Microcrack Detection And Noise Reduction In Integrated Circuit Packages - Koh, Ye Sheng (Universiti Sains Malaysia, 2018)
The rise in consumption of electronic products in the recent years have subsequently led to an increase in manufacturing of integrated circuits (ICs) to meet consumers’ demands. Thus, it is vital that each IC is inspected for defects that compromises its quality and usability. This ensures that no defective ICs are used in conjunction with the manufacturing of electronic products which may severely impact the end product’s performance and lifespan. One of the common defects is microcrack on the IC’s package. Image processing is used to detect the presence of microcracks on the IC and the method currently employed to achieve this is by convolution with multiple kernels with different configurations. However, this method is time consuming due to the multiple configurations needed to be tuned and is also susceptible to image noise which lowers the accuracy of the detected microcracks. Therefore, a better algorithm is desired to improve the detection performance in terms of time and accuracy. Three algorithms are tested and evaluated in terms of microcrack detection and noise reduction which are probability based thresholding, histogram equalization, and modified Perona-Malik’s anisotropic diffusion methods. The first algorithm, probability based thresholding method consists of two stages, (i) image crack segmentation where the crack regions are analysed to obtain a suitable thresholding value, and (ii) image denoising where morphological closing is performed on the image. For the second algorithm, histogram equalization method has three stages, (i) image contrast enhancement through histogram equalization, (ii) image crack segmentation which subtracts the histogram equalized image with the image before histogram equalization process before merging the images using bitwise operation, and (iii) image denoising using morphological opening. The third algorithm, modified Perona-Malik’s anisotropic diffusion method consists of three stages, (i) image crack enhancement which separates the image into its red, green, and blue channels and enhances the crack features using modified Perona-Malik’s anisotropic diffusion, (ii) image crack segmentation which subtracts the diffused image with the pre-diffused image before summing the grey values of the images together, and (iii) image denoising using morphological opening and median filter. Images processed using modified Perona-Malik’s anisotropic diffusion method produces images with less noise compared to probability based thresholding method and histogram equalization method. The method has detected cracks present in three samples out of the five samples tested. The modified Perona-Malik’s anisotropic diffusion method is thus proven to produce relatively better performance compared to the other tested methods.