Abstract

The modification of geometry and interactions in two-dimensional magnetic nanosystems has enabled a range of studies addressing the magnetic order1,2,3,4,5,6, collective low-energy dynamics7,8 and emergent magnetic properties5, 9,10 in, for example, artificial spin-ice structures. The common denominator of all these investigations is the use of Ising-like mesospins as building blocks, in the form of elongated magnetic islands. Here, we introduce a new approach: single interaction modifiers, using slave mesospins in the form of discs, within which the mesospin is free to rotate in the disc plane11. We show that by placing these on the vertices of square artificial spin-ice arrays and varying their diameter, it is possible to tailor the strength and the ratio of the interaction energies. We demonstrate the existence of degenerate ice-rule-obeying states in square artificial spin-ice structures, enabling the exploration of thermal dynamics in a spin-liquid manifold. Furthermore, we even observe the emergence of flux lattices on larger length scales, when the energy landscape of the vertices is reversed. The work highlights the potential of a design strategy for two-dimensional magnetic nano-architectures, through which mixed dimensionality of mesospins can be used to promote thermally emergent mesoscale magnetic states.

Main

Lithographic techniques can be used to fabricate magnetic nano-arrays, in which the interaction between the elements can be chosen by, for example, the distance between the islands. This approach has been used in a number of previous works, addressing both the order and dynamics of magnetic nanostructures1,2,3,4,5,6,7,8,12. In the specific case of square artificial spin ice (SASI), this approach has even enabled tailoring of the thermal dynamics and relaxation8,13,14,15, as well as experimental realizations9 of the degenerate square-ice model16. The distance and thereby the coupling strength for nearest and next-nearest neighbours are different in SASI (d1 ≠ d2; see Fig. 1), resulting in the loss of degeneracy. As a consequence, the ice-rule-obeying vertices, with two islands pointing in–two islands pointing out, are split into two groups (TI and TII) with different energies (EI < EII). One way to remedy this shortcoming is to shift parts of the lattice in the third dimension9, 17,18. An alternative way to modify the energy landscape is to introduce an interaction modifier, as illustrated in Fig. 1b. In these modified SASI (mSASI) arrays, all islands have the same distance, or gap G, to the interaction modifier. While a height offset might seem the obvious choice for manipulating the coupling strengths between the islands, the use of interaction modifiers at the vertices of artificial spin-ice structures is not only lithographically much easier to achieve, but also opens up completely new avenues for tailoring their energy landscapes. Instead of having a system consisting of only one type of island, we use two subsystems with widely different shape anisotropies and activation energies.

Fig. 1: Schematics of the SASI and mSASI lattices shown together with their vertex types.
Fig. 1

a,b, Illustration of a SASI (a) and an mSASI (b) lattice. Real-space base vectors defining the square lattice together with the base (red islands) are shown in a together with the discrete lattice points (black dots). The lattice parameter α is defined as α= a 1 = a 2 . The length of the islands is l = 450 nm and their width is w = 150 nm. c, The four different vertex types with their respective energies, E, and degeneracy, z. In TII and TIII vertices, the XY-like spins of the discs have a well-defined direction due to the residual stray field. In TI and TIV vertices, the XY spins exhibit weak four-fold degeneracy (in the collinear approximation11).

The elongated islands used in artificial spin-ice structures can be described as Ising-like mesospins, while the discs we use here to modify their interaction can be described to a first approximation as XY-like11. The difference in their activation energies will give rise to a master–slave relation, where the vertex state dictates the direction of the XY-mesospin. The mutual interaction of the Ising and the XY spins yields the emergent magnetic order. For TII and TIII vertices, the magnetization direction of the enclosed discs is enforced by the effective dipole moment of the vertex, but the magnetic state of the disc for TI and TIV vertices is four-fold degenerate (see supplementary Fig. 14). Fabricating the islands from a material with an ordering temperature at or below room temperature8, 12,13 allows us to access all of the relevant parts of the phase diagram: from the paramagnetic state of the material to the ordering of the vertices as described below.

