High-temperature superconducting quantum interference antenna

Salvatore Mesoraca (Laboratoire Albert Fert, CNRS, Thales – Palaiseau, France)

Date: February 25^th, 2026

The Quantum Interference Antenna (QIA) is a magnetic field sensor based on arrays of superconducting quantum interference devices (SQUIDs). Unlike conventional resonant RF antennas, which are limited in size by the wavelength of the detected signal, the QIA operates through a non-resonant interaction with the magnetic component of electromagnetic waves.  By exploiting quantum interference in Josephson junctions, it enables detection over an ultra-wide frequency range (from DC up to tens of GHz). When implemented with high-temperature superconductors, QIA also allows for compact and energy-efficient cryocooling. This combination of wide bandwidth and reduced size opens new opportunities in areas where conventional antennas are fundamentally limited.

The performance of a QIA depends on the coherent operation of thousands of Josephson junctions, requiring high homogeneity across the array and demanding precise and challenging nanoscale fabrication. This seminar will introduce the QIA principle, outline its advantages over classical antennas, explore its applications in different fields and present our recent progress on QIA devices based on high-temperature cuprate YBCO and fabricated using masked ion irradiation.

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Superconducting spintronics for racetrack memory

Stuart Parkin (Max Planck Institute of Microstructure Physics, Halle (Saale), Germany)

Date: September 17^th, 2025

Superconducting spintronics is a highly interesting area of research which allows, for example, for the formation of unconventional superconducting states via proximity induced superconductivity in certain magnetic materials. We have shown that Josephson junctions fabricated from conventional s-wave superconductors that have barriers formed from an intrinsic noncollinear antiferromagnet or from magnetic multilayers designed to have magnetic layers with orthogonal
magnetizations show very high supercurrent critical densities that are indicative of the formation of triplet supercurrents. Another highly interesting finding is the observation of a Josephson Diode effect (JDE) in both lateral and vertical Josephson junctions where the barrier is formed from a material that breaks both time reversal symmetry and inversion symmetry. The simplest case is perhaps that of the pure metal platinum that is magnetized at one surface by proximity to an insulating ferromagnet in a direction perpendicular to the supercurrent that is created by niobium electrodes at the opposing surface. We find large asymmetries in the supercurrent critical density that increase with decreasing temperature below that of the superconducting ordering temperature of niobium. A more exotic case is where the barrier in lateral Josephson junctions is formed from a type II Dirac semi-metal, NiTe2. The superconducting critical current density shows large asymmetries for current flowing in opposite directions of up to 80% in the presence of small magnetic fields transverse to the supercurrent direction. The barriers can extend to almost a micron in extent and yet still allow for the passage of supercurrents. Similar results are found for barriers formed from PtTe2. Vertical junctions formed from WTe2 also show a diode-like behavior in the presence of a magnetic field but only when the field is along a direction perpendicular to a mirror plane in the orthorhombic crystal structure of this unusual van der Waals material. The JDE could form a novel device for reading magnetic nanoscopic objects at ultra low temperatures. Triplet supercurrents that carry spin angular momentum could potentially be used to manipulate magnetization. Together these two superconducting spintronic effects are highly interesting for potential applications in cryogenic logic and memory that could support quantum computing systems. One of the most interesting applications is for a novel cryogenic form of racetrack memory.

Superconducting artificial synapses integrated into a self-training neuromorphic architecture

Emilie Jué (National Institute of Standards and Technology, Colorado, USA)

Date: May 14^th, 2025

Superconducting electronics is a compelling technology for designing neuromorphic computing systems. The technology uses Josephson junctions (JJ), which have a natural spiking behavior and can transmit voltage spikes over long distances with near-zero loss. One important building block of a neural network that still needs to be developed in superconducting electronics is the synapse, whose role is to adjust the strength of the connection between two neurons by changing the synaptic weight. In this work, we propose a new superconducting synapse developed at NIST and demonstrate its integrations in neural networks. The artificial synapse is obtained with a SQUID-based circuit using a flux storage loop for the synaptic weight. Using SPICE simulations, we demonstrate that the synaptic
circuit can be tuned using digital single flux quantum pulses.

In the second part of this presentation, we demonstrate the implementation of the synapses in a neural network. We integrate the SQUID-based artificial synapses into a small-scale self-training neural network architecture using SPICE simulations. The network follows reinforcement learning rules that update local weights internally. This property allows the network to learn new functions by changing the target output for a given set of inputs without needing any external adjustments. Finally, using the same constraint as for the network mentioned above, we extend our simulations in Python to a larger problem to classify the handwritten digits from the MNIST dataset and show that the MNIST network can be trained in about 100 ms.

Neuromorphic Computing

Frank Mizrahi (Laboratoire Albert Fert, CNRS, Thales, Université Paris-Saclay)

Date: February 26^th, 2025

The ability to compute and learn at the edge is critical for many artificial intelligence applications, from medical sensors to autonomous vehicles. Yet, this is not possible with current hardware because of the high energy consumption of learning. Neuromorphic computing aims at developing novel energy efficient hardware by taking inspiration from the brain. In this seminar, we will see what ideas from the brain can be used to develop novel hardware. We will explain the main challenges of the field and review the emerging technologies studied.

Magnetization dynamics features in φ0 Josephson junctions

Andrei Mazanik (CFM-CSIC)

Date: November 6^th, 2024

The consortium is proud to present a series of seminars called Josephine Seminars, in which consortium members, as well as external guests working on topics related to the project’s objectives, will give scientific talks. The seminars will be online and open, i.e. scientists from all over the world can participate. Each seminar will last 45 minutes plus 15 minutes for questions. You can attend the semianr via Zoom. Between 3 and 4 seminars will be organized annually. This is the first one of the series.