Predictive design and experimental realization of InAs/GaAs superlattices with tailored thermal conductivity

TL;DR

This paper presents a predictive ab-initio method for calculating the thermal conductivity of InAs/GaAs superlattices, validated by experiments, and discusses how controlling composition profiles can optimize heat dissipation in devices.

Contribution

The study introduces a new ab-initio approach for predicting superlattice thermal conductivity and demonstrates its accuracy with experimental data, highlighting the impact of finite-thickness effects and composition control.

Findings

Good agreement between theory and experiment when realistic composition profiles are used.

Finite-thickness effects significantly influence thermal conductivity predictions.

Minimizing In segregation could enhance thermal conductivity of superlattices.

Abstract

We demonstrate an ab-initio predictive approach to computing the thermal conductivity ($\kappa$) of InAs/GaAs superlattices (SLs) of varying period, thickness, and composition. Our new experimental results illustrate how this method can yield good agreement with experiment when realistic composition profiles are used as inputs for the theoretical model. Due to intrinsic limitations to the InAs thickness than can be grown, bulk-like SLs show limited sensitivity to the details of their composition profile, but the situation changes significantly when finite-thickness effects are considered. If In segregation could be minimized during the growth process, SLs with significantly higher $\kappa$ than that of the random alloy with the same composition would be obtained, with the potential to improve heat dissipation in InAs/GaAs-based devices.