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We have developed the effective method of mask and silicon 2-dimensional metrology. The aim of this method is evaluating the performance of the silicon corresponding to Hotspot on a mask. The method adopts a metrology management system based on DBM (Design Based Metrology). This is the high accurate contouring created by an edge detection algorithm used in mask CD-SEM and silicon CD-SEM.
Currently, as semiconductor manufacture moves towards even smaller feature size, this necessitates more aggressive optical proximity correction (OPC) to drive the super-resolution technology (RET). In other words, there is a trade-off between highly precise RET and mask manufacture, and this has a big impact on the semiconductor market that centers on the mask business. 2-dimensional Shape quantification is important as optimal solution over these problems. Although 1-dimensional shape measurement has been performed by the conventional technique, 2-dimensional shape management is needed in the mass production line under the influence of RET. We developed the technique of analyzing distribution of shape edge performance as the shape management technique. On the other hand, there is roughness in the silicon shape made from a mass-production line. Moreover, there is variation in the silicon shape. For this reason, quantification of silicon shape is important, in order to estimate the performance of a pattern. In order to quantify, the same shape is equalized in two dimensions. And the method of evaluating based
on the shape is popular. In this study, we conducted experiments for averaging method of the pattern (Measurement Based Contouring) as two-dimensional mask and silicon evaluation technique. That is,
observation of the identical position of a mask and a silicon was considered. It is possible to analyze variability of the edge of the same position with high precision. The result proved its detection accuracy and reliability of variability on two-dimensional pattern (mask and silicon) and is adaptable to following fields of mask quality management.
- Estimate of the correlativity of shape variability and a process margin.
- Determination of two-dimensional variability of pattern.
- Verification of the performance of the pattern of various kinds of Hotspots.
In this report, we introduce the experimental results and the application. We expect that the mask measurement and the shape control on mask production will make a huge contribution to mask
yield-enhancement and that the DFM solution for mask quality control process will become much more important technology than ever. It is very important to observe the shape of the same location of Design,
Mask, and Silicon in such a viewpoint.
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