A new sampling method for analog behavioral modeling

In this paper we demonstrate how statistical learning support vector machine (SVM) algorithms can be applied to modeling analog circuits. The success of these types of techniques has been traditionally achieved by using large sets of training data. However, analog data is expensive in terms of simulation time and hardware testing; therefore, achieving high modeling accuracy with limited datasets has become a challenge. The proposed sampling method dynamically forms datasets based on its selection of dominant support vectors, requiring less data while maintaining the same level of model accuracy. The rest of the modeling flow, including the learning and regression methods, is also discussed. We present two industry designs to validate this approach throughout the paper.