In total, we have studied fifteen different SASI arrays with three different lattice parameters α = [660, 720, 800] nm patterned on δ-doped Pd(Fe) thin films. Each array has five different disc sizes, D = [0, 120, 130, 150, 180] nm for α = 660 nm and D = [0, 130, 150, 180, 200] nm for α = [720, 800] nm. All arrays have the same elongated island size of 450 × 150 nm2. Photoemission electron microscopy (PEEM) based on X-ray magnetic circular dichroism (XMCD) was utilized to determine the magnetic state of the elements. Representative results from the near-perturbation-free measurements are shown in Fig. 2a.

Fig. 2: Real-space magnetic imaging and vertex populations.
Fig. 2

a, Representative SEM and PEEM–XMCD images (α = 660 nm, D = 130 nm and G = 40). X-ray direction and colour contrast is indicated by the arrows. b, Degeneracy-normalized vertex populations PD as a function of disc diameter D. The horizontal coloured stripes refer to the populations for D = 0 and serve as a guide to the eye along with the dashed lines, highlighting the vertex population changes while varying D. As the diameter D increases, the population ratios between TI and TII are reversed, pointing towards a transition in vertex energies from EI < EII to EI > EII, where D = 150 nm is the closest realization to the EI = EII condition. EIV > EIII > (EI, EII) holds for all lattices explored. c, Spatial representation, in the form of vertex maps, of the data from b for D = 0, D = 150 and D = 180. The colour coding represents the four different types of vertex, described in Fig. 1c, using the colour scheme from Fig. 2b. All error bars represent one standard deviation.

The degeneracy-normalized vertex populations, derived from the PEEM–XMCD images, as a function of disc diameter for the α = 660 nm array are presented in Fig. 2b (see Supplementary Fig. 1 for full data set). The sample is thermally active and upon cooling will pass the blocking temperatures of the elongated islands and the discs. This implies that the frozen states observed at 120 K represent configurations that were thermally arrested at higher temperatures8. In the absence of a central disc, a large number of excitations are observed as the system approaches the antiferromagnetically ordered ground state of SASI3. The high degree of disorder is caused by the relatively small coupling strength between the Ising islands.

The presence of the discs dramatically changes the overall behaviour as seen in Fig. 2b, passing from a TI-dominated spin texture in the absence of discs (D = 0) to a TII-dominated configuration when discs with a diameter of 180 nm are present. This implies an inversion in the energy levels for EI and EII, at a diameter around 150 nm, corresponding to a gap of 30 nm (see Supplementary Fig. 13 for micromagnetic simulations). We also note that the populations of TIII and TIV appear to be only weakly affected by the presence of the discs, within the range of diameters studied here (see Fig. 2). Therefore, the corresponding vertex energies remain close to constant relative to EI and EII. For D = 150 nm (G = 30 nm), the numbers of TI and TII vertices are similar, which is expected in the compensated regime where all ice-rule-obeying vertex configurations are equal in energy and therefore degenerate.

The spatial distribution of the vertices is shown in Fig. 2c. An average domain size of 3.2 TI vertices is obtained from the analysis for D = 0, with the largest domain composed of 37 TI vertices. With the discs present, the domain size of TI vertices is found to decrease with increasing disc diameter, reaching 1.4 for D = 180 nm. At this diameter, the TII vertices are predominant, with an average domain size of 11.7 vertices and the largest domain observed consisting of 191 vertices. The magnetic correlation between the Ising mesospins decays rapidly, favouring the use of analysis tools that are not tied to pre-identified correlations. For example, direct entropy density estimates, as suggested previously19, can be used to obtain the upper bound for the real entropy in the arrays.

