  Semiconductor device models are essential to understand the charge transport
in thin film transistors (TFTs). Using these TFT models to draw inference
involves estimating parameters used to fit to the experimental data. These
experimental data can involve extracted charge carrier mobility or measured
current. Estimating these parameters help us draw inferences about device
performance. Fitting a TFT model for a given experimental data using the model
parameters relies on manual fine tuning of multiple parameters by human
experts. Several of these parameters may have confounding effects on the
experimental data, making their individual effect extraction a non-intuitive
process during manual tuning. To avoid this convoluted process, we propose a
new method for automating the model parameter extraction process resulting in
an accurate model fitting. In this work, model choice based approximate
Bayesian computation (aBc) is used for generating the posterior distribution of
the estimated parameters using observed mobility at various gate voltage
values. Furthermore, it is shown that the extracted parameters can be
accurately predicted from the mobility curves using gradient boosted trees.
This work also provides a comparative analysis of the proposed framework with
fine-tuned neural networks wherein the proposed framework is shown to perform
better.
