  In this paper, we present a machine learning based architecture for microwave
characterization of inkjet printed components on flexible substrates. Our
proposed architecture uses several machine learning algorithms and
automatically selects the best algorithm to extract the material parameters
(ink conductivity and dielectric properties) from on-wafer measurements.
Initially, the mutual dependence between material parameters of the inkjet
printed coplanar waveguides (CPWs) and EM-simulated propagation constants is
utilized to train the machine learning models. Next, these machine learning
models along with measured propagation constants are used to extract the ink
conductivity and dielectric properties of the test prototypes. To demonstrate
the applicability of our proposed approach, we compare and contrast four
heuristic based machine learning models. It is shown that eXtreme Gradient
Boosted Trees Regressor (XGB) and Light Gradient Boosting (LGB) algorithms
perform best for the characterization problem under study.
