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In this letter, a new method for nonlinear behavioral modeling of high-speed I/O drivers is presented, combining neural networks with driver specific circuit knowledge. In the proposed technique, the circuit knowledge of the driver is exploited to preserve the physical property of the driver. In addition, several neural network sub-models are incorporated into the overall model structure to effectively compensate the missing information in the existing buffer models, when dealing with analog input signals of various shapes. The validity and efficiency of the proposed technique are demonstrated through the modeling of a commercial I/O driver and the use of the resulting model for signal integrity simulations.