# Element-Specific Study of Magnetic Anisotropy and Hardening in SmCo5-xCux
Thin Films

Georgia Gkouzia  Institute of Materials Science, Technical University of
Darmstadt, Germany  Damian Günzing  Faculty of Physics and Center for
Nanointegration (CENIDE), University of Duisburg-Essen, Germany  Ruiwen Xie
Institute of Materials Science, Technical University of Darmstadt, Germany
Teresa Weßels  Ernst Ruska-Centre for Microscopy and Spectroscopy with
Electrons and Peter Grünberg Institute, Forschungszentrum Jülich, Germany
András Kovács  Ernst Ruska-Centre for Microscopy and Spectroscopy with
Electrons and Peter Grünberg Institute, Forschungszentrum Jülich, Germany
Alpha T. N’ Diaye  Lawrence Berkeley National Laboratory, Berkeley, USA
Márton Major  Institute of Materials Science, Technical University of
Darmstadt, Germany  J. P. Palakkal  Institute of Materials Physics, Georg-
August-University of Göttingen, Germany  Rafal E. Dunin-Borkowski  Ernst
Ruska-Centre for Microscopy and Spectroscopy with Electrons and Peter Grünberg
Institute, Forschungszentrum Jülich, Germany  Heiko Wende  Faculty of Physics
and Center for Nanointegration (CENIDE), University of Duisburg-Essen, Germany
Hongbin Zhang  Institute of Materials Science, Technical University of
Darmstadt, Germany  Katharina Ollefs  Faculty of Physics and Center for
Nanointegration (CENIDE), University of Duisburg-Essen, Germany  Lambert Alff
Institute of Materials Science, Technical University of Darmstadt, Germany
<EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

This work investigates the effect of copper substitution on the magnetic
properties of SmCo5 thin films synthesized by molecular beam epitaxy. A series
of thin films with varying concentrations of Cu were grown under otherwise
identical conditions to disentangle structural and compositional effects on
the magnetic behavior. The combined experimental and theoretical studies show
that Cu substitution at the Co3g sites not only stabilizes the formation of
the SmCo5 structure but enhances magnetic anisotropy and coercivity. Density
functional theory calculations indicate that Sm(Co4Cu3g)5 possesses a higher
single-ion anisotropy as compared to pure SmCo5. In addition, X-ray magnetic
circular dichroism reveals that Cu substitution causes an increasing
decoupling of the Sm 4f and Co 3d moments. Scanning transmission electron
microscopy confirms predominantly SmCo5 phase formation and reveals nanoscale
inhomogeneities in the Cu and Co distribution. Our study based on thin film
model systems and advanced characterization as well as modeling reveals novel
aspects of the complex interplay of intrinsic and extrinsic contributions to
magnetic hysteresis in rare earth-based magnets, i.e. the combination of
increased intrinsic anisotropy due to Cu substitution and the extrinsic effect
of inhomogeneous elemental distribution of Cu and Co.

###### keywords:

Permanent magnet, thin film, SmCo5, magnetic hysteresis.

Lawrence Berkeley National Laboratory, Berkeley, USA Faculty of Physics and
Center for Nanointegration (CENIDE), University of Duisburg-Essen, Germany
Institute of Materials Science, Technical University of Darmstadt, Germany

##  1 Introduction

SmCo5-based permanent magnets were developed in the 1960s and known to possess
extremely strong uniaxial magnetic anisotropy of about $K_{1}=17.2$ MJ/m3 1,
2, 3. The large magnetocrystalline anisotropy energy (MAE) arises due to the
spin-orbit coupling of localized and partially filled 4f electrons of Sm and
the spin-orbit coupling of the itinerant 3d electrons of cobalt in a strong
crystal electric field 4, 5. Besides, SmCo5 exhibits a relatively large energy
product ($BH$)${}_{\text{max}}$ up to 200 kJ/m3 and a Curie temperature of
1020 K. The SmCo5 phase has a hexagonal crystal structure with a space group
of $P6/mmm$.

In practical applications, SmCo5-based permanent magnets are widely used in
motors, generators, actuators, etc 6. These magnets are made from complex
alloys, such as Sm-Co-Cu-Fe-Zr derived from the Sm2Co17-type, and require
sophisticated heat treatments during fabrication, which lead to the emergence
of a unique microstructure 7, 8. The increased demand for high
($BH$)${}_{\text{max}}$ magnets in particular for high-temperature
applications in renewable energy technologies has spurred recent interest in
the various phenomena leading to high coercivity 6, 9. It is well-known that
phase decomposition of the starting alloy results in a cellular microstructure
of Sm2Co17 (2:17) with a SmCo5 (1:5) intergranular phase, and a Zr-rich
platelet 10, 11. The SmCo5 phase is Cu-rich and the Sm2Co17 phase Fe-rich.
Over the years, various studies have investigated the role of the individual
elements Cu, Fe, and Zr in intrinsic and extrinsic magnetic properties12.
However, a complete understanding that could help to overcome the so-called
Brown’s paradox and help in the development of novel green magnets is still
lacking 13, 14, 15, 16.

