Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userSimulated annealing with time-varying strain in a dipole-coupled array of magnetostrictive nanomagnets
64
0
0.0
(
0
)
Added by
Supriyo Bandyopadhyay
Publication date
2019
fields
Physics
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
In a two-dimensional arrangement of closely spaced elliptical nanomagnets with in-plane magnetic anisotropy, whose major axes are aligned along columns and minor axes along rows, dipole coupling will make the magnetic ordering ferromagnetic along the columns and anti-ferromagnetic along the rows. Noise and other perturbations can drive the system out of this ground state configuration and pin it in a metastable state where the magnetization orientations will not follow this pattern. Internal energy barriers, sufficiently larger than the thermal energy kT, will prevent the system from leaving the metastable state and decaying spontaneously to the ground state. These barriers can be temporarily eroded by globally straining the nanomagnets with time-varying strain if the nanomagnets are magnetostrictive, which will allow the system to return to ground state after strain is removed. This is a hardware emulation of simulated annealing in an interacting many body system. Here, we demonstrate this function experimentally.
rate research
Read More
The desire to perform information processing, computation, communication, signal generation and related tasks, while dissipating as little energy as possible, has inspired many ideas and paradigms. One of the most powerful among them is the notion of using magnetostrictive nanomagnets as the primitive units of the hardware platforms and manipulating their magnetizations with electrically generated static or time varying mechanical strain to elicit myriad functionalities. This approach has two advantages. First, information can be retained in the devices after powering off since the nanomagnets are non-volatile unlike charge-based devices such as transistors. Second, the energy expended to perform a given task is exceptionally low since it takes very little energy to alter magnetization states with strain. This field is now known as straintronics, in analogy with electronics, spintronics, valleytronics, etc. We review the recent advances and trends in straintronics, including digital information processing (logic), information storage (memory), domain wall devices operated with strain, control of skyrmions with strain, non-Boolean computing and machine learning with straintronics, signal generation (microwave sources) and communication (ultra-miniaturized acoustic and electromagnetic antennas) implemented with strained nanomagnets, hybrid straintronics-magnonics, and interaction between phonons and magnons in straintronic systems. We identify key challenges and opportunities, and lay out pathways to advance this field to the point where it might become a mainstream technology for energy-efficient systems.
The feasibility of reservoir computing based on dipole-coupled nanomagnets is demonstrated using micro-magnetic simulations. The reservoir consists of an 2x10 array of nanomagnets. The static-magnetization directions of the nanomagnets are used as reservoir states. To update these states, we change the magnetization of one nanomagnet according to a single-bit-sequential signal. We also change the uniaxial anisotropy of the other nanomagnets using a voltage-induced magnetic-anisotropy change to enhance information flow, storage, and linear/nonlinear calculations. Binary tasks with AND, OR, and XOR operations were performed to evaluate the performance of the magnetic-array reservoir. The reservoir-computing output matrix was found to be trainable to perform AND, OR, and XOR operations with an input delay of up to three bits.
Probabilistic (p-) bits implemented with low energy barrier nanomagnets (LBMs) have recently gained attention because they can be leveraged to perform some computational tasks very efficiently. Although more error-resilient than Boolean computing, p-bit based computing employing LBMs is, however, not completely immune to defects and device-to-device variations. In some tasks (e.g. binary stochastic neurons for machine learning and p-bits for population coding), extended defects, such as variation of the LBM thickness over a significant fraction of the surface, can impair functionality. In this paper, we have examined if unavoidable geometric device-to-device variations can have a significant effect on one of the most critical requirements for probabilistic computing, namely the ability to program probability with an external agent, such as a spin-polarized current injected into the LBM. We found that the programming ability is fortunately not lost due to reasonable device-to-device variations. The little variation in the probability versus current characteristic that reasonable device variability causes can be suppressed further by increasing the spin polarization of the current. This shows that probabilistic computing with LBMs is robust against small geometric variations, and hence will be scalable to a large number of p-bits.
Context: Combinatorial interaction testing is known to be an efficient testing strategy for computing and information systems. Locating arrays are mathematical objects that are useful for this testing strategy, as they can be used as a test suite that enables fault localization as well as fault detection. In this application, each row of an array is used as an individual test. Objective: This paper proposes an algorithm for constructing locating arrays with a small number of rows. Testing cost increases as the number of tests increases; thus the problem of finding locating arrays of small sizes is of practical importance. Method: The proposed algorithm uses simulation annealing, a meta-heuristic algorithm, to find locating array of a given size. The whole algorithm repeatedly executes the simulated annealing algorithm by dynamically varying the input array size. Results: Experimental results show 1) that the proposed algorithm is able to construct locating arrays for problem instances of large sizes and 2) that, for problem instances for which nontrivial locating arrays are known, the algorithm is often able to generate locating arrays that are smaller than or at least equal to the known arrays. Conclusion: Based on the results, it is concluded that the proposed algorithm can produce small locating arrays and scale to practical problems.
We have demonstrated optical excitation and detection of collective precessional dynamics in arrays of coupled Ni80Fe20 (permalloy) nanoelements with systematically varying areal density by an all-optical time-resolved Kerr microscope. We have applied this technique to precisely determine three different collective regimes in these arrays. At very high areal density, a single uniform collective mode is observed where the edge modes of the constituent elements are suppressed. At intermediate areal densities, three nonuniform collective modes appear and at very low areal density, we observe noncollective dynamics and only the centre and edge modes of the constituent elements appear.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
