KEYWORDS: Calibration, Scanning electron microscopy, Photomasks, Statistical modeling, Signal to noise ratio, Metrology, Image quality, Data modeling, Critical dimension metrology
Building accurate models for Mask Process Correction (MPC) is indispensable for manufacturing masks for advanced wafer production nodes. In recent years, contour-based model calibration is being increasingly studied as a supplement to standard gauge-based modeling. In this paper, we demonstrate a data processing flow for contour calibration of MPC models to overcome the issue of noisy input contours by averaging the measured contours of repeating patterns within the SEM image field-of-view (FOV). This method not only averages out the statistical noise in the incoming FOV contours, but also allows us to make the model calibration process more efficient.
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.
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