Quantitatively Predicting Modal Thermal Conductivity of Nanocrystalline Si by full band Monte Carlo simulations

TL;DR

This paper uses advanced Monte Carlo simulations with full phonon band data and atomistic Green's function calculations to accurately predict how nanocrystalline silicon's thermal conductivity varies with grain size, aiding thermoelectric design.

Contribution

It introduces a combined simulation approach that accurately models phonon transport and boundary transmission in nanostructured silicon, improving predictive capabilities.

Findings

Thermal conductivity decreases significantly as grain size reduces from 550 nm to 10 nm.

The contribution of long mean free path phonons increases with decreasing grain size.

Predicted thermal conductivity ratios align well with experimental data.

Abstract

Thermal transport of nanocrystalline Si is of great importance for the application of thermoelectrics. A better understanding of the modal thermal conductivity of nanocrystalline Si will be expected to benefit the efficiency of thermoelectrics. In this work, the variance reduced Monte Carlo simulation with full band of phonon dispersion is applied to study the modal thermal conductivity of nanocrystalline Si. Importantly, the phonon modal transmissions across the grain boundaries which are modeled by the amorphous Si interface are calculated by the mode-resolved atomistic Greens function method. The predicted ratios of thermal conductivity of nanocrystalline Si to that of bulk Si agree well with that of the experimental measurements in a wide range of grain size. The thermal conductivity of nanocrystalline Si is decreased from 54 percent to 3 percent and the contribution of phonons with mean free path larger than the grain size increases from 30 percent to 96 percnet as the grain size decreases from 550 nm to 10 nm. This work demonstrates that the full band Monte Carlo simulation using phonon modal transmission by the mode-resolved atomistic Greens function method can capture the phonon transport picture in complex nanostructures, and therefore can provide guidance for designing high performance Si based thermoelectrics.