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<!DOCTYPE html>
<html lang="en">
<head>
<title>SpinENGINE</title>
<link rel="icon" href="spinengine.eu/images/SpinENGINE-symbol.png">
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<meta http-equiv="X-UA-Compatible" content="IE=Edge">
<meta name="description" content="An EU H2020 FET-Open Project">
<meta name="keywords" content="SpinENGINE">
<meta name="author" content="Magnus Sjalander">
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<h1>SpinENGINE</h1>
<h3>Harnessing the Emergent Properties of Nanomagnet Ensembles for Massively Parallel Data Analysis</h3>
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<h2>About the SpinENGINE project</h2>
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<p>The SpinENGINE project will lay the foundations for
a new, massively parallel, platform based on emergent
behaviour in nanomagnet ensembles. The project will an
efficient, highly scalable, and easily reproducible
platform meeting the data challenges in our
increasingly data-rich society. We will build upon our
recent discoveries and use complex, nonlinear, and
highly tunable interactions in such ensembles to
realize a hardware platform for “Reservoir Computing”,
a biologically-inspired computational approach. Our
critical hypothesis is that the synergies between the
inherent properties of nanomagnet ensembles and those
required for reservoir computing will enable the
efficient creation of a highly adaptive computational
platform for the analysis of complex, dynamic data
sets. This has the potential to greatly outperform
current approaches using conventional CMOS
hardware.</p>
<p>SpinENGINE will bring together a multidisciplinary
team of researchers with expertise in computer
science, condensed matter physics, material science,
computational modelling, and high-resolution
microscopy. This will enable us to simultaneously
explore the fundamental behaviours of nanomagnet
ensembles and understand how these can be harnessed
for useful computation. By the end of the project, we
aim to fabricate a proof-of-concept device capable of
solving pattern recognition and classification
problems, and, in collaboration with our industrial
partner, IBM, produce a roadmap to the further scaling
and commercialization of our computational
platform. Success in the SpinENGINE project will have
vast implications for data analysis at all scales,
ranging from low power computation in the simplest
sensor node to accelerated data processing in the most
complex supercomputer.</p>
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<img src="images/rings.png" width="50" class="img-responsive" alt="">
Micromagnetically-calculated domain wall arrangements at ring junctions with 100-nm-wide wires and (a) 10 % and (b) 100 % wire overlap. Colour represents magnetisation direction.
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<h2>Publications</h2>
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<ol>
<li>
<a href="https://journals.aps.org/prb/abstract/10.1103/PhysRevB.106.064408">
<img class="wrap-figure" src="images/papers/sw-ellipse.jpg" width="340" class="img-responsive" alt="">
<b>flatspin: A Large-Scale Artificial Spin Ice Simulator</b> <br>
Johannes H. Jensen, Anders Strømberg, Odd Rune
Lykkebø, Arthur Penty, Jonathan Leliaert, Magnus Själander, Erik Folven, and Gunnar
Tufte <br>
<em>Phys. Rev. B 106, 064408</em>, August 2022<br>
https://journals.aps.org/prb/abstract/10.1103/PhysRevB.106.064408<br>
<br>
<b>Abstract:</b>
We present flatspin, a novel simulator for
systems of interacting mesoscopic spins on a
lattice, also known as artificial spin ice
(ASI). A generalization of the Stoner-Wohlfarth
model is introduced, and combined with a
well-defined switching protocol to capture
realistic ASI dynamics using a point-dipole
approximation. Temperature is modelled as an
effective thermal field, based on the
Arrhenius-Néel equation. Through GPU
acceleration, flatspin can simulate the
dynamics of millions of magnets within
practical time frames, enabling exploration of
large-scale emergent phenomena at unprecedented
speeds. We demonstrate flatspin's versatility
through the reproduction of a diverse set of
established experimental results from
literature. In particular, the field-driven
magnetization reversal of “pinwheel” ASI is
reproduced, for the first time, in a dipole
model. Finally, we use flatspin to explore
aspects of “square” ASI by introducing dilution
defects and measuring the effect on the vertex
population.
