Hiroaki Mito

Integration of mask and silicon metrology in DFM

We have developed a highly integrated method of mask and silicon metrology. 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. We have inspected the high accuracy, stability and reproducibility in the experiments of integration. The accuracy is comparable with that of the mask and silicon CD-SEM metrology. In this report, we introduce the experimental results and the application. As shrinkage of design rule for semiconductor device advances, OPC (Optical Proximity Correction) goes aggressively dense in RET (Resolution Enhancement Technology). However, from the view point of DFM (Design for Manufacturability), the cost of data process for advanced MDP (Mask Data Preparation) and mask producing is a problem. Such trade-off between RET and mask producing is a big issue in semiconductor market especially in mask business. Seeing silicon device production process, information sharing is not completely organized between design section and production section. Design data created with OPC and MDP should be linked to process control on production. But design data and process control data are optimized independently. Thus, we provided a solution of DFM: advanced integration of mask metrology and silicon metrology. The system we propose here is composed of followings. 1) Design based recipe creation: Specify patterns on the design data for metrology. This step is fully automated since they are interfaced with hot spot coordinate information detected by various verification methods. 2) Design based image acquisition: Acquire the images of mask and silicon automatically by a recipe based on the pattern design of CD-SEM.It is a robust automated step because a wide range of design data is used for the image acquisition. 3) Contour profiling and GDS data generation: An image profiling process is applied to the acquired image based on the profiling method of the field proven CD metrology algorithm. The detected edges are then converted to GDSII format, which is a standard format for a design data, and utilized for various DFM systems such as simulation. Namely, by integrating pattern shapes of mask and silicon formed during a manufacturing process into GDSII format, it makes it possible to bridge highly accurate pattern profile information over to the design field of various EDA systems. These are fully integrated into design data and automated. Bi-directional cross probing between mask data and process control data is allowed by linking them. This method is a solution for total optimization that covers Design, MDP, mask production and silicon device producing. This method therefore is regarded as a strategic DFM approach in the semiconductor metrology.

Consideration of correlativity between litho and etching shape

We developed an effective method for evaluating the correlation of shape of Litho and Etching pattern. The purpose of this method, makes the relations of the shape after that is the etching pattern an index in wafer same as a pattern shape on wafer made by a lithography process. Therefore, this method measures the characteristic of the shape of the wafer pattern by the lithography process and can predict the hotspot pattern shape by the etching process. 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 wafer 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 lithography management, and this has a big impact on the semiconductor market that centers on the semiconductor business. 2-dimensional shape of wafer 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. In this study, we conducted experiments for correlation method of the pattern (Measurement Based Contouring) as two-dimensional litho and etch evaluation technique. That is, observation of the identical position of a litho and etch was considered. It is possible to analyze variability of the edge of the same position with high precision.

Study of shape evaluation for mask and silicon using large field of view

We have developed a highly integrated method of mask and silicon metrology. The aim of this integration is evaluating the performance of the silicon corresponding to Hotspot on a mask. It can use the mask shape of a large field, besides. 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. As an optimal solution to these issues, we provide a DFM solution that extracts 2-dimensional data for a more realistic and error-free simulation by reproducing accurately the contour of the actual mask, in addition to the simulation results from the mask data. On the other hand, there is roughness in the silicon form made from a mass-production line. Moreover, there is variation in the silicon form. For this reason, quantification of silicon form is important, in order to estimate the performance of a pattern. In order to quantify, the same form is equalized in two dimensions. And the method of evaluating based on the form 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. 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. •Discrimination of nuisance defects for fine pattern. •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 form of the same location of Design, Mask, and Silicon in such a viewpoint. And we report it about algorithm of the image composition in Large Field.

Development for 2D pattern quantification method on mask and wafer

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.

New approach for mask-wafer measurement by design-based metrology integration system

OPC technique is getting more complicated toward 32nm and below technology node, i.e. from moderate OPC to aggressive OPC. Also, various types of phase shift mask have been introduced, and then the manufacturing process of them is complicated now. In order to shorten TAT (Turn around time) time, mask technique need be considered in addition to lithography technique. Furthermore, the lens aberration of the exposure system is getting smaller, so the current performance of it is very close to the ideal. On the other hand, when down sizing goes down to 32nm technology node, it starts to be reported that there are cases that size cannot be matched between a mask pattern and the corresponding printed pattern. Therefore, it is very indispensable to understand the pattern sizes correlation between a mask and the corresponding printed wafer in order to improve the accuracy and the quality, in the situation that the device size is so small that low k1 lithography had been developed and widely used in a production. Then it is thought that it is one of the approaches to improve an estimated accuracy of lithography by using contour that was extracted from mask SEM image in addition to mask model. This paper describes a newly developed integration system in order to solve issues above, and the applications. This is a system which integrates CG4500; CD-SEM for mask and CG4000; CD SEM for wafer; using DesignGauge; OPC evaluation system by Hitachi High-Technologies. It was investigated that a measurement accuracy improvement by executing a mask-wafer same point measurement with same measurement algorithm utilizing the new system. At first, we measured patterns described on a mask and verified the validity based on a measurement value, picture, measurement parameter and the coordinate. Then create a job file for a wafer CD-SEM using the system so as to measure the same patterns that were exposed using the mask. In addition, average CD measurement was tried in order to improve the correlation. Photomask Technology 2009, edited by Larry S. Zurbrick, M. Warren Montgomery, Proc. of SPIE Vol. 7488, 748832 ·