An even more comprehensive way to study the emergent magnetic order is the computation of the magnetic spin structure factor9, 10. Here we utilize the real-space lattice and related vectors defined and presented in Fig. 1b when calculating the magnetic spin structure factor from the real-space PEEM–XMCD images. The result for α = 660 nm, D = 0 nm, and the reciprocal lattice, related vectors and high-symmetry points are presented in Fig. 3a. The position of the Bragg peaks at the M points of the first Brillouin zone ( ± 1 2 , ± 1 2 reciprocal lattice units (r.l.u.) or [ 1 2 b1, 1 2 b2]) stems from domains of TI vertices, having a magnetic structure with a periodicity twice as large as that of the lattice. The width of the peaks arises from the abundance of defects in the form of TIII and TIV vertices, resulting in short correlation lengths. The thermalized system is free from kinetic constraints imposed by external fields, as, for example, observed in athermal systems9, yielding highly symmetric Bragg peaks. When the diameter of the discs is increased, the Bragg peaks diminish and diffuse scattering becomes more prominent (see Supplementary Fig. 4 for the full data set). At D = 150 nm (G = 30 nm), which corresponds to the nearly compensated array, the signal is diffuse yet structured as seen in Fig. 3b. This map resembles the characteristic intensity distribution for a square-ice model spin liquid, associated with an emergent Coulomb phase with slow decaying spin correlations20,21. In such cases, the spin structure factor exhibits characteristic intensity features at specific reciprocal lattice points, so-called ‘pinch points’22, appearing at [±1, ±1] r.l.u., where the intensity exhibits a singularity9. The intensities at the pinch points of Fig. 3b exhibit a weak divergence due to finite-size effects and the amount of excitations in the arrays. Expressing the intensity distribution around a pinch point in polar coordinates of {q, θ}, we can identify a clear dependence on θ, but not on q, in contrast with the expected intensity distribution for an ideal paramagnet, which is independent of both θ and q (ref. 23).

Fig. 3: Magnetic spin structure factor and autocorrelation.
Fig. 3

a,b, The magnetic spin structure factor for the α = 660 nm array, D = 0 nm (a) and the magnetic spin structure factor for D = 150 nm (b), both frozen-in spin states. The colour scale indicates the intensity for every point (q x , q y ), while b1 and b2 in a are the reciprocal lattice vectors constructed from the real-space lattice vectors in Fig. 1b. The first Brillouin zone, dotted line, is shown together with a portion of the reciprocal lattice, white dots. The Γ point of the first Brillouin zone is also marked. c, Time–temperature average of seven magnetic spin structure factors exploring the spin-liquid manifold. d, Autocorrelation of the spin states as a function of time and temperature. The autocorrelation reveals how far from the original spin state the array has evolved, an indication of how much of the spin-liquid manifold has been explored.

Direct observation of the magnetic microstates enables investigations of exotic magnetic phases, such as the spin-liquid state24. Furthermore, the rate of transformation in the spin liquid, for a given temperature and time interval, can be directly determined using the Edwards–Anderson order parameter25. To probe the thermal dynamics in the spin-liquid manifold, we use a heating protocol as described in the Methods. On heating, spins will start to reverse, changing the overall magnetic structure. The time- and temperature-averaged spin structure factor for the nearly compensated array, computed from seven different time–temperature steps, is shown in Fig. 3c. Figure 3d illustrates the changes in the Edwards–Anderson order parameter25, which we use to determine how far from the original spin configuration the array has evolved. It has the form of an autocorrelation between the measurement at t = t0 and every subsequent time–temperature step (see Methods for further details) as shown in Fig. 3d. Between t = t0 and t = tfinal, close to 20% of all spins have reversed, while the array still remains in the spin-liquid-like state. The evolution of the system presented in Fig. 3d depicts changes in the spin-liquid manifold, differing significantly from other dynamics studies targeting thermal relaxation processes7, 13,15. Here the magnetic structure does not relax; instead the vertex populations and domain sizes remain constant. The activities are similar for the TI, TII and TIII vertices, while TIV vertices are comparably more active (see Supplementary Fig. 11). This approach yields information on the dynamics of thermally equilibrated mesospins, but can also be used to follow glassy behaviour26 as a function of time or temperature.

