Abstract
In integrated circuit manufacturing industry, in order to meet the high demand of electronic products, wafers are designed to be smaller and smaller, which makes automatic wafer defect detection a great challenge. The existing wafer defect detection methods are mainly based on the precise segmentation of one single wafer, which relies on high-cost and complicated hardware instruments. The segmentation performance obtained is unstable because there are too many limitations brought by hardware implementations such as the camera location, the light source location, and the product location. To address this problem, in this paper, we propose a method for wafer defect detection. This novel method includes two phases, namely wafer segmentation and defect detection. In wafer segmentation phase, the target wafer image is segmented based on the affine iterative closest algorithm with spatial feature points guided (AICP-FP). In wafer defect detection phase, with the inherent characteristics of wafers, a simple and effective algorithm based on machine vision is proposed. The simulations demonstrate that, with these two phases, the higher accuracy and higher speed of wafer defect detection can be achieved at the same time. For real industrial system, this novel method can satisfy the real-time detection requirements of automatic production line.
Highlights
With the rapid development of semiconductor, the demand of the basic element, wafers, is growing higher and higher
WAFER SEGMENTATION In this method, wafer segmentation is the essential prerequisite and the precise wafer segmentation results are crucial to the following defect detection
In order to verify the accuracy of our proposed wafer segmentation method, we make comparative experiments with five other wafer segmentation methods: segmentation based on the size, segmentation based on line segment detector (LSD) algorithm, centroid segmentation based on k-means clustering algorithm, traditional affine iterative closest algorithm and corner points detection algorithm
Summary
With the rapid development of semiconductor, the demand of the basic element, wafers, is growing higher and higher. The quality standards of wafer is becoming stricter. There are lots of complex procedures during the manufacturing process, so it is highly possible that wafers get contaminated in the assembly line. It is necessary to recognize the defect pattern for finding out the abnormal sources in the manufacturing process [1]. According to the research conducted by A. Freeman [2], the accuracy of human-expert based detection method is less than 45%. What’s worse, the final product contains a large number of single wafer, as shown, making detection much more difficult What’s worse, the final product contains a large number of single wafer, as shown in Figure 1, making detection much more difficult
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