Accounting for Heat Release in Small Volumes of Matter on the Example of the Growth of ZnO Microrods: Search for a Modeling Technique

Author:

Matyushkin I. V.,Telminov O. A.,Mikhaylov A. N.

Publisher

Pleiades Publishing Ltd

Subject

Materials Chemistry,Electrical and Electronic Engineering,Condensed Matter Physics,Electronic, Optical and Magnetic Materials

Reference25 articles.

1. Stempkovsky, A.L., Gavrilov, S.V., Matyushkin, I.V., and Teplov, G.S., On the issue of application of cellular automata and neural networks methods in VLSI design, Opt. Memory Neural Networks, 2016, vol. 25, no. 2, pp. 72–78. https://www.elibrary.ru/wvvldl.https://doi.org/10.3103/S1060992X16020065

2. Sidorenko, K.V., Gorshkov, O.N., and Kasatkin, A.P., Application of the kinetic Monte Carlo method for calculating I–V curves and heat transfer in memristive structures based on stabilized zirconium dioxide, Trudy XXI Mezhdunar. simpoziuma Nanofizika i nanoelektronika (Proc. 21st Int. Symp. on Nanophysics and Nanoelectronics, Nizhnii Novgorod: Nizhegorod. Gos. Univ. im. N.I. Lobachevskogo, 2017, vol. 2, pp. 716–717.

3. Menzel, S., Waters, M., Marchewka, A., Böttger, U., Dittmann, R., and Waser, R., Origin of the ultra-nonlinear switching kinetics in oxide-based resistive switches, Adv. Funct. Mater., vol. 21, no. 23, pp. 4487–4492. https://doi.org/10.1002/adfm.201101117

4. Guseinov, D.V., Korolev, D.S., Belov, A.I., Okulich, E.V., Okulich, V.I., Tetelbaum, D.I., and Mikhaylov, A.N., Flexible Monte-Carlo approach to simulate electroforming and resistive switching in filamentary metal-oxide memristive devices, Modell. Simul. Mater. Sci. Eng., 2020, vol. 28, no. 1, рр. 015007–015023. https://doi.org/10.1088/1361-651X/ab580e

5. Zhang, X., Yang, Sh., Tu, Ch.-G., Kiang, Y.-W., and Yang, C.C., Growth model of a GaN nanorod with the pulsed-growth technique of metalorganic chemical vapor deposition, Cryst. Growth Des., vol. 18, no. 7, pp. 3767–3773. https://doi.org/10.1021/acs.cgd.7b01605

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