At a disc diameter of D = 180 nm, the TII population increases to almost 60%, while the TI population decreases below 30% with vertex energies EII < EI < EIII < EIV. The resulting spin structure factor looks completely different as illustrated in Fig. 4a (see also ref. 27 for a discussion concerning an athermal system). When considering the effective dipole moment associated with the TII vertices, it becomes clear that their abundance can give rise to an emergent flux lattice on the next length scale, dictating the magnetic order of the spin system. In more detail, we experimentally identify four different types of flux lattice (see Supplementary Fig. 10): an emergent TI-like tiling of TII vertices (see Fig. 4c), vortex/anti-vortex pairs (see Fig. 4d), and ferromagnetic states, forming both a non-collinear herringbone structure (Fig. 4e) or collinear ferromagnetic domains (Fig. 4f). The flux lattice with the lowest energy, as determined by micromagnetic calculations, is the emergent TI-like tiling, whereas the vortex structure has only slightly higher energy. Both the ferromagnetic states invoke the presence of a net moment, implying a higher energy cost and being more unfavourable, as compared with the flux closure states. The energy differences ΔE between the ground state and the other emergent states, expressed as ΔE/kB, are 9 K, 86 K and 174 K for the vortex–antivortex state, the herringbone state and the polarized state, respectively (calculated using micromagnetic simulations; note that the absolute energy of each state depends on the domain size). The energy difference between the TI-tiling and the collinear flux state is therefore almost 25 times larger than the difference between the TI-tiling and the vortex–antivortex state. To get an estimate of the magnetic ordering of the array, we have calculated the spin structure factor maps for the configurations depicted in Fig. 4c–f and summed all four results into one map in Fig. 4b (see Methods for details). As seen in Fig. 4b, there is a strong resemblance between the experimental and the simulated results. To further elaborate on this, we make a cut along q x  = 0 for both the experimental and the calculated result shown in Fig. 4a,b. The weighted sum obtained from the four textures described in Fig. 4c–f overlaps to a great extent with the experimental data. With long-range interactions present28, the state with the lowest energy in the emergent flux lattice is, as already mentioned, the two-fold degenerate TI tiling. This state is from a symmetry perspective, identical to the ground state in regular SASI, albeit at a different length scale. At finite temperatures we observe the traces of competing states, with small energy differences, as well as frozen-in higher energy states. In the scenario of an array with even more dominant populations of TII vertices, which implies even larger energy gaps between TII vertices and other vertex types, the TII abundance would make the features we observe even more pronounced. This could provide a pathway towards examining systems where order is dominated by the emergent flux lattices.

Fig. 4: Emergent flux lattice ordering.
Fig. 4

a, Magnetic spin structure factor of the α = 660, D = 180 array. b, Summation of the magnetic structure factor for spin states (c-f), with π/2 rotation for spin states (e) and (f) included. c-f, TI-tiling, vortex state, herringbone state, and collinear state of the flux from TII vertices, drawn with blue arrows. g, Cut along q x  = 0 for both the experimental and simulated maps. The intensity of the peaks in the simulated map is scaled to match the experimental data. The scaling and consequently the abundance of each state is not uniquely determined here (see Methods for further details).

We have presented a generic solution to continuously alter the effective coupling between mesoscopically sized islands of a ferromagnetic material in a fully planar geometry. This approach can be utilized to engineer the energy landscape of two-dimensional nanomagnetic systems in a completely new way, employing nanomagnetic objects of distinctively different mesospin dimensionality. In our example using nanosized magnetic discs, we tailor the energy landscape of arrays, recovering the degeneracy in SASI and also promoting emergent magnetic order of the Ising mesospins. This approach opens thereby new routes for investigations of ordered and frustrated artificial systems. Here we have focused only on the final state of one of the subsystems in the sample (elongated Ising-like islands), treating the discs as interaction modifiers. One can also envisage structures where the situation is reversed and the collective magnetic structure of the discs dominates the ordering, leaving the Ising mesospins in the role of the modifier. This synergy and cooperative behaviour therefore provides a route for designing new types of magnetic metamaterials with rich magnetic phase diagrams and thermodynamics. The calculated spin structure factors, obtained from the real-space microscopy results, demonstrate the plausibility of using magnetic scattering29,30, providing new insights into emergent mesoscale magnetic structures driven by collective dynamics31.

