Electron Transport in Double-Barrier Semiconductor Heterostructures for Thermionic Cooling

Zhu, Xiangyu; Bescond, Marc; Onoue, Toshiki; Bastard, G.; Carosella, Francesca; Ferreira, Robson; Nagai, Naomi; Hirakawa, Kazuhiko

doi:10.1103/physrevapplied.16.064017

Cited by 6 publications

(2 citation statements)

References 28 publications

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“…By connecting the probe to the device, a net electron and energy current is produced. It can be calculated as follows, using the previously determined Green's functions of the device: (14) in which γ = 0 or 1 for the electron or energy current, respectively. The principle is now to find [T p ; µ p ] such that I 0 p and I 1 p vanish.…”

Section: Negf+h Simulation Methodologymentioning

confidence: 99%

“…Then, the challenge of managing self-induced heat [9] entails the exploration of innovative cooling solutions, as the ones grounded in solid-state physics [10,11,12]. In this context, this study focus on one of the most promising solid-state cooling devices, the asymmetric double-barrier heterostructures based on semiconductors, which had been validated as an effective integrated-chip cooling solution [13,14]. To capture the physics involved in these heterostructures, and, specifically, to evaluate the energy transfer between the semiconductor lattice and the conduction electrons, the performed simulations self-consistently couple the quantum non-equilibrium Green's function formalism for electrons with the heat equation (NEGF+H) [15,16].…”

Section: Introductionmentioning

confidence: 99%

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A novel machine learning workflow to optimize cooling devices grounded in solid-state physics

Fernandez,

Etesse,

Seoane

et al. 2024

Preprint

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Cooling devices grounded in solid-state physics are promising candidates for integrated-chip nanocooling applications. These devices are modeled by coupling the quantum non-equilibrium Green’s function for electrons with the heat equation (NEGF+H), which allows to accurately describe the energetic and thermal properties. We propose a novel machine learning (ML) workflow to accelerate the design optimization process of these cooling devices, alleviating the high computational demands of NEGF+H. This workflow, trained with NEGF+H data, obtains the optimum heterostructure designs that provide the best trade-off between the cooling power of the lattice (CP) and the electron temperature (Te). Using a vast search space of 1.18×105 different device configurations, we obtained a set of optimum devices with prediction relative errors lower than 4% for CP and 1% for Te. The ML workflow reduces the computational resources needed, from two days for a single NEGF+H simulation to 10s to find the optimum designs.

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Section: Negf+h Simulation Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel machine learning workflow to optimize cooling devices grounded in solid-state physics

Fernandez,

Etesse,

Seoane

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

A novel machine learning workflow to optimize cooling devices grounded in solid-state physics

Fernandez,

Etesse,

Seoane

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Cooling devices grounded in solid-state physics are promising candidates for integrated-chip nanocooling applications. These devices are modeled by coupling the quantum non-equilibirum Green’s function for electrons with the heat equation (NEGF+H), which allows to accurately describe the energetic and thermal properties. We propose a novel machine learning (ML) workflow to accelerate the design optimization process of these cooling devices, alleviating the high computational demands of NEGF+H. This methodology, trained with NEGF+H data, obtains the optimum heterostructure designs that provide the best trade-off between the cooling power of the lattice (CP) and the electron temperature ( ). Using a vast search space of different device configurations, we obtained a set of optimum devices with prediction relative errors lower than for CP and for T e . The ML workflow reduces the computational resources needed, from two days for a single NEGF+H simulation to 10 s to find the optimum designs.

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Rate equations description of the asymmetric double barrier electronic cooler

Philippe,

Carosella,

Zhu

et al. 2023

Journal of Applied Physics

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Recent experimental results showed that an electron gas in an asymmetrical double barrier heterostructure can be effectively cooled down under resonant tunneling condition, thus leading to the realization of an electronic cooler. The cooling process is a multi-parameters phenomenon and it is desirable to handle this problem through a reasonably simple approach, in order to understand the role of each parameter. To this end, we present a rate equation modeling of the electron cooling. We model the resonant tunnel injection of the electrons in the well and their thermionic emission assisted by Longitudinal Optical (LO) phonons absorption and emission. The influence of several parameters on the electronic temperature is discussed. This simple model compares rather well to the predictions of non-equilibrium Green function approach and to the experiments.

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Electron Transport in Double-Barrier Semiconductor Heterostructures for Thermionic Cooling

Cited by 6 publications

References 28 publications

A novel machine learning workflow to optimize cooling devices grounded in solid-state physics

A novel machine learning workflow to optimize cooling devices grounded in solid-state physics

A novel machine learning workflow to optimize cooling devices grounded in solid-state physics

Rate equations description of the asymmetric double barrier electronic cooler

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