Approximate Bayesian Computation for Physical Inverse Modeling

TL;DR

This paper introduces an automated parameter estimation method for semiconductor device models using approximate Bayesian computation and gradient boosted trees, improving accuracy over neural networks.

Contribution

The work presents a novel framework combining ABC and machine learning for efficient, accurate parameter extraction in TFT models, outperforming neural network approaches.

Findings

ABC provides accurate posterior distributions of parameters.

Gradient boosted trees effectively predict parameters from mobility curves.

Proposed method outperforms fine-tuned neural networks.

Abstract

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.