Methods

Sample manufacturing

The arrays were patterned from δ-doped Pd(Fe)32 thin films grown on MgO substrates with a 1.5-nm-thick V seeding layer using an ultrahigh-vacuum sputter system. δ-doped Pd(Fe) is a magnetic trilayer system, in our case consisting of Pd(40 nm)/Fe(2.0 monolayers)/Pd(2 nm). The magnetic nanostructures were produced by post-patterning the Pd(Fe) δ-doped thin films using electron-beam lithography at the Center for Functional Nanomaterials (CFN), Brookhaven National Laboratory in Upton New York. The physical dimension of each array is 200 × 200 μm2 and these were all patterned on the same substrate ensuring near-identical conditions for all arrays during the measurements.


PEEM–XMCD

The photoemission electron microscopy (PEEM) measurements employing the X-ray magnetic circular dichroism (XMCD) technique were carried out at the 11.0.1 PEEM3 beamline at the Advanced Light Source, CA, USA. The imaging of the frozen states was performed at a temperature of 120 K using the L3 edge of Fe (708.6 eV). For each array, multiple XMCD images were acquired and merged together revealing the state of several thousands of islands. Due to the size of the discs, it is difficult to observe their magnetic orientation; however, some of them can be observed in the PEEM–XMCD images (see Supplementary Fig. 9). At 120 K, all mesospins are frozen with an average fluctuation rate lower than the timescale of the whole experiment; as such, no mesospin fluctuations were observed at 120 K. This also has the implication that changing the acquisition protocol in this frozen regime does not affect the observed state.


Magnetic spin structure factor

The magnetic spin structure factor is defined analogous with neutron scattering experiments where spin correlations perpendicular to the scattering vector are measured. We start by defining a perpendicular spin component S of spin S:

S =S- ( q ^ S ) q ^
(1)

where q ^ is the unit scattering vector:

q ^ = q q

For every q = (q x , q y ) the intensity I is given by

I ( q ) = 1 N ( i , j = 1 ) N S i S j exp i q r i - r j
(2)

where N stands for the number of spins. We can further write it as

I ( q ) = 1 N i = 1 N S i exp i qr i j = 1 N S j exp - i qr j
(3)

Expanding yields:

I ( q ) = 1 N i = 1 N S i cos ( q r i ) + i i = 1 N S i sin ( q r i ) j = 1 N S j cos ( q r j ) - i j = 1 N S j sin ( q r j )
(4)

Recognizing that i and j sum up over the same spins and defining A= i = 1 N S i cos ( q r i ) and B= i = 1 N S i sin ( q r i ) , we can simplify the equation such that

I ( q ) = 1 N A + i B A - i B = 1 N A 2 + B 2
(5)

I is now a real quantity that we calculate for the interval (q x , q y ) = [−3, −3] − [3, 3] r.l.u. in 601 × 601 steps.


Heating protocol

The sample was cooled from its paramagnetic state to 170 K where the spin flip time is on the order of hours. At this temperature, the t0 spin state was recorded. The temperature was subsequently raised in steps of 10 K up to 200 K with two measurement points recorded at each temperature. The nominal acquisition time was kept the same for all measurements with the shortest acquisition time being 470 s and the longest 483 s. The starting time t for all measurements relative to t0 was 1047 s, 1531 s, 2371 s, 2864 s, 3753 s, and 4242 s.


