Parametric Study for Optimal Performance of Coulomb-Coupled Quantum Dots

TL;DR

This paper investigates how various parameters influence the performance of Coulomb-coupled quantum dot heat engines and uses genetic algorithms to optimize their output power and efficiency.

Contribution

It provides a detailed parametric analysis and optimization method for quantum heat engines based on Coulomb-coupled quantum dots.

Findings

Optimal parameters maximize output power and efficiency.

Sequential tunneling and cotunneling dominate under different conditions.

Genetic algorithms effectively find optimal performance configurations.

Abstract

We study the optimal output power and efficiency of the three-terminal quantum heat engine with Coulomb-coupled quantum-dots (CCQD). It has been well known that in the weak coupling regime, two kinds of dominant transport mechanisms are sequential tunneling and cotunneling processes in CCQD. What process becomes dominant, which can be controlled by several parameters such as temperature difference, bias voltage, Coulomb interaction and tunneling parameters, is one of the key problems to determine the performance of the heat engine. We show the parametric dependence of the output power and coefficient and find the optimal performance of this CCQD heat engine through genetic algorithm.