Resistive Switching in Powder Semiconductor Materials

This paper presents the results of an experimental and theoretical study of resistive switching in compressed powder samples of n- and p-type silicon, p-type germanium, and p-type copper oxide. The measured current–voltage characteristics exhibit a symmetrical shape and hysteresis, the loop area of which depends on the material resistivity and dispersion. Germanium and copper oxide have relatively low thermal conductivities, and so thermal breakdown occurs in these materials due to insufficient heat dissipation, while avalanche breakdown is typical for silicon. A theoretical model of resistive switching in powder material is developed, based on the application of an activation function and automation principles. Calculations performed within this model qualitatively describe the obtained experimental data.

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Publication Details

Journal
Russian Electrical Engineering
Published
2025-12-01
DOI
https://doi.org/10.3103/s1068371225701524
Primary Topic
Advanced Memory and Neural Computing
Type
article
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article

Resistive Switching in Powder Semiconductor Materials

Yu. K. Altudov, S. Sh. Rekhviashvili, D. S. Gaev
Russian Electrical Engineering
Advanced Memory and Neural Computing
article

Resistive Switching in Powder Semiconductor Materials

Yu. K. Altudov, S. Sh. Rekhviashvili, D. S. Gaev
article en

Abstract

This paper presents the results of an experimental and theoretical study of resistive switching in compressed powder samples of n- and p-type silicon, p-type germanium, and p-type copper oxide. The measured current–voltage characteristics exhibit a symmetrical shape and hysteresis, the loop area of which depends on the material resistivity and dispersion. Germanium and copper oxide have relatively low thermal conductivities, and so thermal breakdown occurs in these materials due to insufficient heat dissipation, while avalanche breakdown is typical for silicon. A theoretical model of resistive switching in powder material is developed, based on the application of an activation function and automation principles. Calculations performed within this model qualitatively describe the obtained experimental data.

Russian Electrical EngineeringVol. 96(12)
Kabardino-Balkarian State University (RU), Kabardino-Balkarian Scientific Center (RU)
Openalex Percentile: Top 34%
Advanced Memory and Neural Computing
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