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

Abstract

SUMMARY Usually, wafer inspection is a two-step process. The first step, which is very important, consists of detection of defects on the wafer. The second step classification of the detected defects, which is done after defect detection as necessary. Recently, it has become necessary to perform simultaneous detection and classification of wafer defects in order to reduce the wafer inspection time while maintaining high resolution of inspection. Optical wafer inspection is the most effective method for detecting and classifying defects and estimating their size in a short time. In this method, the scattered light distribution is compared with a database of previous scattered light distributions and pattern matching is used to classify defects and estimate their size. Therefore, to achieve optical wafer inspection based on scattered light distribution, it is necessary to gather scattered light distribution data for many samples of various kinds. The first aim of this study is to develop a defect classification and size estimation method utilizing the scattered light distribution. We propose a method of classifying defects on wafers and estimating their size. The proposed method is based on 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 were generated by discrete dipole approximation (DDA) simulation. A database of the light scattering distribution is easily created using the simulation.