This paper presents an new direct--fitting method to generate posynomial response surface models with arbitrary constant exponents for linear and nonlinear performance parameters of analog integrated circuits. Posynomial models enable the use of efficient geometric programming techniques for circuit sizing and optimization. The automatic generation avoids the time--consuming nature and inaccuracies of handcrafted analytic model generation. The technique is based on the fitting of posynomial model templates to numerical data from SPICE simulations. Attention is paid to estimating the relative `goodness--of--fit' of the generated models. Experimental results illustrate the significantly better accuracy of the new approach.

This paper presents a novel method to automatically generate symbolic expressions for both linear and nonlinear circuit characteristics using a template-based fitting of numerical, simulated data. The aim of the method is to generate convex, interpretable expressions. The posynomiality of the generated expressions enables the use of efficient geometric programming techniques when using these expressions for circuit sizing and optimization. Attention is paid to estimating the relative `goodness-of-fit' of the generated expressions. Experimental results illustrate the capabilities of the approach.

This paper presents a new method to automatically generate posynomial symbolic expressions for the performance characteristics of analog integrated circuits. The coefficient set as well as the exponent set of the posynomial expression are determined based on SPICE simulation data with device-level accuracy. We will prove that this problem corresponds to solving a non--convex optimization problem without local minima. The presented method is capable of generating posynomial performance expressions for both linear and nonlinear circuits and circuit characteristics. This approach allows to automatically generate an accurate sizing model that composes a geometric program that fully describes the analog circuit sizing problem. The automatic generation avoids the time--consuming nature of hand--crafted analytic model generation. Experimental results illustrate the capabilities and effectiveness of the presented modeling technique.