An error-driven adaptive grid refinement algorithm for automatic generation of analog circuit performance macromodels

Abstract

In this paper, we present an error-driven adaptive sampling algorithm called adaptive grid refinement (AGR) algorithm to automatically generate performance macromodels for analog circuits. Starting from samples on a coarse grid, the AGR algorithm builds a global model and validates its accuracy on an independent validation data set sampled within this grid. If this model is not accurate enough on the validation data, the grid is split into equal sized smaller grids. On each of these grids, a local model is built using samples on this grid and its neighboring and validated similarly. A grid will not be further refined only if the corresponding local model is accurate on its validation data set. The algorithm will stop when all the local models are accurate on their corresponding validation data set. We build six performance macromodels of a CMOS opamp using the AGR algorithm and compare it with the competing techniques. The strengths and weaknesses of the proposed algorithm are discussed.