散乱光分布を利用した半導体ウエハ上の欠陥種分類および寸法推定に関する研究

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  • Classification and Size Estimation of Wafer Defects by Using Scattered Light Distribution

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Usually, there are two processes in wafer inspection. One is detection of defects on a wafer, which is very important. The other is classification of the detected defects, which is done after defect detection as necessary. Recently, it has been necessary to simultaneously detect and classify defects on wafers in order to shorten the wafer inspection time with high resolution. Optical wafer inspection is the most effective method for detecting and classifying a defect and estimating its size in a short time. Optical wafer inspection utilizing scattered light distribution can easily detect and classify a defect since the distribution depends on both the type and size of the defect. The measured scattered light distribution is compared with a database of the previous scattered light distributions using pattern matching in order to classify the defect and estimate its size. Therefore, to achieve optical wafer inspection with scattered light distribution, it is necessary to gather scattered light distribution data for many various samples.<br>The first aim of this study is to develop a defect classification and size estimation method utilizing scattered light distribution. We propose a way for classifying a defect on a wafer and estimating its size. The proposed method uses pattern matching utilizing a parametric eigenspace. The second aim of the paper is to validate the proposed method by applying it to scattered light distribution samples. The light scattering distribution samples are generated by Discrete Dipole Approximation (DDA) simulation. The database of the light scattering distributions is easily created using the simulation.

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