Joule Heating and Thermal Transport in 2D Materials for Device Applications

Heat dissipation and thermal management is a rising concern for nanoelectronic devices and threatens to curtail their adoption in integrated circuits, sensors, and energy converters. Joule heating due to dissipation in the channel region of nanoelectronic devices causes increased temperature and may lead to mobility degradation and long-term reliability issues. Here we study thermal transport and cross-plane thermal boundary conductance in a variety of “beyond graphene” 2D materials and few-layer stacks on several amorphous and crystalline substrates using a combination of first principles methods and Boltzmann transport of phonons. We employ machine learning to accelerate the discovery of 2D-substrate pairings with enhanced thermal conductance. Beyond that, we couple electronic and thermal transport to study dissipation in field effect MOS transistors and show that heat dissipation is non-uniform and that self-heating reduces mobility. We find that judicious selection of the number of layers and substrate can significantly reduce the deleterious effects of Joule heating.

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Publication Details

Published
2024-10-21
DOI
https://doi.org/10.1109/nmdc58214.2024.10894294
Primary Topic
Heat Transfer and Optimization
Type
article
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article

Joule Heating and Thermal Transport in 2D Materials for Device Applications

Zlatan Akšamija
Heat Transfer and Optimization
article

Joule Heating and Thermal Transport in 2D Materials for Device Applications

Zlatan Akšamija
article en

Abstract

Heat dissipation and thermal management is a rising concern for nanoelectronic devices and threatens to curtail their adoption in integrated circuits, sensors, and energy converters. Joule heating due to dissipation in the channel region of nanoelectronic devices causes increased temperature and may lead to mobility degradation and long-term reliability issues. Here we study thermal transport and cross-plane thermal boundary conductance in a variety of “beyond graphene” 2D materials and few-layer stacks on several amorphous and crystalline substrates using a combination of first principles methods and Boltzmann transport of phonons. We employ machine learning to accelerate the discovery of 2D-substrate pairings with enhanced thermal conductance. Beyond that, we couple electronic and thermal transport to study dissipation in field effect MOS transistors and show that heat dissipation is non-uniform and that self-heating reduces mobility. We find that judicious selection of the number of layers and substrate can significantly reduce the deleterious effects of Joule heating.

University of Utah (US)
Affordable and clean energy
Openalex Percentile: Top 72%
Heat Transfer and Optimization
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