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This paper presents a method to automatically generate compact symbolic performance models of analog circuits with no prior specification of an equation template. The approach takes SPICE simulation data as input, which enables modeling of any nonlinear circuits and circuit characteristics. Genetic programming is applied as a means of traversing the space of possible symbolic expressions. A grammar is specially designed to constrain the search to a canonical form for functions. The approach generates a set of symbolic models that collectively provide a tradeoff between error and model complexity. Experimental results show that the symbolic models generated ar compact and easy to understand, making this an effective method for aiding understanding in analog design. The models also demonstrate better prediction quality than polynomials.

CAFFEINE: template-free symbolic model generation of analog circuits via canonical form functions and genetic programming