</a>
</li>
<br>
<li>
<a href="https://doi.org/10.1088/1361-6528/ac87b5">
<img class="wrap-figure" src="images/papers/nanotechnology-2022.png" width="240" class="img-responsive" alt="">
<b>Quantifying the Computational Capability of a
Nanomagnetic Reservoir Computing Platform with
Emergent Magnetisation Dynamics</b> <br>
Ian Vidamour, Matthew O.A. Ellis, David Griffin,
Guru Venkat, Charles Swindells, Richard W. S. Dawidek,
Thomas J Broomhall, Nina-Juliane Steinke, Joshaniel
Cooper, Francesco Maccherozzi, Sarnjeet Dhesi, Susan
Stepney, Eleni Vasilaki, Dan A Allwood and Tom James
Hayward<br>
<em>Nanotechnology</em>, August 2022<br>
https://doi.org/10.1088/1361-6528/ac87b5<br>
<br>
<b>Abstract:</b>
Devices based on arrays of interconnected magnetic
nano-rings with emergent magnetization dynamics have
recently been proposed for use in reservoir computing
applications, but for them to be computationally
useful it must be possible to optimise their dynamical
responses. Here, we use a phenomenological model to
demonstrate that such reservoirs can be optimised for
classification tasks by tuning hyperparameters that
control the scaling and input-rate of data into the
system using rotating magnetic fields. We use
task-independent metrics to assess the rings'
computational capabilities at each set of these
hyperparameters and show how these metrics correlate
directly to performance in spoken and written digit
recognition tasks. We then show that these metrics,
and performance in tasks, can be further improved by
expanding the reservoir's output to include multiple,
concurrent measures of the ring arrays' magnetic
states.
</a>
</li>
<br>
<li>
<a href="https://www.cs.york.ac.uk/nature/temc/TEMC2021-UCNC21/programme.pdf">
<img class="wrap-figure" src="images/papers/UCNC-2021.png" width="550" class="img-responsive" alt="">
<b>A Representation of Artificial Spin Ice for Evolutionary Search</b> <br>
Arthur Penty and Gunnar Tufte<br>
<em>Proceedings of the International Conference on Unconventional Computation and Natural Computation</em>, October 2021<br>
https://www.cs.york.ac.uk/nature/temc/TEMC2021-UCNC21/programme.pdf<br>
<br>
<b>Abstract:</b>
Ensembles of interacting nanomagnets known as
Artificial Spin Ice (ASI) have become a promising
new substrate for computation. Properties such as
emergence and non-linear local interactions make
it of particular interest for unconventional and
material computation. Previously, we have proposed
a method to represent and grow new ASI geometries,
suited for use in an Evolutionary Algorithm
(EA). Here we use our representation and evolution
to further investigate towards computational
properties including memory and
classification. The richness of geometries found
with sought computational properties indicates
that ASI geometry is a fruitful tuning parameter
for computational ASI systems.
</a>
</li>
<br>
<li>
<a href="https://doi.org/10.1103/PhysRevB.104.134421">
<img class="wrap-figure" src="images/papers/PhysRevB-2021.png" width="300" class="img-responsive" alt="">
<b>Effects of array shape and disk ellipticity in dipolar-coupled magnetic metamaterials</b> <br>
Sam D. Slöetjes, Einar S. Digernes, Anders Strømberg, Fredrik K. Olsen, Ambjørn D. Bang, Alpha T. N’Diaye, Rajesh V. Chopdekar, Erik Folven, and Jostein K. Grepstad<br>
<em>Physics Review B</em> Vol.104, No. 134421, October 2021<br>
https://doi.org/10.1103/PhysRevB.104.134421</br>
<br>
<b>Abstract: </b>
Two-dimensional lattices of
dipolar-coupled thin film ferromagnetic nanodisks
give rise to emergent superferromagnetic (SFM)
order when the spacing between dots becomes
sufficiently small. In this paper, we define
micron-sized arrays of permalloy nanodisks
arranged on a hexagonal lattice. The arrays were
shaped as hexagons, squares, and rectangles to
investigate finite-size effects in the SFM domain
structure for such arrays. The resulting domain
patterns were examined using x-ray magnetic
circular dichroism photoemission electron
microscopy. At room temperature, we find these SFM
metamaterials to be below their blocking
temperature. Distinct differences were found in
the magnetic switching characteristics of
horizontally and vertically oriented rectangular
arrays. The results are corroborated by
micromagnetic simulations.
