Multiscale Simulations for Defect-Controlled Processing of Group IV Materials

Abstract

Multiscale approaches for the simulation of materials processing are becoming essential to the industrialization of future nanotechnologies, as they allow for a reduction in production costs and an enhancement of devices and applications. Their integration as modules of “digital twins”, i.e., a combined sequence of predictive chemical–physical simulations and trained black-box techniques, should ideally complement the real sequence of processes throughout all development and production stages, starting from the growth of materials, their functional manipulation and finally their integration in nano-devices. To achieve this framework, computational implementations at different space and time scales are necessary, ranging from the atomistic to the macro-scale. In this paper, we propose a general paradigm for the industrially driven computational modeling of materials by deploying a multiscale methodology based on physical–chemical simulations bridging macro, meso and atomic scale. We demonstrate its general applicability by studying two completely different processing examples, i.e., the growth of group IV crystals through physical vapor deposition and their thermal treatment through pulsed laser annealing. We indicate the suitable formalisms, as well as the advantages and critical issues associated with each scale, and show how numerical methods for the solution of the models could be coupled to achieve a complete and effective virtualization of the process. By connecting the process parameters to atomic scale modifications such as lattice defects or faceting, we highlight how a digital twin module can gain intrinsic predictivity far from the pre-assessed training conditions of black-box “Virtual Metrology” techniques.

Keywords

• digital twin modules
• process simulation
• multiscale modeling
• Kinetic Monte Carlo
• finite element methods