Amorphous Semiconductors by Kugler, Sándor; Shimakawa, Koichi

By Kugler, Sándor; Shimakawa, Koichi

Explains easy methods to use computing device modelling to appreciate amorphous semiconductors for researchers in stable country physics and semiconductor engineering.

summary: Explains how you can use computing device modelling to appreciate amorphous semiconductors for researchers in reliable country physics and semiconductor engineering

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Adv. , 9, 3021–3027. Gereben, O. and Pusztai, L. (2012) RMC_POT, a computer code for Reverse Monte Carlo modeling the structure of disordered systems containing molecules of arbitrary complexity. J. Comput. , 33(29), 2285–2291. , and Jóvári, P. (2015) Short range order in Ge–As–Se glasses. J. Alloys Compounds, 623, 454–459.

2004) Growth of amorphous silicon: Low energy molecular dynamics simulation of atomic bombardment. Mol. , 30(1), 17–22. L. and Pusztai, L. (1988) Reverse Monte Carlo simulation: A new technique for the determination of disordered structures. Mol. , 1, 359–367. Evrard, G. and Pusztai, L. (2005) Reverse Monte Carlo modelling of the structure of disordered materials with RMC++: A new implementation of the algorithm in C++. J. : Condens. Matter, 17, S1–S13. , and Pusztai, L. (2007) A new version of the RMC++ reverse Monte Carlo programme, aimed at investigating the structure of covalent glasses.

Jóvári et al. 2003 [16]. Reproduced with permission from the American Physical Society. 1 0 1 2 3 4 5 6 r(Å) it was not possible to determine whether the amorphous selenium consisted of long chains, Se8 rings, or a mixture of the two. Both types of model were possible to construct whenever the structure factors agreed quantitatively with the measured data. The sample measured was made from crystalline Se consisting of long chains, and so it was suggested that this sample contained primarily chainlike structures.

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