LAUSR

This article addresses the nonlinear behavioral modeling of tunable drivers with preemphasis including power supply noise. The proposed model relies on the use of state-aware weighting functions that control the transitions of the driver’s output stage for the scenarios where switched input logic states are shorter than the preemphasis duration, and the influence of supply voltage variation is considered. For the power supply noise analysis, the method is applied to multiple ports. Feedforward neural networks (FFNNs) are used to implement the state-aware weighting functions, and recurrent neural networks (RNNs) are used to capture the dynamic memory characteristics of driver’s ports. For tunable drivers in the state-of-the-art design covering features such as drive strength and preemphasis, a parameterized model that considers driver control parameters is presented. As a black-box approach, the resulting model protects intellectual property (IP). Practical industrial driver examples demonstrate the good accuracy, flexibility, and significant simulation speedup of the proposed model, which can facilitate the signal and power integrity (SIPI) analysis.