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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.

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

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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