activity
20172022
most citedHardware realization of the multiply and accumulate operation on radio-frequency signals with magnetic tunnel junctions

34 citations · 82 across the 6 of their papers we have counts for

collaborators

10 papers

cond-mat.supr-con2022

Superconducting bimodal ionic photo-memristor

Ralph El Hage, Vincent Humbert, Victor Rouco +7

Memristive circuit elements constitute a cornerstone for novel electronic applications, such as neuromorphic computing, called to revolutionize information technologies. By definit…

cond-mat.dis-nn202134 cited

Hardware realization of the multiply and accumulate operation on radio-frequency signals with magnetic tunnel junctions

Nathan Leroux, Alice Mizrahi, Danijela Markovic +7

Artificial neural networks are a valuable tool for radio-frequency (RF) signal classification in many applications, but digitization of analog signals and the use of general purpos…

cond-mat.mes-hall2020

Wireless communication between two magnetic tunnel junctions acting as oscillator and diode

Danijela Marković, Nathan Leroux, Alice Mizrahi +7

Magnetic tunnel junctions are nanoscale spintronic devices with microwave generation and detection capabilities. Here we use the rectification effect called "spin-diode" in a magne…

cond-mat.supr-con2020

Long-Range Propagation and Interference of -wave Superconducting Pairs in Graphene

D. Perconte, K. Seurre, V. Humbert +13

Recent experiments have shown that proximity with high-temperature superconductors induces unconventional superconducting correlations in graphene. Here we demonstrate that those c…

cond-mat.mtrl-sci2019

Factors limiting ferroelectric field-effect doping in complex-oxide heterostructures

L. Bégon-Lours, V. Rouco, Qiao Qiao +12

Ferroelectric field-effect doping has emerged as a powerful approach to manipulate the ground state of correlated oxides, opening the door to a new class of field-effect devices. H…

physics.app-ph2019

Microwave neural processing and broadcasting with spintronic nano-oscillators

P. Talatchian, M. Romera, S. Tsunegi +14

Can we build small neuromorphic chips capable of training deep networks with billions of parameters? This challenge requires hardware neurons and synapses with nanometric dimension…