Accelerated Modelling of Interfaces for Electronic Devices using Graph Neural Networks

Pratik Brahma, Krishnakumar Sivaganesh Bhattaram, Sayeef Salahuddin

Submission Track: Papers

Submission Category: Automated Material Characterization

Keywords: Graph Neural Networks, Heterostructures, Transistor, Molecular Dynamics, Ballistic Transport, Nanoslabs

TL;DR: We implement graph neural networks to accelerate an end-to-end simulation pipeline from transistor dimensions to atomic configuration to electronic transport properties.

Abstract: 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.

Digital Discovery Special Issue: Yes

Submission Number: 87