KEYWORDS: Data modeling, Semiconducting wafers, Overlay metrology, Machine learning, 3D modeling, Lithography, Data acquisition, Wafer testing, Target detection, Process modeling
As a part of the semiconductor manufacturing process, an overlay measurement instrument is used to inspect overlay accuracy after exposure. The overlay measurement results are not only used to evaluate accuracy, but also to optimize exposure processing by calculating various offsets based on the measurement results and feeding them back to the exposure system. Increasing the number of overlay measurement points can help identify and compensate for local distortions including EPE (edge placement errors). However, it is not practical to perform overlay measurement for all wafers and all regions, therefore the better strategy for is performing correction through combining predicted results with actual measurement results. Canon is working with Macronix to develop the VMOM (Virtual Machine Overlay Metrology) system for predicting overlay measurement results. The VMOM method uses machine learning to study large amounts of data to derive the relationship between overlay error results and exposure system process variables that cause overlay error. A VMOM model was developed using 3D-NAND process data and overlay prediction accuracy and exposure process optimization were evaluated. This paper reports the development status of the VMOM system and the practical effects of the system.
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