@inproceedings{
brahma2023accelerated,
title={Accelerated Modelling of Interfaces for Electronic Devices using Graph Neural Networks},
author={Pratik Brahma and Krishnakumar Sivaganesh Bhattaram and Sayeef Salahuddin},
booktitle={AI for Accelerated Materials Design - NeurIPS 2023 Workshop},
year={2023},
url={https://openreview.net/forum?id=3yJB9iHq65}
}
Modern microelectronic devices are composed of interfaces between a large number of materials, many of which are in amorphous or polycrystalline phases. Modeling such non-crystalline materials using first-principles methods such as density functional theory is often numerically intractable. Recently, graph neural networks (GNNs) have shown potential to achieve linear complexity with accuracies comparable to ab-initio methods. Here, we demonstrate the applicability of GNNs to accelerate the atomistic computational pipeline for predicting macroscopic transistor transport characteristics via learning microscopic physical properties. We generate amorphous heterostructures, specifically the HfO$_2$-SiO$_2$-Si semiconductor-dielectric transistor gate stack, via GNN predicted atomic forces, and show excellent accuracy in predicting transport characteristics including injection velocity for nanoslab silicon channels. This work paves the way for faster and more scalable methods to model modern advanced electronic devices via GNNs.
Accelerated Modelling of Interfaces for Electronic Devices using Graph Neural Networks
Pratik Brahma Krishnakumar Sivaganesh Bhattaram Sayeef Salahuddin