KEYWORDS: Process modeling, Calibration, Gallium arsenide, Instrument modeling, 3D modeling, Fin field effect transistors, Transmission electron microscopy, Silicon, Scatterometry, Manufacturing
Process optimization is a required step during semiconductor technology pathfinding and device evaluation. Virtual process modeling and 3D fabrication tools can be used to study diagnostic, predictive, and prescriptive modeling of process windows and to accelerate process integration. These virtual techniques will become especially valuable as novel gate-all-around devices (GAA) are introduced to replace state-of-the-art FinFET technologies. Model calibration is needed to ensure the accuracy of any virtual fabrication model and requires wafer-based metrology data. Optical scatterometry has established its value in the FinFET era as an effective inline metrology technique due to its accuracy, throughput, and non-destructive nature. In this article, we demonstrate how spectra collected from scatterometry targets can be utilized to resolve sub-nanometer feature changes within a virtual fabrication platform. First, FEOL GAA simulations up to the SiGe epitaxial growth step were performed to establish spectral sensitivity to upstream process changes. A virtual fabrication model was subsequently calibrated using spectra with variations from earlier process steps as model parameters. These variations were accurately pinpointed for unknown spectra via least-square optimization. Additionally, machine learning methods were leveraged to provide instantaneous feedback during the inference phase. Sub-nanometer accuracy was achieved, enabling wide applications in semiconductor technology development. This newly demonstrated capability will be indispensable in GAA commercialization, where 3D metrology and process integration are ongoing challenges.
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