Optimization of thermionic cooling semiconductor heterostructures with deep learning techniques-Reference-Cited by-同舟云学术

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Optimization of thermionic cooling semiconductor heterostructures with deep learning techniques

Published:2023-09-27 Issue: Volume: Page:
ISSN:
Container-title:2023 International Conference on Simulation of Semiconductor Processes and Devices (SISPAD)
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Author:

Fernandez Julian G.¹,Etesse Guéric²,Comesaña Enrique³,Seoane Natalia¹,Zhu Xiangyu⁴,Hirakawa Kazuhiko⁴,Garcia-Loureiro Antonio¹,Bescond Marc²

Affiliation:

1. CiTIUS, University of Santiago de Compostela,Spain

2. Aix Marseille Université, CNRS, IM2NP,Marseille,France,133997

3. University of Santiago de Compostela,Escola Politécnica Superior de Enxeñaría,Lugo,Spain

4. University of Tokyo,Institute of Industrial Science,Tokyo,Japan,153-8505

Funder

Xunta de Galicia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10319472/10319477/10319607.pdf?arnumber=10319607

Reference10 articles.

1. Learning an adaptive learning rate schedule;xu,2019

2. Tune: A research platform for distributed model selection and training;liaw,2018

3. Temperature and voltage measurement in quantum systems far from equilibrium

4. Comprehensive Analysis of Electron Evaporative Cooling in Double-Barrier Semiconductor Heterostructures

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1. Cooling of 1 MW Electric Motors through Submerged Oil Impinging Jets for Aeronautical Applications;Aerospace;2024-07-17

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