A Machine Learning Approach to Varactor Diode Modeling

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.