Physical Origin of Negative Differential Resistance in V3O5 and Its Application as a Solid‐State Oscillator. Issue 8 (30th December 2022) - Das, Sujan Kumar; Nandi, Sanjoy Kumar; Marquez, Camilo Verbel; Rúa, Armando; Uenuma, Mutsunori; Puyoo, Etienne; Nath, Shimul Kanti; Albertini, David; Baboux, Nicolas; Lu, Teng; Liu, Yun; Haeger, Tobias; et al.
Abstract: Oxides that exhibit an insulator–metal transition can be used to fabricate energy‐efficient relaxation oscillators for use in hardware‐based neural networks but there are very few oxides with transition temperatures above room temperature. Here the structural, electrical, and thermal properties of V3 O5 thin films and their application as the functional oxide in metal/oxide/metal relaxation oscillators are reported. The V3 O5 devices show electroforming‐free volatile threshold switching and negative differential resistance (NDR) with stable (<3% variation) cycle‐to‐cycle operation. The physical mechanisms underpinning these characteristics are investigated using a combination of electrical measurements, in situ thermal imaging, and device modeling. This shows that conduction is confined to a narrow filamentary path due to self‐confinement of the current distribution and that the NDR response is initiated at temperatures well below the insulator–metal transition temperature where it is dominated by the temperature‐dependent conductivity of the insulating phase. Finally, the dynamics of individual and coupled V3 O5 ‐based relaxation oscillators is reported, showing that capacitively coupled devices exhibit rich non‐linear dynamics, including frequency and phase synchronization. These results establish V3 O5 as a new functional material for volatile threshold switching and advance the development of robust solid‐state neurons for neuromorphic computing. Abstract : The unique properties of V3 O5 and its application as a functional oxide layer in volatile memristive switching devices are reported, with a particular focus on the devlopment of oxide‐based relaxation oscillators as artificial neurons in hardware‐based neural networks.