Autocorrelation

The autocorrelation is calculated in a way so that any change in the spin system at t > t0 is tracked cumulatively towards tfinal. For every given time–temperature (t,T) step, the autocorrelation is given by Q ( t , T ) = 1 N j = 1 N S j , t 0 , T i S j , t , T , where t0 is the initial time, Ti is the initial temperature and N is the number of islands with an assignable magnetic vector. Only islands visible in both time–temperature steps are taken into account (see Supplementary Fig. 8).


Composite spin structure map

To obtain insight into the overall spin structure of the α = 660, D = 180 array (see Fig. 4a), we calculated the individual spin structure factor of the states illustrated in Fig. 4c–f in the following way. We used an array containing 144 islands, with 64 vertices, all TII. Concerning the structure illustrated in Fig. 4d, this implies that the spin structure factor is calculated from five vortices and four antivortices. Each of the spin structure factors is scaled in intensity (1/20, 1/2, 1/20, 1/9) to match the experimental data illustrated in Fig. 4a. However, these scaling factors do not directly relate to the extent or the abundance of the different states, particularly because the scaling does not provide a unique solution. The weighted sum of the four spin structure factor maps is shown in Fig. 4b. The data points are binned in series of three, using a moving average, in the bar diagram in Fig. 4g. Additional real space analysis was performed to estimate the abundance of the spin configurations contributing to the different flux states (see Supplementary Fig. 16).


Micromagnetic simulations

The micromagnetic simulations were performed using the GPU-accelerated MUMAX3 software33. The calculations are all 0 K calculations with a saturation magnetization of Ms = 663,260 A m–1 (ref. 8) and an exchange stiffness of 6.5 × 10–12. The thickness of the magnetic layers was assumed to be 1 nm. The calculation of the energies for the states illustrated in Fig. 4 were performed using 32 islands and 16 discs, using periodic boundaries. Initially, the magnetic order was pre-defined in all elements. The system was thereafter relaxed, a process where MUMAX3 minimizes the energy, allowing for divergence of the magnetization within the elements.


Data availability

The data that support the plots within this paper and other findings of this study are available from the corresponding author upon request.

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Acknowledgements

The authors would like to thank S. T. Bramwell and P. C. W. Holdsworth for valuable discussions. The authors acknowledge support from the Knut and Alice Wallenberg Foundation project 'Harnessing light and spins through plasmons at the nanoscale' (2015.0060), the Swedish Research Council and the Swedish Foundation for International Cooperation in Research and Higher Education. The patterning was performed at the Center for Functional Nanomaterials, Brookhaven National Laboratory, supported by the US Department of Energy, Office of Basic Energy Sciences, under contract no. DE-SC0012704. This research used resources of the Advanced Light Source, which is a DOE Office of Science User Facility under contract no. DE-AC02-05CH11231. This work is part of a project which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 737093. U.B.A. acknowledges funding from the Icelandic Research Fund grant nos 141518 and 152483.

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Affiliations

  1. Department of Physics and Astronomy, Uppsala University, Uppsala, Sweden

    • Erik Östman
    • , Henry Stopfel
    • , Ioan-Augustin Chioar
    • , Vassilios Kapaklis
    •  & Björgvin Hjörvarsson
  2. Science Institute, University of Iceland, Reykjavik, Iceland

    • Unnar B. Arnalds
  3. Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, NY, USA

    • Aaron Stein

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Contributions

H.S. and A.S. fabricated the sample. E.Ö., H.S., U.B.A. and V.K. performed the PEEM–XMCD experiments. E.Ö., I.-A.C., H.S., V.K. and B.H. analysed the data and contributed to theory development. E.Ö., I.-A.C. V.K. and B.H. wrote the manuscript. All authors discussed the results and commented on the manuscript.

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The authors declare no competing financial interests.

Corresponding author

Correspondence to Erik Östman.

Supplementary information

  1. Supplementary Information

    Supplementary figures 1–16, Supplementary references 1–3

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https://doi.org/10.1038/s41567-017-0027-2

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