Summary
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 Mn3+xGe) 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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| Web resources: | https://cordis.europa.eu/project/id/896307 |
| Start date: | 01-09-2020 |
| End date: | 31-08-2022 |
| Total budget - Public funding: | 203 852,16 Euro - 203 852,00 Euro |
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Original description
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 Mn3+xGe) 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.Status
CLOSEDCall topic
MSCA-IF-2019Update Date
28-04-2024
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