A neural network trained on synthetic delta-T noise data estimates the temperature bias across atomic-scale junctions with mean error below 1 K when averaged over many junctions.
Anomalous electronic shot noise in resonant tunneling junctions
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
We study the behavior of shot noise in resonant tunneling junctions far from equilibrium. Quantum-coherent elastic charge transport can be characterized by a transmission function, that is the probability for an incoming electron at a given energy to tunnel through a potential barrier. In systems such as quantum point contacts, electronic shot noise is oftentimes calculated based on a constant (energy independent) transmission probability, a good approximation at low temperatures and under a small bias voltage. Here, we generalize these investigations to far from equilibrium settings by evaluating the contributions of electronic resonances to the electronic current noise. Our study extends canonical expressions for the voltage-activated shot noise and the recently discovered delta-T noise to the far from equilibrium regime, when a high bias voltage or a temperature difference is applied. In particular, when the Fermi energy is located on the shoulder of a broad resonance, we arrive at a formula for the shot noise revealing anomalous-nonlinear behavior at high bias voltage.
citation-role summary
citation-polarity summary
fields
cond-mat.mes-hall 1years
2024 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Machine learning delta-T noise for temperature bias estimation
A neural network trained on synthetic delta-T noise data estimates the temperature bias across atomic-scale junctions with mean error below 1 K when averaged over many junctions.