  In this paper, we address the problem of compact model parameter extraction
to simultaneously extract tens of parameters via derivative-free optimization.
Traditionally, parameter extraction is performed manually by dividing the
complete set of parameters into smaller subsets, each targeting different
operational regions of the device, a process that can take several days or even
weeks. Our approach streamlines this process by employing derivative-free
optimization to identify a good parameter set that best fits the compact model
without performing an exhaustive number of simulations. We further enhance the
optimization process to address critical issues in device modeling by carefully
choosing a loss function that evaluates model performance consistently across
varying magnitudes by focusing on relative errors (as opposed to absolute
errors), prioritizing accuracy in key operational regions of the device above a
certain threshold, and reducing sensitivity to outliers. Furthermore, we
utilize the concept of train-test split to assess the model fit and avoid
overfitting. This is done by fitting 80% of the data and testing the model
efficacy with the remaining 20%. We demonstrate the effectiveness of our
methodology by successfully modeling two semiconductor devices: a diamond
Schottky diode and a GaN-on-SiC HEMT, with the latter involving the ASM-HEMT DC
model, which requires simultaneously extracting 35 model parameters to fit the
model to the measured data. These examples demonstrate the effectiveness of our
approach and showcase the practical benefits of derivative-free optimization in
device modeling.
