Abstract
As complex NTD resist behavior significantly impacts AF wafer printing , there is a need to better model NTD AF printing. We present our work to further enhance NTD compact modeling accuracy for AF printing prediction by physics-based and data-based methods. The physics-based enhancements are derived from improvements in behavioral mechanisms in exposed and partially exposed NTD materials. The data-based enhancements are derived from learning methodologies developed for predicting lithography hot-spots at the limits of process control. Both types of enhancements are needed to predict fine changes in imaging and resist behavior where traditional compact models break down.
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