Macromodeling of Nonlinear Digital I/O Drivers

Abstract

In this paper, a modeling technique using spline functions with finite time difference approximation is discussed for modeling moderately nonlinear digital input/output (I/O) drivers. This method takes into account both the static and the dynamic memory characteristics of the driver during modeling. Spline function with finite time difference approximation includes the previous time instances of the driver output voltage/current to capture the output dynamic characteristics of digital drivers accurately. In this paper, the speed and the accuracy of the proposed method is analyzed and compared with the radial basis function (RBF) modeling technique, for modeling different test cases. For power supply noise analysis, the proposed method has been extended to multiple ports by taking the previous time instances of the power supply voltage/current into account. The method discussed can be used to capture sensitive effects like simultaneous switching noise (SSN) and cross talk accurately when multiple drivers are switching simultaneously. A comparison study between the presented method and the transistor level driver models indicate a computational speed-up in the range of 10-40 with an error of less than 5%. For highly nonlinear drivers, a method based on recurrent artificial neural networks (RNN) is discussed.