A Wavelet-Based Neural Network Applied to Surface Defect Detection of LED Chips

Authors:
Hong-Dar Lin;Chung-Yu Chung
Affiliations:
Department of Industrial Engineering and Management, Chaoyang University of Technology, 168 Jifong E. Rd., Wufong Township, Taichung County 41349, Taiwan, (R.O.C.);Department of Industrial Engineering and Management, Chaoyang University of Technology, 168 Jifong E. Rd., Wufong Township, Taichung County 41349, Taiwan, (R.O.C.)
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

Citing 7
Cited 1

Texture Measures for Carpet Wear Assessment

IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine vision

Machine vision
Integrated machine learning approaches for complementing statistical process control procedures

Decision Support Systems
Wavelet transform-based locally orderless images for texture segmentation

Pattern Recognition Letters
Texture segmentation using wavelet transform

Pattern Recognition Letters
Defect detection on semiconductor wafer surfaces

Microelectronic Engineering
Computer-Aided vision system for MURA-Type defect inspection in liquid crystal displays

PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology

Novelty detection for the inspection of light-emitting diodes

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

This research explores the automated detection of surface defects that fall across two different background textures in a light-emitting diode (LED) chip. Water-drop defects, commonly found on chip surface, impair the appearance of LEDs as well as their functionality and security. Automated inspection of a water-drop defect is difficult because the defect has a semi-opaque appearance and a low intensity contrast with the rough exterior of the LED chip. Moreover, the blemish may fall across two different background textures, which further increases the difficulties of defect detection. We first use the one-level Haar wavelet transform to decompose a chip image and extract four wavelet characteristics. Then, the Multi-Layer Perceptron (MLP) neural network with back-propagation (BPN) algorithm is applied to integrate the multiple wavelet characteristics. Finally, the wavelet-based neural network approach judges the existence of water-drop defects. Experimental results show that the proposed method achieves an above 96.8% detection rate and a below 4.8% false alarm rate.