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D. TSIOTAS1,* , L. MAGAFAS2
- Department of Regional and Economic Development, Agricultural University of Athens, Greece, Nea Poli, Amfissa, 33100, Greece
- Laboratory of Complex Systems, Department of Physics, International Hellenic University, Kavala Campus, St. Loukas, 65404, Greece
This paper applies a multilevel analysis based on statistical physics to detect structures of semiconductor a-SiC:H thin-film alloys with the best possible electrical performance expressed as a function of the temperature and hydrogen flow. Toward promoting the multidisciplinary demand in statistical physics, this paper broadens the conceptualization of the natural visibility graph algorithm by applying it to conductivity activation energy instead of time series; to study datasets described by insufficient information as a complex network. The statistical analysis shows that the conductivity activation energy is statistically indifferent to the temperature without the supply of hydrogen flows. However, the variation captured amongst conductivity activation energy levels supplied by hydrogen flows does not appear statistically significant. Hydrogen flows to the semiconductor body leads to better semiconductor structures at lower temperatures, where a zone of 17-20sccm has better conductivity activation energy levels. Finally, the network analysis reveals a rich-club configuration of temperatures and leads to three distinct conductivity activation energy states, corresponding to different structural (semiconductor) behaviors of the a-SiC:H thin-film alloys. The overall analysis proposes a framework of dealing with complexity under insufficient information, where traditional methods of statistical physics are of marginal functionality..
Structural optimization, Complex network analysis of time series, Community detection.
Submitted at: Feb. 4, 2021
Accepted at: Nov. 24, 2021
D. TSIOTAS, L. MAGAFAS, A new approach for studying experimental results of a-SiC:H thin-films alloys based on statistical physics, Journal of Optoelectronics and Advanced Materials Vol. 23, Iss. 11-12, pp. 612-623 (2021)
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