The mask CD mean-to-target (MTT) has been widely adopted as one of the key metrics for the mask quality control. As more aggressive optical proximity correction (OPC) is applied to push the resolution limit, traditional CDSEM measurement-based metrology is not sufficient to characterize mask CD MTT, especially for complicated 2D patterns. In this paper, we present the method of using SEM image contours for the characterization of mask CD MTT. The full flow includes contour extraction from mask SEM images, contour-to-contour alignment, contour averaging and edge placement error (EPE) measurement of mask image contour against the target. The OPC Verify engine is employed to give fast EPE check at closely packed sampling sites along the target. We apply this method to evaluate mask CD MTT for the hotspot patterns from two masks. The generated mask CD MTT distribution histograms and color maps demonstrate a good correlation with the wafer defect counts.
KEYWORDS: Process modeling, Semiconductor manufacturing, Control systems, Statistical analysis, Scanning electron microscopy, Process control, Calibration
In this paper, we present the flow and results of contour-based process characterization, modeling and control used for semiconductor manufacturing. First, high-quality contours are extracted from large field of view (FOV) SEM images based on the improved Canny edge detection algorithm. Prior to the contour analysis steps, SEM image distortion correction is performed by using the loworder linear model. When there are repeating cells within one FOV, the N-sigma roughness band of the unit cell is calculated to show the stochastic process variation fingerprint. For SEM images collected from a focus-exposure matrix wafer, the contour-based process window analysis is performed to generate the depth-of-focus map for the full image, enabling precise detection of process window limiting locations. Finally, 3D compact resist models are calibrated by using both inner and outer contours from the same SEM images, which proves to be effective for the prediction of resist top loss related hotspots.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.