2025/06/01 by Zsigmond Pollner, Pollner, Zsigmond, Tímea Nóra Török +21 · 1 citation
Engineering · Materials Science · Physics and Astronomy · #FOS: Physical sciences #GaN-based semiconductor devices and materials #Materials Science (cond-mat.mtrl-sci) #Mesoscale and Nanoscale Physics (cond-mat.mes-hall) #Optical Wireless Communication Technologies #Transition Metal Oxide Nanomaterials
paper · pdf · doi:10.48550/arxiv.2506.01139
openalex publication_date 2025/06/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Oscillating neural networks are promising candidates for a new computational paradigm, where complex optimization problems are solved by physics itself through the synchronization of coupled oscillating circuits. Nanoscale VO2 Mott memristors are particularly promising building blocks for such oscillating neural networks. Until now, however, not only the maximum frequency of VO2 oscillating neural networks, but also the maximum frequency of individual VO2 oscillators has been severely limited, which has restricted their efficient and energy-saving use. In this paper, we show how the oscillating frequency can be increased by more than an order of magnitude into the 100 MHz range by optimizing the sample layout and circuit layout. In addition, the physical limiting factors of the oscillation frequencies are studied by investigating the switching dynamics. To this end, we investigate how much the set and reset times slow down under oscillator conditions compared to the fastest switching achieved with single dedicated pulses. These results pave the way towards the realization of ultra-fast and energy-efficient VO2-based oscillating neural networks.