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OPC technology is one of the most key procedures in IC manufacturing. When it came to the 90nm node process, lithography table-driven OPC technology was replaced gradually by the model-based OPC. In the early stage, optical model played a dominant role in whole OPC model, resist model provided limited adjustment on aerial images. As the technical node continue shrinking, and pattern complexity increasing, simplified resist model can’t perform resist behavior sufficiently. The properties of strong shrinkage and queue-time sensitivity posed a great challenge to resist modeling. Plenty of new resist model forms are introduced to describe the complex properties. Huge amounts of SEM measurement data are in need to serve model coverage for the complex pattern structures. Currently the gauge amount of measurement data could be over 10 thousands, OPC modeling becomes a big CPU and time consumer. In order to balance the run time and model accuracy, gauges are down sampled with random selection. Usually, around 5000 gauges are used for model calibration, and the left gauges are used for model verification. The weighted RMS values between calibration and verification should be in the same level, which represents the gauge selection could be considered as the reasonable replacement for the whole gauge data base. In this paper, we proposed a SONR (State Of Nature Reduction)- based gauge down sampling method. The SONR is a tool, which could classify the pattern structures and measurement values into specific clusters. With its help, the gauge amount for model calibration could be shrunk from 5000 to 3000. This method accelerated model calibration speed by nearly 1x time. In the meanwhile, it decreased CPU and license consume, and shorten R&D cycle of new OPC models.
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