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Ponopono Health Group

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Weston Cook
Weston Cook

AUTO SPIN



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AUTO SPIN



a Responsivities of rectification devices vs frequency. Blue and orange rectangular regions refer to bolometers and magnetic tunnel junctions (MTJs), respectively. Red point represents our result. b, c Schematic of spin bolometer (b) without and (c) with applied microwaves. Red and black arrows represent the magnetizations of the ferromagnetic free and pinned layers, respectively.


a Perpendicular magnetic field dependence of the ferromagnetic resonance frequency measured by the spin-torque diode technique. The dashed lines indicate linear fitting. b Bias-voltage dependence of perpendicular magnetic anisotropy. The open and filled circles represent the voltage sweep directions. The dashed red line is the fitting curve of a second-order polynomial.


Moreover, the HCMA value can be enhanced by the improvement of thermal design as discussed by Okuno30. By contrast, enhancement of spin-transfer torque requires a decrease in the magnetization or thickness of the ferromagnetic layer; this induces deterioration in MTJs. Therefore, utilization of HCMA is promising for further enhancement of responsivity.


HCMA is also useful for enhancement of dynamic range. Although the dynamic range is limited by the noise equivalent voltage, it can be improved by increasing the ferromagnetic thickness. However, spin-transfer torque and VCMA decrease significantly when this is done. HCMA decreases only slightly because the increase in the temperature of the ferromagnetic layer is mainly affected by the MgO layer through which the heat flows, rather than the FeB layer. Therefore, HCMA is the appropriate spin torque for improving dynamic range (see Supplementary Note 5).


With the advent of pure-spin-current sources, spin-based electronic (spintronic) devices no longer require electrical charge transfer, opening new possibilities for both conducting and insulating spintronic systems. Pure spin currents have been used to suppress noise caused by thermal fluctuations in magnetic nanodevices, amplify propagating magnetization waves, and to reduce the dynamic damping in magnetic films. However, generation of coherent auto-oscillations by pure spin currents has not been achieved so far. Here we demonstrate the generation of single-mode coherent auto-oscillations in a device that combines local injection of a pure spin current with enhanced spin-wave radiation losses. Counterintuitively, radiation losses enable excitation of auto-oscillation, suppressing the nonlinear processes that prevent auto-oscillation by redistributing the energy between different modes. Our devices exhibit auto-oscillations at moderate current densities, at a microwave frequency tunable over a wide range. These findings suggest a new route for the implementation of nanoscale microwave sources for next-generation integrated electronics.


We use micromagnetic simulations to map out and compare the linear and auto-oscillating modes in constriction-based spin Hall nano-oscillators as a function of the applied magnetic field with a varying magnitude and out-of-plane angle. We demonstrate that, for all possible applied field configurations, the auto-oscillations emerge from the localized linear modes of the constriction. For field directions tending towards the plane, these modes are of the so-called edge type, i.e., localized at the opposite edges of the constriction. By contrast, when the magnetization direction approaches the film normal, the modes transform to the so-called bulk type, i.e., localized inside the constriction with substantially increased precession volume, consistent with the redistribution of the magnetic charges from the edges to the top and bottom surfaces of the constriction. In general, the threshold current of the corresponding auto-oscillations increases with the applied field strength and decreases with its out-of-plane angle, consistent with the behavior of the internal field and in good agreement with a macrospin model. A quantitative agreement is then achieved by taking into account the strongly nonuniform character of the system via a mean-field approximation. Both the Oersted (Oe) field and the spin-transfer torque from the drive current increase the localization and decrease the frequency of the observed mode. Furthermore, the antisymmetric Oe field breaks the lateral symmetry, favoring the localized mode at one of the two constriction edges, particularly for large out-of-plane field angles where the threshold current is significantly increased and the edge demagnetization is suppressed.


Auto-oscillation threshold current vs applied field strength and out-of-plane angle as estimated (a) using micromagnetic simulations, (b) a macrospin model given by Eqs. (4) and (5), and (c) a mean-field model given by Eqs. (7) and (6). (d) The efficiency of pure-spin-current injection vs applied field geometry.


Spatial profiles of the auto-oscillations calculated for a field applied at θ0=70 and (a) without any Oe field or STT, (b) with STT, (c) with an Oe field, and (d) with both an Oe field and STT. (e) Contribution of the demagnetizing field and the Oe field to the depth of the spin-wave wells vs the applied field strength. (f) Contribution of the STT and Oe field to the frequency of the edge mode vs the applied field strength.


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Amazon EC2 Auto Scaling is a fully managed service designed to launch or terminate Amazon EC2 instances automatically to help ensure you have the correct number of Amazon EC2 instances available to handle the load for your application. Amazon EC2 Auto Scaling helps you maintain application availability through fleet management for EC2 instances, which detects and replaces unhealthy instances, and by scaling your Amazon EC2 capacity up or down automatically according to conditions you define. You can use Amazon EC2 Auto Scaling to automatically increase the number of Amazon EC2 instances during demand spikes to maintain performance and decrease capacity during lulls to reduce costs.


Amazon EC2 Auto Scaling helps to maintain your Amazon EC2 instance availability. Whether you are running one Amazon EC2 instance or thousands, you can use Amazon EC2 Auto Scaling to detect impaired Amazon EC2 instances, and replace the instances without intervention. This ensures that your application has the compute capacity that you expect. You can use Amazon EC2 Auto Scaling to automatically scale your Amazon EC2 fleet by following the demand curve for your applications, reducing the need to manually provision Amazon EC2 capacity in advance. For example, you can set a condition to add new Amazon EC2 instances in increments to the ASG when the average utilization of your Amazon EC2 fleet is high; and similarly, you can set a condition to remove instances in increments when CPU utilization is low. You can also use Amazon CloudWatch to send alarms to trigger scaling activities and Elastic Load Balancing (ELB) to distribute traffic to your instances within the ASG. If you have predictable load changes, you can use Predictive Scaling policy to proactively increase capacity ahead of upcoming demand. Amazon EC2 Auto Scaling enables you to run your Amazon EC2 fleet at optimal utilization.


The dynamic scaling capabilities of Amazon EC2 Auto Scaling refers to the functionality that automatically increases or decreases capacity based on load or other metrics. For example, if your CPU spikes above 80% (and you have an alarm setup) Amazon EC2 Auto Scaling can add a new instance dynamically.


Target tracking is a new type of scaling policy that you can use to set up dynamic scaling for your application in just a few simple steps. With target tracking, you select a load metric for your application, such as CPU utilization or request count, set the target value, and Amazon EC2 Auto Scaling adjusts the number of EC2 instances in your ASG as needed to maintain that target. It acts like a home thermostat, automatically adjusting the system to keep the environment at your desired temperature. For example, you can configure target tracking to keep CPU utilization for your fleet of web servers at 50%. From there, Amazon EC2 Auto Scaling launches or terminates EC2 instances as required to keep the average CPU utilization at 50%.


An Amazon EC2 Auto Scaling group (ASG) contains a collection of EC2 instances that share similar characteristics and are treated as a logical grouping for the purposes of fleet management and dynamic scaling. For example, if a single application operates across multiple instances, you might want to increase the number of instances in that group to improve the performance of the application, or decrease the number of instances to reduce costs when demand is low. Amazon EC2 Auto Scaling will automaticallly adjust the number of instances in the group to maintain a fixed number of instances even if a instance becomes unhealthy, or based on criteria that you specify. You can find more information about ASG in the Amazon EC2 Auto Scaling User Guide. 041b061a72


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