Abstract
Machine learning is one tool that can be used to model circuits with complex characteristics. This paper explores the use of a k-nearest neighbors (k-NN) approximation method to model and characterize tunable varactor diodes using the available information on the datasheets. The method used is easily expandable and can rapidly generate a massive amount of data points. Once properly trained, the model resulted in a highly accurate approximation of the characteristics, calculating a mean squared error for the output variables of the equivalent resistance (R_{eq}) being 8.04\times 10^{-8} and the equivalent capacitance value for the diode (C_{eq}) being 3.24 \times 10^{-19} . The proposed method was validated by modeling a commercially available varactor and comparing the results with the data provided by the vendor.