Abstract

Defects on a reticle are inspected, reviewed, and repaired by different tools. They are located by automated optical inspection (AOI); however, if the characteristic size of defects is similar to that of light and electron beam wavelengths, they are often unclassified or misclassified by AOI. Atomic force microscopes (AFM) along with electron microscopes are used for investigating defects located by AOI to distinguish false defects from real defects and effectively classify them. Both AFM and electron microscopes provide high resolution images. However, electron microscopy is known to be destructive and have less accuracy in 3rd dimension measurement compared to AFM [1]. On the other hand, AFM is known to have low throughput and limited tip life in addition to requiring significant effort to finding the defects. These limitations emanate from having to perform multiple large scans to find the defect locations, to compensate for stage coordinate inaccuracies, and to correct the mismatch between the AFM and the AOI tools. In this work we introduce automatic defect review (ADR) AFM for defect study and classification of EUV mask reticles that overcomes the aforementioned limitations of traditional AFM. This metrology solution is based on an AFM configuration with decoupled Z and XY scanners that makes it possible to collect large survey images with minimum out of plane motion. To minimize the stage errors and mismatch between the AFM and the AOI coordinates, the coordinates of fiducial markers are used for coarse alignment. In addition, fine alignment of the coordinates is performed using enhanced optical vision on marks on the reticle. The ADR AFM is used to study a series of phase defects identified by an AOI tool on a reticle. Locating the defects, imaging, and defect classification are performed using the ADR automation software and with the throughput of several defects per hour. In order to preserve tip life and data consistency, AFM imaging is performed in non-contact mode. The ADR AFM provides high throughput, high resolution, and non-destructive means for obtaining 3D information for defect review and classification. Therefore this technology can be used for in-line defect review and classification for mask repair.

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