Automatic Semiconductor Wafer Image Segmentation for Defect Detection Using Multilevel Thresholding
School of Microelectronic Engineering, Universiti Malaysia Perlis, KampusPauh Putra, 02600 Arau, Perlis, Malaysia
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Quality control is one of important process in semiconductor manufacturing. A lot of issues trying to be solved in semiconductor manufacturing industry regarding the rate of production with respect to time. In most semiconductor assemblies, a lot of wafers from various processes in semiconductor wafer manufacturing need to be inspected manually using human experts and this process required full concentration of the operators. This human inspection procedure, however, is time consuming and highly subjective. In order to overcome this problem, implementation of machine vision will be the best solution. This paper presents automatic defect segmentation of semiconductor wafer image based on multilevel thresholding algorithm which can be further adopted in machine vision system. In this work, the defect image which is in RGB image at first is converted to the gray scale image. Median filtering then is implemented to enhance the gray scale image. Then the modified multilevel thresholding algorithm is performed to the enhanced image. The algorithm worked in three main stages which are determination of the peak location of the histogram, segmentation the histogram between the peak and determination of first global minimum of histogram that correspond to the threshold value of the image. The proposed approach is being evaluated using defected wafer images. The experimental results shown that it can be used to segment the defect correctly and outperformed other thresholding technique such as Otsu and iterative thresholding.
© The Authors, published by EDP Sciences, 2016
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