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"Spiking neural network"

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"Spiking neural network"

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In this study, we systematically investigated the effects of thermal atomic layer deposition (ALD) conditions on the electrical characteristics of conductive-filament-based volatile threshold switching memristors with a Pt/Al₂O₃/Ag structure. Although volatile threshold switching memristors have attracted significant attention for neuromorphic computing applications such as spiking neural networks, the impact of dielectric deposition conditions on their switching behavior remains relatively unexplored. The number of ALD cycles was varied from 40 to 200, and the deposition temperature was varied from 50 to 250°C to evaluate their effects on threshold voltage (Vth), off-state resistance (Roff), and device yield. Devices fabricated using 75–150 ALD cycles showed clear volatile threshold switching behavior with relatively high device yield, whereas excessively low or high cycle numbers resulted in short-type and open-type failures, respectively. In addition, Vth and Roff tended to increase at higher deposition temperatures, while the device yield significantly degraded above 150°C. These results indicate that the number of ALD cycles and the deposition temperature define a critical process window for achieving volatile threshold switching while suppressing both short-type and open-type failures. This study provides practical guidelines for optimizing dielectric ALD conditions in conductive-filament-based volatile threshold switching memristors for future neuromorphic hardware applications.
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