Quantitatively predicting modal thermal conductivity of nanocrystalline Si by full-band Monte Carlo simulations

Lina Yang, Yi Jiang, and Yanguang Zhou
Phys. Rev. B 104, 195303 – Published 11 November 2021
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Abstract

Thermal transport in nanocrystalline Si is of great importance for many advanced applications. A better understanding of the modal thermal conductivity of nanocrystalline Si will be expected. In this work, the efficient variance reduced Monte Carlo simulation with full band 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 Green's 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 ratio of thermal conductivity of nanocrystalline Si is decreased from 54% to 3% and the contribution of phonons with mean free path larger than the grain size increases from 30% to 96% as the grain size decreases from 550 to 10 nm. This work demonstrates that the full band Monte Carlo simulation using phonon modal transmission by the mode-resolved atomistic Green's function method can effectively capture the phonon transport picture in complex nanostructures, and therefore can provide guidance for designing Si based devices with better performance.

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  • Received 12 July 2021
  • Revised 17 October 2021
  • Accepted 20 October 2021

DOI:https://doi.org/10.1103/PhysRevB.104.195303

©2021 American Physical Society

Physics Subject Headings (PhySH)

Condensed Matter, Materials & Applied PhysicsStatistical Physics & ThermodynamicsInterdisciplinary Physics

Authors & Affiliations

Lina Yang1,*, Yi Jiang1, and Yanguang Zhou2,†

  • 1School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, China
  • 2Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Clear water bay, Kowloon, Hong Kong SAR, China

  • *yangln@bit.edu.cn
  • maeygzhou@ust.hk

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Issue

Vol. 104, Iss. 19 — 15 November 2021

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