Accurate and reusable macromodeling technique using a fuzzy-logic approach

Abstract

An approach for applying fuzzy logic for accurate analog circuit macromodel sizing is presented. In our proposed method, multiple adaptive neuro-fuzzy inference systems (MANFIS) are trained to predict the performance characteristics (gain, bandwidth) of a fully differential telescopic transconductance amplifier (OTA). The neuro-fuzzy computed characteristic values are in excellent agreement and one order of magnitude faster than those obtained from device level SPICE simulations. This technique allows the generation of accurate, efficient and reusable models of analog circuits. It is demonstrated and compared with other classical techniques like polynomial regression or artificial neural network approaches.