Yoshiaki Ogiso

A new algorithm for SEM critical dimension measurements for differentiating between lines and spaces in dense line/space patterns without tone dependence

In performing SEM Critical Dimension (CD) measurements on photomasks in dense line and space arrays it is often difficult to distinguish between whether a feature is a line or space. This is a result of tone shifts that occur affecting contrast on target images. The inability to reliably differentiate lines and spaces leads to the inclusion of fliers, or inaccurate measurements into automated measurement results. In an effort to overcome this phenomenon a new algorithm has been developed to increase the robustness of the CD SEM measurements to insure reliable data acquisition. This new algorithm takes into account apparent tone reversals on a variety of todays photomask material types. This paper will detail the various elements of the new algorithm and show before and after test results of improved recognition performance.

The study on quantifiable analysis for complex OPCed patterns based on Mask CD SEM contour information

As the design rule becomes continuously smaller, the Hard OPC is being applied to pattern design in semiconductor production. Controllability of hard OPCed pattern’s quality directly affects to the performance of the device and yields of production. Critical Dimension Scanning Electron Microscopy (CD-SEM) is used to accurately confirm the Critical Dimension (CD) quality of the photomask. CD-SEM makes the pattern’s shape image by using secondary electrons information directly from the Mask surface and can measure CD values. Classically the purpose of CD-SEM measurement was to get one dimensional CD values. However it is difficult to guarantee complex hard OPCed pattern’s quality by using only one dimensional CD values because complexity of pattern design has been increased. To confirm and control the quality of hard OPCed pattern, the quality of pattern fidelity must be measured quantitatively. In order to overcome this difficulty we developed a new method to quantitatively evaluate the quality of pattern fidelity using EPE (Edge Placement Error) distance from the overlay between Target Design GDS and SEM GDS contour which is extracted from CD-SEM image. This paper represents how to define and analyze quantitatively the quality of complex hard OPCed pattern.

Novel CD-SEM measurement methodology for complex OPCed patterns

As design rules of lithography shrink: accuracy and precision of Critical Dimension (CD) and controllability of hard OPCed patterns are required in semiconductor production. Critical Dimension Scanning Electron Microscopes (CD SEM) are essential tools to confirm the quality of a mask such as CD control; CD uniformity and CD mean to target (MTT). Basically, Repeatability and Reproducibility (R and R) performance depends on the length of Region of Interest (ROI). Therefore, the measured CD can easily fluctuate in cases of extremely narrow regions of OPCed patterns. With that premise, it is very difficult to define MTT and uniformity of complex OPCed masks using the conventional SEM measurement approach. To overcome these difficulties, we evaluated Design Based Metrology (DBM) using Large Field Of View (LFOV) of CD-SEM. DBM can standardize measurement points and positions within LFOV based on the inflection/jog of OPCed patterns. Thus, DBM has realized several thousand multi ROI measurements with average CD. This new measurement technique can remove local CD errors and improved statistical methodology of the entire mask to enhance the representativeness of global CD uniformity. With this study we confirmed this new technique as a more reliable methodology in complex OPCed patterns compared to conventional technology. This paper summarizes the experiments of DBM with LFOV using various types of the patterns and compares them with current CD SEM methods.

The large contour data generation from divided image of photomask pattern of 32 nm and beyond

The application of Mask CD-SEM for process management of photomask using two dimensional measurements as photomask patterns become smaller and more complex, [1]. Also, WPI technology application using an optical Mask inspection tool simulates wafer plane images using photomask images [2]. In order to simulate the MEEF influence for aggressive OPC and High-end photomask patterns in 32nm node and beyond, a requirement exists for wide Field of View (FOV) GDS data and tone information generated from high precision SEM images. In light of these requirements, we developed a GDS data extraction algorithm with sub-nanometer accuracy using wide FOV images, for example, greater than 10um square. As a result, we over come the difficulty of generating large contour data without the distortion that is normally associated with acquired SEM images. Also, it will be shown that the evaluation result can be effective for 32 nm applications and beyond using Mask CD-SEM E3620 manufactured by Advantest. On the other hand, we investigate the application example of the wide FOV GDS data. In order to easily compare the acquired GDS data with design data, we explain the separate algorithm with three layer structures for Tri-tone (Ternary) photomask pattern, consisting of an outer pattern and another pattern.

A study of contour image comparison measurement for photomask patterns of 32 nm and beyond

In order to analyze small reticle defects quantitatively, we have developed a function to measure differences in two patterns using contour data extracted from SEM images. This function employs sub-pixel contour data extracted with high accuracy to quantify a slight difference by ΔCD and ΔArea. We assessed the measurement uncertainty of the function with a test mask and compared the sizes of programmed defects by each of conventional and proposed methods. We have also investigated a correlation between measured minute defects in high MEEF (Mask Error Enhancement Factor) regions and aerial images obtained by AIMS (Aerial Image Measurement System) tool. In this paper, we will explain the Contour Comparison Measurement function jointly developed by Toppan and Advantest and will show its effectiveness for photomask defect analyses.