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Search Publications by: Michael Schneider (Fed)

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Displaying 1 - 25 of 62

Comparison of measured and simulated spin-wave mode spectra of magnetic nanostructures

January 6, 2021
Author(s)
Hans Nembach, Robert McMichael, Michael Schneider, Justin Shaw, Thomas J. Silva
Motivated by the importance of magnetization dynamics in nanomagnets for the development and optimization of magnetic devices and sensors, we measured and modeled spin wave spectra in patterned elliptical nanomagnets. Ferromagnetic resonance spectra for

Synaptic weighting in single flux quantum neuromorphic computing

January 22, 2020
Author(s)
Michael L. Schneider, Christine A. Donnelly, Ian W. Haygood, Alex Wynn, Stephen E. Russek, Manuel C. Castellanos Beltran, Paul D. Dresselhaus, Peter F. Hopkins, Matthew R. Pufall, William H. Rippard
Josephson junctions act as a natural spiking neuron-like device for neuromorphic computing. By leveraging the advances recently demonstrated in digital single flux quantum (SFQ) circuits and using recently demonstrated magnetic Josephson junction (MJJ)

Energy efficient single flux quantum based neuromorphic computing

November 8, 2018
Author(s)
Michael Schneider, Christine A. Donnelly, Stephen E. Russek, Burm Baek, Matthew Pufall, Pete Hopkins, William Rippard
Many neuromorphic hardware technologies are being explored for their potential to increase the efficiency of computing certain problems, and thus facilitate machine learning with greater energy efficiency and or with more complexity. Among the technologies

High-speed low-power neuromorphic systems based on magnetic Josephson junctions

October 25, 2018
Author(s)
Michael Schneider, Christine A. Donnelly, Stephen E. Russek
Josephson junctions and single flux quantum (SFQ) circuits form a natural neuromorphic technology with SFQ pulses and superconducting transmission lines simulating action potentials and axons, respectively. Josephson junctions consist of superconducting

Superconducting optoelectronic networks III: synaptic plasticity

July 5, 2018
Author(s)
Jeffrey M. Shainline, Adam N. McCaughan, Sonia M. Buckley, Christine A. Donnelly, Manuel C. Castellanos Beltran, Michael L. Schneider, Richard P. Mirin, Sae Woo Nam
As a means of dynamically reconfiguring the synaptic weight of a superconducting optoelectronic loop neuron, a superconducting flux storage loop is inductively coupled to the synaptic current bias of the neuron. A standard flux memory cell is used to

Anomalous Supercurrent Modulation in Josephson Junctions With Ni-Based Barriers

May 15, 2018
Author(s)
Burm Baek, Michael L. Schneider, Matthew R. Pufall, William H. Rippard
We investigate the supercurrent transport characteristics of Ni-barrier Josephson junctions with various barrier multilayer structures. Our device fabrication and magneto-electrical measurement methods provide high enough statistics and rigor necessary for

Superconducting optoelectronic networks II: receiver circuits

April 6, 2018
Author(s)
Jeffrey M. Shainline, Sonia M. Buckley, Adam N. McCaughan, Manuel C. Castellanos Beltran, Christine A. Donnelly, Michael L. Schneider, Richard P. Mirin, Sae Woo Nam
Circuits using superconducting single-photon detectors and Josephson junctions to perform signal reception, synaptic weighting, and integration are investigated. The circuits convert photon-detection events into flux quanta, the number of which is

Magnetic damping in sputtered Co2MnGe Heusler compounds with A2, B2 and L21 order: Experiment and Theory

March 19, 2018
Author(s)
Justin M. Shaw, Eric R. Edwards, Martin A. Schoen, Michael L. Schneider, Matthew R. Pufall, Thomas J. Silva, Hans T. Nembach, Erna Delczeg
We show that very low values of the magnetic damping parameter can be achieved in sputter deposited poly-crystalline films of Co2MnGe annealed at relatively low temperatures ranging from 240 oC to 400 oC. Damping values as low as 0.0014 are achieved with

Ultralow power artificial synapses using nanotextured magnetic Josephson junctions

January 28, 2018
Author(s)
Michael L. Schneider, Christine A. Donnelly, Stephen E. Russek, Burm Baek, Matthew R. Pufall, Peter F. Hopkins, Paul D. Dresselhaus, Samuel P. Benz, William H. Rippard
Neuromorphic computing is a promising avenue to dramatically improve the efficiency of certain computational tasks, such as perception and decision making. Neuromorphic systems are currently being developed for critical applications ranging from self