Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1309.7505

Deep Sub-Micron Stud-Via Technology for Superconductor VLSI Circuits

Sergey K. Tolpygo·V. Bolkhovsky·T. Weir·L. Johnson·W. D. Oliver·M. A. Gouker
View original source ↗Read source document ↗Export metadata ↓

Abstract

A fabrication process has been developed for fully planarized Nb-based superconducting inter-layer connections (vias) with minimum size down to 250 nm for superconductor very large scale integrated (VLSI) circuits with 8 and 10 superconducting layers on 200-mm wafers. Instead of single Nb wiring layers, it utilizes Nb/Al/Nb trilayers for each wiring layer to form Nb pillars (studs) providing vertical connections between the wires etched in the bottom layer of the trilayer and the next wiring layer that is also deposited as a Nb/Al/Nb trilayer. This technology makes possible a dramatic increase in the density of superconducting digital circuits by reducing the area of interconnects with respect to presently utilized etched contact holes between superconducting layers and by enabling the use of stacked vias. Results on the fabrication and size dependence of electric properties of Nb studs with dimensions near the resolution limit of 248-nm photolithography are presented in the normal and superconducting states. Superconducting critical current density in the fabricated stud-vias is about 0.3 A/μm2 and approaches the depairing current density of Nb films.

Explore related subjects

cond-mat.supr-con

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Sergey K. Tolpygo, V. Bolkhovsky, T. Weir, L. Johnson, W. D. Oliver, M. A. Gouker. 2013-09-28. Deep Sub-Micron Stud-Via Technology for Superconductor VLSI Circuits. https://doi.org/10.1088/0953-2048%2F27%2F2%2F025016

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Collective excitation-mediated transport in nanoscale Josephson junctions that exhibit quantum confinement

Quantum confinement can strongly modify transport through Josephson junctions. Here, we study local tunneling transport through nanoscale Josephson junction stacks in the Coulomb blockade regime, where the metallic layers exhibit strong vertical quantum confinement. We find that quasiparticle transport is strongly enhanced by a collective excitation mode intrinsic to the junction and localized in the isolated metallic overlayer. We quantify both the collective-mode energy and the Coulomb gap and show that both exhibit strong layer-dependent modulation, consistent with the modulation of the underlying quantum well states. We further investigate how the collective excitation responds to various perturbations, including mechanical motion and an applied magnetic field. Our results suggest that this collective mode is sensitive to quasiparticles near the Fermi level and may therefore provide an indirect probe of the superconducting state.

cond-mat.supr-con↗
arXiv2026

Universal Dzyaloshinski-Moriya interaction dictates pairing in unconventional superconductor families

The collinear-antiferromagnetic spin-fluctuation paradigm has long guided unconventional superconductivity research, yet fails to reconcile the noncollinear spin phenomena observed across cuprates, iron-based superconductors, and nickelates. Using extensive first-principles calculations and unbiased large-scale DMRG simulations, we show that Dzyaloshinski-Moriya interaction (DMI)-arising from local inversion-symmetry breaking-is a common ingredient across these families. This DMI unifies hallmark observations in parent compounds-incommensurate orders, spin-wave gaps, and noncollinear textures. Under hole doping, strong DMI drives spin vortices to merge with pi-shifted hole stripes, forming hybrid vortex-hole stripe phases. These phases stabilize charge order while supporting, not suppressing, superconductivity. By contrast, under electron doping, these vortices pin holes and suppress long-range superconductivity. Our results establish DMI as a unifying link between noncollinear magnetism and superconductivity, identifying hole-strip-vortex coupling as a microscopic pairing engine. Given that DMI is common across major superconductor families, these findings challenge the prevailing pairing mechanism and offer an experimentally testable roadmap for materials optimization.

cond-mat.supr-con↗
arXiv2026

Digital-analog concept for superconducting perceptron-like neural networks

A promising route to superconducting artificial neural networks is a hybrid digital-Analog architecture that combines digital single-flux-quantum (SFQ) communication with compact Analog nonlinear processing. The study focused on the dynamic conversion of a discrete signal passing through a digital-to-Analog-to-digital (DAD) converter, in which the role of the Analog cell was performed by a $Σ$-neuron with a nonlinear transfer function -- the basic cell of perceptron-like neural networks. Furthermore, the DAD converter, the elementary functional block of the hybrid architecture, combines a digital-to-analog converter (DAC) and an Analog-to-digital converter (ADC), and re-encodes the Analog $Σ$-neuron waveforms as an SFQ pulse sequence. Circuit-level simulations demonstrate how input values encoded by SFQ pulse trains are converted into analog signal levels, transformed by the $Σ$-neuron, and mapped back to pulse-based outputs. As a key experimental step, we fabricated and characterised a redesigned $Σ$-neuron and measured a sigmoid-like transfer characteristic suitable for activation-function implementation. The extracted response was incorporated into system-level simulations to assess the influence of realistic device parameters on the conversion process. We delineate the operating-range matching requirements for the DAC, neuron, and ADC blocks, supporting the feasibility of the proposed interface as a building block for perceptron-like superconducting neural networks with digital inputs and outputs. Finally, we developed two perceptron networks, one using a mathematical sigmoid activation and the other the measured $Σ$-neuron transfer characteristic, which reached classification accuracies of $97.0\%$ and $91.9\%$, respectively, on the MNIST handwritten digit dataset.

cond-mat.supr-con↗