In this paper, we demonstrate the efficient use of contours extracted from photomask SEM images to characterize features with respect to placement, pattern fidelity, and uniformity. Absorber defects can be automatically detected and categorized. To assess the probability of mask defect printing, we show how an extracted mask image contour can be used as input for a rigorous lithography 3D resist process simulation to quickly estimate the severity and potential printing behavior in resist of a defect through dose and focus. The presented simulation results are validated by wafer data. The results of this work could provide guidelines for the mask making process and mask inspection.
The application of accurate and predictive physical resist simulation is seen as one important use model for fast and efficient exploration of new patterning technology options, especially if fully qualified OPC models are not yet available at an early pre-production stage. The methodology of using a top-down CD-SEM metrology to extract the 3D resist profile information, such as the critical dimension (CD) at various resist heights, has to be associated with a series of presumptions which may introduce such small, but systematic CD errors. Ideally, the metrology effects should be carefully minimized during measurement process, or if possible be taken into account through proper metrology modeling. In this paper we discuss the application of a fast SEM signal emulation describing the SEM image formation. The algorithm is applied to simulated resist 3D profiles and produces emulated SEM image results for 1D and 2D patterns. It allows estimating resist simulation quality by comparing CDs which were extracted from the emulated and from the measured SEM images.
Moreover, SEM emulation is applied for resist model calibration to capture subtle error signatures through dose and defocus. Finally, it should be noted that our SEM emulation methodology is based on the approximation of physical phenomena which are taking place in real SEM image formation. This approximation allows achieving better speed performance compared to a fully physical model.
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