Approximate Bayesian Computation for Physical Inverse Modeling

• URL: http://arxiv.org/abs/2111.13296v1
• Date: Fri, 26 Nov 2021 02:23:05 GMT
• Title: Approximate Bayesian Computation for Physical Inverse Modeling
• Authors: Neel Chatterjee, Somya Sharma, Sarah Swisher, Snigdhansu Chatterjee
• Abstract summary: We propose a new method for automating the model parameter extraction process resulting in an accurate model fitting. 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.
• Score: 0.32771631221674324
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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