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Support vector machines (SVMs) have been widely used for creating fast and efficient performance macro-models for quickly predicting the performance parameters of analog circuits. These models have proved to be not only effective and fast but accurate also while predicting the performance. A kernel function is an integral part of SVM to obtain an optimized and accurate model. There is no formal way to decide, which kernel function is suited to a class of regression problem. While most commonly used kernels are radial basis function, polynomial, spline, multilayer perceptron; we have explored many other un-conventional kernel functions and report their efficacy and computational efficiency in this paper. These kernel functions are used with SVM regression models and these macromodels are tested on different analog circuits to check for their robustness and performance. We have used HSPICE for generating the set of learning data. Least Square SVM toolbox along with MATLAB was used for regression. The models which contained modified compositions of kernels were found to be more accurate and thus have lower root mean square error than those containing standard kernels. We have used different CMOS circuits varying in size and complexity as test vehicles-two-stage op amp, cascode op amp, comparator, differential op amp and voltage controlled oscillator.

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