Informatics and Applications
2020, Volume 14, Issue 4, pp 91-99
APPLICATION OF MULTISCALE APPROACH AND DATA SCIENCES FOR MODELING THERMAL CONDUCTIVITY IN LAYERED STRUCTURES
- K. K. Abgaryan
- I. S. Kolbin
Abstract
Modeling thermal properties of layered structures is currently a popular area of scientific research. This is due to the constantly growing speed of operation of microelectronic elements often based on layered structures that release more and more energy during operation in the form of heat which must be removed to avoid overheating and loss of functional properties of devices. The paper presents an integration approach that allows one to combine the methods of multiscale modeling and data analysis. It is shown that application of this approach makes it possible to obtain a new quality when solving the problem of constructing a model of heat transfer in a two-layer GaAs/AlAs structure. The effectiveness of use of machine learning methods for analyzing the dependence of the effective thermal conductivity coefficient of laminated materials on structural features and external factors is shown. The development of the proposed approach will be able to provide formation of information for reasonable selection of materials for layered structures for microelectronic devices.
[+] References (18)
- Mark, A., A. B. Tepole, W R. Cannon, et al. 2019. Integrating machine learning and multiscale modeling- perspectives, challenges, and opportunities in the bio-logical, biomedical, and behavioral sciences. NPJ Digital Medicine2(1):115. 11 p. doi: 10.1038/s41746-019-0193-y.
- Ladygin, V., P. Korotaev, A. Yanilkin, and A. Shapeev. 2020. Lattice dynamics simulation using machine learning interatomic potentials. Comp. Mater. Sci. 172:109333.
- Abgaryan, K. K. 2017. Mnogomasshtabnoe modelirovanie v zadachakh strukturnogo materialovedeniya [Multiscale modeling in material science problems]. Moscow: MAKS Press. 284 p.
- Abgaryan, K. K. 2018. Informatsionnaya tekhnologiya postroeniya mnogomasshtabnykh modeley v zadachakh vychislitel'nogo materialovedeniya [Information technol-ogy in the construction of multiscale models in problems of computational materials science]. Sistemy vysokoy do- stupnosti [High Availability Systems] 14(2):9-15.
- Kohn, W., and L. J. Sham. 1965. Self-consistent equations including exchange and correlation effects. Phys. Rev. A 140(4A):A1133-A1138. doi: 10.1103/PhysRev.140.A1133.
- Jia, Weile, Han Wang, Mohan Chen, el al. 2020. Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning. Cornell University. arXiv:2005.00223 [physics.comp-ph]. Avail-able at: https://arxiv.org/pdf/2005.00223.pdf (accessed November 9, 2020).
- Khvesyuk, V. I., and A. S. Skryabin. 2017. Heat conduc-tion in nanostructures. High Temp. 55(3):434-456. doi: 10.1134/S0018151X17030129.
- Vermeersch, B., J. Carrete, and N. Mingo. 2016. Cross-plane heat conduction in thin films with ab-initio phonon dispersions and scattering rates. Appl. Phys. Lett. 108(19):193104. doi: 10.1063/1.4948968.
- Carrete, J., B. Vermeersch, A. Katre, A. Roekeghem, T. Wang, G. Madsen, and N. Mingo. 2017. ÀlmaBTE: A solver of the space-time dependent Boltzmann transport equation for phonons in structured materials. Comput. Phys. Commun. 220C:351-362. doi: 10.1016/j.cpc.2017.06.023.
- Chung, J. D., A. J. H. McGaughey, and M. Kaviany. 2004. Role of phonon dispersion in lattice thermal conduc-tivity modeling. J. Heat Transf. 126(3):376-380. doi: 10.1115/1.1723469.
- Loy, J. M., J.Y. Murthy, and D.A. Singh. 2012. Fast hybrid Fourier-Boltzmann transport equation solver for nongray phonontransport. J. Heat Transf. 135(1):011008. doi: 10.1115/1.4007654.
- Broido, D.A., M. Malorny, G. Birner, N. Mingo, and
D. A. Stewart. 2007. Intrinsic lattice thermal conductivity of semiconductors from first principles. Appl. Phys. Lett. 91(23):231922. doi: 10.1063/1.2822891.
- Powell, D. 2006. Elasticity, lattice dynamics and parameterization techniques for the Tersoff potential applied to elemental and type III-V semiconductors. Sheffield: University of Sheffield. PhD Thesis. 252 p.
- Abgaryan, K. K., I. V. Mutigullin, S. I. Uvarov, and
O. V. Uvarova. 2019. Multiscale modeling of clusters of point defects in semiconductor structures. CEUR Workshop Proceedings. Eds. S. I. Smagin and A. A. Zatsarinnyy. Khabarovsk. 2426:43-51.
- Li, W., L. Lindsay, D.A. Broido, D.A. Stewart, and N. Mingo. 2012. Thermal conductivity of bulk and
nanowire Mg2SiSni- alloys from first principles. Phys. Rev. B 86(17):174307. doi: 10.1103/PhysRevB.86.174307.
- Li, W, J. Carrete, N.A. Katcho, and N. Mingo. 2014. A solver of the Boltzmann transport equation for phonons. Comput. Phys. Commun. 185(6):1747-1758. doi: 10.1016/j.cpc.2014.02.015.
- Carrete, J., B. Vermeersh, L. Thumfart, R. R. Kakodkar, G. Trevisi, P. Frigeri, L. Seravalli, J. P. Feser, A. Rastelli, and N. Mingo. 2018. Predictive design and experimental realization of InAs/GaAs superlatices with tailored thermal conductivity. J. Phys. Chem. C 122(7):4054-4062. doi: 10.1021/acs.jpcc.7b11133.
- Muraki, K., S. Fukatsu, Y. Shiraki, and R. Ito. 1992. Sur-face segregation of In atoms during molecular beam epitaxy and its influence on the energy levels in InGaAs/GaAs quantum wells. Appl. Phys. Lett. 61(5):557-559. doi: 10.1063/1.107835.
[+] About this article
Title
APPLICATION OF MULTISCALE APPROACH AND DATA SCIENCES FOR MODELING THERMAL CONDUCTIVITY IN LAYERED STRUCTURES
Journal
Informatics and Applications
2020, Volume 14, Issue 4, pp 91-99
Cover Date
2020-12-30
DOI
10.14357/19922264200413
Print ISSN
1992-2264
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
multiscale modeling; integration approach; layered structures; predictive modeling; kinetic Boltzmann equation, thermal conductivity coefficient; data analysis methods
Authors
K. K. Abgaryan  ,  and I. S. Kolbin ,
Author Affiliations
A. A. Dorodnicyn Computing Center, Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 40 Vavilov Str., Moscow 119333, Russian Federation
Moscow Aviation Institute (National Research University), 4 Volokolamskoe Shosse, Moscow 125080, Russian Federation
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