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Memristive Switching Behavior of Sol–Gel Derived Ga₂O₃ Thin Films

Jamoliddin X. MurodovTashkent State Technical University named after Islam Karimov, Tashkent, Uzbekistan; Center of Nanotechnology Development, National University of UzbekistanSh. U. YuldashevCenter of Nanotechnology Development, National University of Uzbekistan, Tashkent, UzbekistanAzamat O. ArslanovNational University of Uzbekistan named after Mirzo Ulugbek, Tashkent, UzbekistanNoiba U. BotirovaCenter of Nanotechnology Development, National University of Uzbekistan, Tashkent, UzbekistanJavohir Sh. XudoyqulovNational University of Uzbekistan named after Mirzo Ulugbek, Tashkent, Uzbekistan; Central Asian University, Tashkent, UzbekistanIlyos Kh. KhudaykulovArifov Institute of Ion-Plasma and Laser Technologies of Uzbekistan Academy of Sciences, Tashkent, UzbekistanMarguba S. MirkamilovaTashkent State Technical University named after Islam Karimov, Tashkent, UzbekistanUtkur E. JurayevTashkent State Technical University named after Islam Karimov, Tashkent, UzbekistanAzlarxon M. Tillaboyev
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Annotatsiya

Gallium oxide (Ga₂O₃) is an ultrawide bandgap semiconductor (~4.8–5.0 eV) that has recently gained considerable attention for next-generation nanoelectronic and memory devices owing to its superior breakdown field, chemical durability, and thermal robustness. In this study, Ga₂O₃ thin films were fabricated through a sol–gel spin-coating route and subsequently annealed at 1000 °C. X-ray diffraction revealed the structural evolution from an amorphous state to the stable monoclinic β-Ga₂O₃ phase after annealing. Electrical measurements exhibited reproducible bipolar resistive switching with an ON/OFF resistance ratio exceeding 102 and relatively low set/reset voltages. The observed switching is interpreted within the framework of conductive filament formation and rupture, predominantly governed by oxygen vacancy dynamics. The combination of low-cost synthesis, scalable processing, and robust memristive performance highlights sol–gel derived Ga₂O₃ thin films as strong contenders for future resistive random-access memory (RRAM) architectures and neuromorphic computing technologies.

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