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Sensors and Materials, Volume 33, Number 11(3) (2021)
Copyright(C) MYU K.K.
pp. 4003-4016
S&M2741 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2021.3521
Published: November 30, 2021

Terahertz Imaging of ICs Using K-means Clustering for Scale-Invariant Feature Transform Feature and Fusion Model of Wavelet Transform [PDF]

Xiao-Xiang Pu, Chih-Cheng Chen, Tie-Jun Li, Wei-Lung Mao, and Zhen Liu

(Received July 1, 2021; Accepted November 22, 2021)

Keywords: terahertz spectral image, IC chip, SIFT feature, wavelet fusion

Terahertz spectral imaging is widely used in the failure analysis of ICs. However, owing to the limitations of current imaging hardware, the quality of terahertz images is not high enough to show the internal structure of chips and analyze chip failure. Thus, to improve the detection capability of chip failure in different packaging materials, we propose a terahertz imaging model based on scale-invariant feature transform (SIFT) feature extraction with the K-means clustering of images from multiple sources and the wavelet fusion method. The model creates a terahertz image data set from images drawn from multiple sources in the time and frequency domains. Then, the images drawn from the multiple sources are compared to select a representative image, and the SIFT features of the image are also extracted. The high-quality images obtained from multiple sources are searched and selected by K-means clustering. The images are reconstructed by wavelet image fusion. Experimental results on terahertz imaging in various packaging materials show that the model can quickly and effectively create high-quality images in the internal structure of ICs, which is essential for the nondestructive analysis of chip failures.

Corresponding author: Chih-Cheng Chen, Wei-Lung Mao


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Cite this article
Xiao-Xiang Pu, Chih-Cheng Chen, Tie-Jun Li, Wei-Lung Mao, and Zhen Liu, Terahertz Imaging of ICs Using K-means Clustering for Scale-Invariant Feature Transform Feature and Fusion Model of Wavelet Transform , Sens. Mater., Vol. 33, No. 11, 2021, p. 4003-4016.



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