On Simulating Thin-Film Processes at the Atomic Scale Using Machine Learned Force Fields

TL;DR

This paper demonstrates the development of machine learned force fields for simulating thin-film processes at the atomic level, enabling detailed insights into chemical mechanisms and reactions at the gas-surface interface.

Contribution

It introduces a method to efficiently build MLFFs for process simulations and applies it to atomic layer deposition and etching of relevant materials.

Findings

MLFFs can accurately model thin-film processes

Successful simulation of HfO2 deposition

Effective modeling of MoS2 etching

Abstract

Atomistic modeling of thin-film processes provides an avenue not only for discovering key chemical mechanisms of the processes but also to extract quantitative metrics on the events and reactions taking place at the gas-surface interface. Molecular dynamics (MD) is a powerful computational method to study the evolution of a process at the atomic scale, but studies of industrially relevant processes usually require suitable force fields, which are in general not available for all processes of interest. However, machine learned force fields (MLFF) are conquering the field of computational materials and surface science. In this paper, we demonstrate how to efficiently build MLFFs suitable for process simulations and provide two examples for technologically relevant processes: precursor pulse in the atomic layer deposition of HfO2 and atomic layer etching of MoS2.