component analysis.

Yi-Hung Liu1, Chi-Kai Wang1, Yung Ting1

  • 1Department of Mechanical Engineering, Chung Yuan Christian University, Chungli, 320, Taiwan.

International Journal of Molecular Sciences
|January 9, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a Kernel Principal Component Analysis (KPCA) method for automatic optical inspection (AOI) in thin-film transistor liquid-crystal display (TFT-LCD) manufacturing. The novel scheme achieves over 99% defect detection and 96% classification rates, significantly improving LCD panel yield.

Keywords:
TFT array processautomatic optical inspectiondefect inspectionkernel principal component analysissupport vector machinethin film transistor liquid crystal display

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Area of Science:

  • Materials Science and Engineering
  • Electrical Engineering
  • Computer Science

Background:

  • Defect inspection is crucial for thin-film transistor liquid-crystal display (TFT-LCD) manufacturing, with a recent shift in focus towards the critical TFT array process.
  • Existing automatic optical inspection (AOI) methods have limitations in addressing defects within the TFT array process, impacting overall LCD panel yield.
  • Accurate and efficient defect detection and classification in the TFT array process are essential for enhancing manufacturing efficiency and product quality.

Purpose of the Study:

  • To develop a novel defect inspection scheme for TFT-LCD manufacturing, specifically targeting defects in the TFT array process.
  • To enhance the accuracy and efficiency of defect detection and classification using advanced algorithms.
  • To address the challenges associated with inspecting the initial TFT array process, which significantly influences LCD panel yield.

Main Methods:

  • Implementation of a novel inspection scheme based on Kernel Principal Component Analysis (KPCA), a nonlinear extension of Principal Component Analysis (PCA).
  • Utilized KPCA for both detecting and automatically recognizing defect types from captured images of LCD panels.
  • Employed an imbalanced support vector machine (ISVM) with a 2-norm soft margin as the classifier for defect categorization.

Main Results:

  • The KPCA-based inspection scheme achieved a defect detection rate exceeding 99%.
  • A high defect classification rate of over 96% was attained using the ISVM classifier.
  • The inspection process demonstrated remarkable efficiency, with processing times under 1 second per image.

Conclusions:

  • The proposed KPCA-based defect inspection scheme is highly effective for identifying and classifying defects in the critical TFT array process of TFT-LCD manufacturing.
  • The method significantly improves upon existing AOI techniques, offering high accuracy and speed.
  • This advanced inspection approach has the potential to substantially increase LCD panel yield and manufacturing quality.