</a>
</li>
<br>
<li>
<a href="https://direct.mit.edu/isal/proceedings/isal/99/102944">
<img class="wrap-figure" src="images/papers/ALIFE-2021.png" width="200" class="img-responsive" alt="">
<b>A Representation of Artificial Spin Ice for Evolutionary Search</b> <br>
Arthur Penty and Gunnar Tufte<br>
<em>Proceedings of the Artificial Life Conference</em>, July 2021<br>
https://direct.mit.edu/isal/proceedings/isal/99/102944<br>
<br>
<b>Abstract:</b>
Arrangements of nanomagnets known as artificial
spin ices show great potential for use in
unconventional computation. The majority of
exploratory work done in this area considers just
a small handful of well studied geometries
(nanomagnetic arrangements), and uses them as if
they were a black box. Here we detail a novel
representation of artificial spin ice geometries,
which lends itself to the tuning and evolutionary
search of geometries. Using our representation we
present geometries tuned to exhibit a desired
computational or meta-material property. This is
the first example of such a search performed on
artificial spin ice.
</a>
</li>
<br>
<li>
<a href="https://doi.org/10.1063/5.0048672">
<img class="wrap-figure" src="images/papers/APL-222405-2021.jpg" width="250" class="img-responsive" alt="">
<b>Synchronization of Chiral Vortex Nano-Oscillators</b> <br>
Zhiyang Zeng, Zhaochu Luo, Laura J. Heyderman, Joo-Von Kim, and Aleš Hrabec <br>
<em>Applied Physics Letters</em> Vol. 118, No. 222405, June 2021<br>
https://doi.org/10.1063/5.0048672</br>
<br>
<b>Abstract: </b>
The development of spintronic
oscillators is driven by their potential
applications in radio frequency telecommunication
and neuromorphic computing. In this work, we
propose a spintronic oscillator based on the
chiral coupling in thin magnetic films with
patterned anisotropy. With an in-plane magnetized
disk imprinted on an out-of-plane magnetized slab,
the oscillator takes a polar vortex-like magnetic
structure in the disk stabilized by a strong
Dzyaloshinskii–Moriya interaction. By means of
micromagnetic simulations, we investigate its
dynamic properties under applied spin current, and
by placing an ensemble of oscillators in the near
vicinity, we demonstrate their synchronization
with different resonant frequencies. Finally, we
show their potential application in neuromorphic
computing using a network with six oscillators.
</a>
</li>
<br>
<li>
<a href="https://doi.org/10.1063/5.0045450">
<img class="wrap-figure" src="images/papers/APL-202404-2021.jpeg" width="230" class="img-responsive" alt="">
<b>Anisotropy and Domain Formation in a Dipolar Magnetic Metamaterial</b> <br>
Einar Digernes, Anders Strømberg, Carlos A. F. Vaz, Armin Kleibert, Jostein K. Grepstad and Erik Folven <br>
<em>Applied Physics. Letters</em> Vol. 118, No. 202404, May 2021 <br>
https://doi.org/10.1063/5.0045450<br>
<br>
<b>Abstract:</b>
Long-range magnetic ordering can
be stabilized in arrays of single-domain
nanomagnets through dipolar interactions. In these
metamaterials, the magnetic properties are
determined by geometric parameters such as the
nanomagnet shape and lattice symmetry. Here, we
demonstrate engineering of the anisotropy in a
dipolar magnetic metamaterial by tuning of the
lattice parameters. Furthermore, we show how a
modified Kittel's law explains the resulting
domain configurations of the dipolar ferromagnetic
arrays.
