Modeling of IC Buffers from Channel Responses via Machine Learning Kernel Regression

This paper investigates the potential of a fully behavioral approach for the generation of accurate models of digital IC buffers based on conventional kernel regressions. The proposed approach does not assume a specific model structure like the classical two-piece model representation which has been massively used in literature, offering a promising and viable alternative to facilitate the modeling of nonlinear electrical devices. The collected results represent a first proof-of-concept, aimed at demonstrating the strengths of the proposed alternative modeling approach.