2016
DOI: 10.1007/s10909-016-1606-9
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Confocal Annular Josephson Tunnel Junctions

Abstract: The physics of Josephson tunnel junctions drastically depends on their geometrical configurations and here we show that also tiny geometrical details play a determinant role. More specifically, we develop the theory of short and long confocal annular Josephson tunnel junctions in the presence of an in-plane magnetic field of arbitrary orientations. The behavior of a circular annular Josephson tunnel junction is then seen to be simply a special case of the above result. For junctions having a normalized perimet… Show more

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Cited by 10 publications
(26 citation statements)
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“…In the limit c → 0, the elliptic coordinates (ν, τ ) reduce to polar coordinates (r, θ), where θ is the angle relative to the Y -axis; the correspondence is given by τ → θ and c cosh ν → r (note that ν itself will becomes infinite as c → 0). For closely spaced inner and outer ellipses, ∆ν ≡ ν o − ν i << 1, the expression of the local annulus width is 25 :…”
Section: B Current-voltage Characteristicsmentioning
confidence: 99%
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“…In the limit c → 0, the elliptic coordinates (ν, τ ) reduce to polar coordinates (r, θ), where θ is the angle relative to the Y -axis; the correspondence is given by τ → θ and c cosh ν → r (note that ν itself will becomes infinite as c → 0). For closely spaced inner and outer ellipses, ∆ν ≡ ν o − ν i << 1, the expression of the local annulus width is 25 :…”
Section: B Current-voltage Characteristicsmentioning
confidence: 99%
“…In the small-width approximation, ∆w max << λ J , the system becomes one-dimensional and the ν-independent Josephson phase, φ(τ,t), of a CAJTJ in the presence of a spatially homogeneous in-plane magnetic field H of arbitrary orientation,θ, relative to the Y -axis, obeys a modified and perturbed 1+1 sine-Gordon equation with a space dependent effective Josephson penetration, λ J /Qν(τ ), length inversely proportional to the local junction width 25 :…”
Section: B Current-voltage Characteristicsmentioning
confidence: 99%
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“…Neural networks are usually seen as black boxes in which only node(s) of the output layer bear meaning to the classification problem in question. However, there has been a recent increase in research on how to detect which features are most important to the network and how these features relate to the final classification results (e.g., Google's Deep Dream (Simonyan et al, 2014) experiments). Unfortunately, there is still no consensus on which techniques should be used to perform such a task.…”
Section: Analysis Of the Neural Network Structurementioning
confidence: 99%