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22 February 2021 Contour-based process characterization, modeling, and control for semiconductor manufacturing
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Abstract
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.
Conference Presentation
© (2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kan Zhou, Xin Guo, Wenzhan Zhou, Qijian Wan, Chunshan Du, Wenming Wu, Ao Chen, Recoo Zhang, Germain Fenger, Seshadri Rampoori, and Bhamidipati Samir "Contour-based process characterization, modeling, and control for semiconductor manufacturing", Proc. SPIE 11611, Metrology, Inspection, and Process Control for Semiconductor Manufacturing XXXV, 1161113 (22 February 2021); https://doi.org/10.1117/12.2591300
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