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Register a new userOn the occurrence of Berezinskii-Kosterlitz-Thouless behavior in highly anisotropic cuprate superconductors
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T. Schneider
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The conflicting observations in the highly anisotropic Bi2Sr2CaCu2O8+x, vidence for BKT behavior emerging from magnetization data and smeared 3D-xy behavior, stemming form the temperature dependence of the magnetic in-plane penetration depth are traced back to the rather small ratio, gsic+/gsic-=0.45, between the c-axis correlation length probed above (+) and below (-) Tc, and the comparatively large anisotropy. The latter leads to critical amplitudes gsic0+,-which are substantially smaller than the distance between two CuO2 double layers. In combination with gsic+/gsic-=0.45 and in contrast to the situation below Tc the c-axis correlation length gsic exceeds the distance between two CuO2 double layers very close to Tc only. Below this narrow temperature regime where 3D-xy fluctuations dominate, there is then an extended temperature regime where the units with two CuO2 double layers are nearly uncoupled so that 2D thermal fluctuations dominate and BKT features are observable.
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One of the hallmarks of the Berezinskii-Kosterlitz-Thouless (BKT) transition in two-dimensional (2D) superconductors is the universal jump of the superfluid density, that can be indirectly probed via the non-linear exponent of the current-voltage $IV$ characteristics. Here, we compare the experimental measurements of $IV$ characteristics in two cases, namely NbN thin films and SrTiO$_3$-based interfaces. While the former display a paradigmatic example of BKT-like non-linear effects, the latter do not seem to justify a BKT analysis. Rather, the observed $IV$ characteristics can be well reproduced theoretically by modelling the effect of mesoscopic inhomogeneity of the superconducting state. Our results offer an alternative perspective on the spontaneous fragmentation of the superconducting background in confined 2D systems.
We demonstrate that a machine learning technique with a simple feedforward neural network can sensitively detect two successive phase transitions associated with the Berezinskii-Kosterlitz-Thouless (BKT) phase in q-state clock models simultaneously by analyzing the weight matrix components connecting the hidden and output layers. We find that the method requires only a data set of the raw spatial spin configurations for the learning procedure. This data set is generated by Monte-Carlo thermalizations at selected temperatures. Neither prior knowledge of, for example, the transition temperatures, number of phases, and order parameters nor processed data sets of, for example, the vortex configurations, histograms of spin orientations, and correlation functions produced from the original spin-configuration data are needed, in contrast with most of previously proposed machine learning methods based on supervised learning. Our neural network evaluates the transition temperatures as T_2/J=0.921 and T_1/J=0.410 for the paramagnetic-to-BKT transition and BKT-to-ferromagnetic transition in the eight-state clock model on a square lattice. Both critical temperatures agree well with those evaluated in the previous numerical studies.
Superconducting hybrid junctions are revealing a variety of novel effects. Some of them are due to the special layout of these devices, which often use a coplanar configuration with relatively large barrier channels and the possibility of hosting Pearl vortices. A Josephson junction with a quasi ideal two-dimensional barrier has been realized by growing graphene on SiC with Al electrodes. Chemical Vapor Deposition offers centimeter size monolayer areas where it is possible to realize a comparative analysis of different devices with nominally the same barrier. In samples with a graphene gap below 400 nm, we have found evidence of Josephson coherence in presence of an incipient Berezinskii-Kosterlitz-Thouless transition. When the magnetic field is cycled, a remarkable hysteretic collapse and revival of the Josephson supercurrent occurs. Similar hysteresis are found in granular systems and are usually justified within the Bean Critical State model (CSM). We show that the CSM, with appropriate account for the low dimensional geometry, can partly explain the odd features measured in these junctions.
The Berezinskii-Kosterlitz-Thouless (BKT) transition is expected to have a clear signature on the specific heat. The singularity at the transition temperature $T_{BKT}$ is predicted to be immeasurable, and a broad non-universal peak is expected at $T>T_{BKT}$. Up to date this has not been observed in two-dimensional superconductors. We use a unique highly sensitive technique to measure the specific heat of ultrathin Pb films. We find that thick films exhibit a specific heat jump at $T_C$ that is consistent with BCS theory. As the film thickness is reduced below the superconducting coherence length and the systems enters the 2D limit the specific heat reveals BKT-like behavior. We discuss these observations in the framework of the continuous BCS-BKT crossover as a function of film thickness.
The precondition for the BKT transition in thin superconducting films, the logarithmic intervortex interaction, is satisfied at distances short relative to $Lambda=2lambda^2/d$, $lambda$ is the London penetration depth of the bulk material and $d$ is the film thickness. For this reason, the search for the transition has been conducted in samples of the size $L<Lambda$. It is argued below that film edges turn the interaction into near exponential (short-range) thus making the BKT transition impossible. If however the substrate is superconducting and separated from the film by an insulated layer, the logarithmic intervortex interaction is recovered and the BKT transition should be observable.
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