Title :
Rapidly Void Detection in TSVs With 2-D X-Ray Imaging and Artificial Neural Networks
Author :
Fuliang Wang ; Feng Wang
Author_Institution :
State Key Lab. of High Performance Complex Manuf., Central South Univ. Changsha, Changsha, China
Abstract :
Through-silicon via (TSV) is a vertical channel that passes through a chip to connect stacked dies in 3-D packaging. Voids may be produced during via filling; therefore, void detection is important for improving the quality of TSV devices. In this paper, a rapid void detection method using a single 2-D X-ray imaging was developed. An image processing method was used to divide the x-ray image into blocks for multithreshold image cutting and feature extraction. An artificial neural network (ANN) was then used to find the blocks that contain voids, and the voids were located by Hough transform. The effects of various block widths and heights were studied; a block size of 30×40 pixels is recommended. The void detection is more sensitive to block width than height. Experiments show that the method proposed in this paper can automatically and rapidly detect voids in TSVs using one 2-D X-ray image.
Keywords :
Hough transforms; X-ray imaging; electronic engineering computing; feature extraction; image processing; integrated circuit packaging; neural nets; semiconductor device packaging; three-dimensional integrated circuits; voids (solid); 2-D X-ray imaging; 3-D packaging; Hough transform; TSV; artificial neural networks; feature extraction; image processing method; multithreshold image cutting; stacked dies; through-silicon via; vertical channel; via filling; void detection; Artificial neural networks; Feature extraction; Image segmentation; Silicon; Three-dimensional displays; Through-silicon vias; X-ray imaging; 2D X-ray image; Artificial neural network; TSV; image process; void detection;
Journal_Title :
Semiconductor Manufacturing, IEEE Transactions on
DOI :
10.1109/TSM.2014.2309591