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SPINHALL SIGNED

Computing with mutually synchronized topological insulator based spin Hall nano-oscillators

Total Cost €

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EC-Contrib. €

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Partnership

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 SPINHALL project word cloud

Explore the words cloud of the SPINHALL project. It provides you a very rough idea of what is the project "SPINHALL" about.

cmos    300    magnetization    ultrafast    shnos    insulators    shno    vortex    nano    found    consumption    ultra    angle    synchronization    first    demonstrated    day    generators    standard    orders    weak    alloys    easily    radar    candidates    spin    power    ideal    speech    mutual    ferromagnets    structure    detectors    drive    microwave    mn3xge    computing    mn3    latest    anisotropy    spectral    performance    coupling    applicability    frequency    consumes    phones    operate    fast    nor    local    techniques    ground    reservoir    of    primary    compatibility    recognition    spinhall    scopic    group    topological    magnetic    industry    linear    stnos    revolutionary    heusler    network    conductivity    neuromorphic    candidate    xga    explore    bisb    cell    oscillator    oscillatory    efficient    magnitude    bise    individual    breakthroughs    direct    tunable    vehicle    stno    characterization    mhz    oscillators    scalable    networks    breaking    modulated    hall    cells    torque    signal    attractive    memory    mtj    demonstration    materials    wireless   

Project "SPINHALL" data sheet

The following table provides information about the project.

Coordinator
GOETEBORGS UNIVERSITET 

Organization address
address: VASAPARKEN
city: GOETEBORG
postcode: 405 30
website: www.gu.se

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Sweden [SE]
 Total cost 203˙852 €
 EC max contribution 203˙852 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2019
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2020
 Duration (year-month-day) from 2020-09-01   to  2022-08-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    GOETEBORGS UNIVERSITET SE (GOETEBORG) coordinator 203˙852.00

Map

 Project objective

Spin Hall nano-oscillators (SHNOs) are revolutionary nano-scopic, ultra-tunable, and ultra-rapidly modulated microwave oscillators. They show highly attractive ground-breaking properties and have direct compatibility with industry standard CMOS technology due to its similar structure as present-day magnetic memory cells. While their first target applications are ultra-wide frequency tunable microwave signal generators/detectors for cell phones, wireless networks, vehicle radar, and ultrafast spectral analysis applications, the rapidly improved understanding of their non-linear properties and demonstration of mutual synchronization of large numbers of SHNOs make them promising candidate for large-scale oscillator networks. It has been found very recently that spin torque nano-oscillators (STNOs) and SHNOs are ideal candidates for efficient oscillatory computing and a group of researchers demonstrated speech recognition using reservoir computing with a network of four MTJ-STNOs. However, this approach is neither fast (the vortex STNOs operate in the 100-300 MHz range and STNO-STNO coupling is weak) nor easily scalable to large networks since each STNO requires individual control of both its drive current and local magnetic field, which consumes high power. SPINHALL will use the recent breakthroughs in spin Hall devices, materials, and characterization techniques to improve the performance, and applicability of SHNOs and their networks. The primary goal is to use the latest breakthroughs in topological insulators (such as BiSb and BiSe) with their high spin Hall angle and high spin Hall conductivity and low-magnetization high-anisotropy ferromagnets (such as Heusler alloys Mn3 xGa and Mn3xGe) to improve the SHNO operating frequency by an order of magnitude, the power consumption by several orders of magnitude, and explore improved mutual synchronization and neuromorphic computing using networks of these SHNOs.

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