</a>
</li>
<br>
<li>
<a href="https://doi.org/10.1063/5.0048911">
<img class="wrap-figure" src="images/papers/APL-202402-2021.jpeg" width="600" class="img-responsive" alt="">
<b>Voltage-controlled superparamagnetic ensembles for low-power reservoir computing</b> <br>
A. Welbourne, A. L. R. Levy, M. O. A. Ellis, H. Chen, M. J. Thompson, E. Vasilaki, D. A. Allwood, T. J. Hayward<br>
<em>Applied Physics Letters</em> Vol. 118, No. 202402, May 2021<br>
https://doi.org/10.1063/5.0048911<br>
<br>
<b>Abstract:</b>
We propose thermally driven,
voltage-controlled superparamagnetic ensembles as
low-energy platforms for hardware-based reservoir
computing. In the proposed devices, thermal noise
is used to drive the ensembles' magnetization
dynamics, while control of their net magnetization
states is provided by strain-mediated voltage
inputs. Using an ensemble of CoFeB nanodots as an
example, we use analytical models and
micromagnetic simulations to demonstrate how such
a device can function as a reservoir and perform
two benchmark machine learning tasks (spoken digit
recognition and chaotic time series prediction)
with competitive performance. Our results indicate
robust performance on timescales from microseconds
to milliseconds, potentially allowing such a
reservoir to be tuned to perform a wide range of
real-time tasks, from decision making in
driverless cars (fast) to speech recognition
(slow). The low energy consumption expected for
such a device makes it an ideal candidate for use
in edge computing applications that require low
latency and power.
</a>
</li>
<br>
<li>
<a href="https://doi.org/10.1002/adfm.202008389">
<img class="wrap-figure" src="images/papers/AFM-2021.png" width="400" class="img-responsive" alt="">
<b>Dynamically Driven Emergence in a Nanomagnetic System</b> <br>
Richard W. Dawidek, Thomas J. Hayward, Ian T. Vidamour, Thomas J. Broomhall, Guru Venkat, Mohanad Al Mamoori, Aidan Mullen, Stephan J. Kyle, Paul W. Fry, Nina-Juliane Steinke, Joshaniel F. K. Cooper, Francesco Maccherozzi, Sarnjeet S. Dhesi, Lucia Aballe, Michael Foerster, Jordi Prat, Eleni Vasilaki, Matthew O. A. Ellis, and Dan A. Allwood<br>
<em>Advanced Functional Materials</em> Vol. 31, No. 15, February 2021<br>
https://doi.org/10.1002/adfm.202008389<br>
<br>
<b>Abstract: </b>
Emergent behaviors occur when
simple interactions between a system's constituent
elements produce properties that the individual
elements do not exhibit in isolation. This article
reports tunable emergent behaviors observed in
domain wall (DW) populations of arrays of
interconnected magnetic ring-shaped nanowires under
an applied rotating magnetic field. DWs interact
stochastically at ring junctions to create
mechanisms of DW population loss and gain. These
combine to give a dynamic, field-dependent
equilibrium DW population that is a robust and
emergent property of the array, despite highly
varied local magnetic configurations. The magnetic
ring arrays’ properties (e.g., non-linear behavior,
“fading memory” to changes in field, fabrication
repeatability, and scalability) suggest they are an
interesting candidate system for realizing reservoir
computing (RC), a form of neuromorphic computing, in
hardware. By way of example, simulations of ring
arrays performing RC approaches 100% success in
classifying spoken digits for single speakers.
</a>
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