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Sebastian Werner Schmid

Publications and source records attributed to Sebastian Werner Schmid.

2 recordsLinked to original sources

Voltage-driven transition from steady-state fluctuations to phase-transition noise in nanoscale VO$_2$ devices

We investigate the electrically driven metal-to-insulator transition (MIT) in nanoscale vanadium dioxide (VO$_2$) Mott memristor through noise spectroscopy and two-dimensional resistor network simulations. Our experiments focus on both the insulating phase as the applied voltage approaches the threshold voltage (set transition) and the metallic phase as the voltage is reduced toward the reset voltage (reset transition). In both regimes, we observe an order of magnitude increase in relative current noise near the transition points. To analyze the origin of this noise enhancement, we use simulations that capture the stochastic dynamics of the phase transition. The simulations indicate that the increased noise stems from amplified phase fluctuations near the percolation threshold, where competing metallic and insulating domains lead to dynamic reconfiguration of the conduction paths. In addition, we show that the precursor current fluctuations observed near the switching threshold are consistent with the threshold voltage variability measured in repeated switching cycles, indicating that the noise sets a lower bound on the achievable variance. These findings offer key insights into the non-equilibrium processes governing phase transitions in nanoscale VO$_2$ devices under electrical stimuli.

cond-mat.mes-hall

VO$_2$ oscillator circuits optimized for ultrafast, 100 MHz-range operation

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 VO$_2$ Mott memristors are particularly promising building blocks for such oscillating neural networks. Until now, however, not only the maximum frequency of VO$_2$ oscillating neural networks, but also the maximum frequency of individual VO$_2$ 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 VO$_2$-based oscillating neural networks.

cond-mat.mes-hall