Sm-Co thin films offer precise control over nanostructure, making them an
ideal candidate for providing model structures to understand the specific role
of individual defects on the magnetic properties. Nevertheless, due to the
thermodynamic instability of the SmCo5 phase, also in thin films complex
decomposition phenomena do occur. We have recently discovered, a novel phase
decomposition regime in molecular beam epitaxy (MBE) grown thin films,
resulting in the coexistence of SmCo5 and Sm2Co17 blocks at the nanoscale with
a width of only a few nanometers17. These films have low coercivity due to
their high crystallinity, phase purity, and fully coherent interfaces between
SmCo5 and Sm2Co17. In contrast, sputtered thin films grown at much higher
particle energies have a more complex precipitation nanostructure made up of
various Sm-rich phases such as SmCo3 and Sm2Co7, which can lead to ultra-high
coercivity primarily due to pinning at low-symmetry grain boundaries18.

Typically, different buffers and underlayers made from Ru, Cr, or Cu have been
used to ease the growth of Sm-Co thin films. Textured Cr and Ru buffer layers
promote the $c$-axis orientation and improve the magnetic properties. Above
all, Cu has been widely studied because it not only facilitates the out-of-
plane orientation but has been found to have the unique advantage of
stabilizing the CaCu5 structure 19, 20, 21. Nevertheless, the magnetic
properties of the Sm-Co layer will be affected by diffusion processes from
these buffer layers.

In this work, we have established a direct growth process of Cu substituted
SmCo1-xCux samples on sapphire without using any additional underlayer, which
eliminates the potential impact of diffusing elements. We use computational
and experimental methods to characterize the element-specific magnetic
properties and illuminate the role of Cu incorporation in SmCo5 and its effect
on coercivity.

##  2 Methodology

###  2.1 Experimental part

The base pressure of the used MBE chamber was 10-10 mbar. The SmCo5-xCux thin
films were deposited by e-beam co-evaporation from elemental Sm, Co, and Cu
sources. The films were deposited onto $c$-axis oriented Al2O3 substrates,
known to promote the $c$-axis orientation of the SmCo5 phase. First, a
temperature scan was carried out showing that the most favorable temperature
for growing a crystalline SmCo5 layer was in this setup 550∘C. The substrates
were heated from the backside using a diode laser, a technique that avoids
contamination from in vacuum heaters. Before evaporation, the substrates were
annealed at 540∘C for 1h in the MBE chamber, to obtain a clean crystalline
substrate surface. For all samples, the deposition rate of the samarium was
kept constant at 0.1 Å/sec. The individual deposition rates of cobalt and
copper were changed but the sum of the deposition rate (Co plus Cu) was kept
constant at 0.1 Å/sec. A series of SmCo5-xCux films was produced with
$x=0.5,1,1.5,2$. The evaporation rates during the growth of the thin films
were controlled by using quartz crystal microbalances. The quality of the
films was monitored using in-situ reflection high energy electron diffraction
(RHEED).

X-ray diffraction (XRD) with Cu Kα radiation on a Rigaku SmartLab system was
carried out for the crystallographic and structural characterization of the
films. The thicknesses of the films were determined to be 30 nm$\pm$2 nm. The
magnetic properties of the films were measured by a superconducting quantum
interference device (SQUID) in two directions, out-of-plane and in-plane of
the substrate surface, using the MPMS XL magnetometer by Quantum Design. The
measurements have been performed at 300 K with external fields up to 6 Tesla.
The diamagnetic contribution from the Al2O3 substrate has been subtracted by
correcting the slope between 4 and 6 Tesla. The coupling of Sm and Co moments
has been studied using X-ray magnetic circular dichroism (XMCD) by recording
element-specific hysteresis loops. For structural and spectroscopic
characterizations, scanning transmission electron microscopy (STEM) and energy
dispersive X-ray spectroscopy (EDX) 22 was used in combination on cross-
sectional specimens that were prepared along the $c$-axis using a focused Ga
ion beam (FIB) sputtering. The STEM images and EDX maps were processed using
Velox software (ThermoFisher Scientific).

###  2.2 Computational part

In order to evaluate the single-ion anisotropy of Sm3+, the crystal field
parameters (CFPs) were calculated in the framework of density functional
theory (DFT) using the WIEN2k program 23. The generalized gradient
approximation (GGA) was employed for the exchange-correlation functional. The
experimental lattice parameters of SmCo5 were adopted in the calculations.
Regarding the Cu-doped cases, including Sm(Co4Cu3g)5 and Sm(Co4Cu2c)5, the
lattice parameters were fixed to those of SmCo5 considering the relatively
small volume change. Therefore, the solely chemical effect of Cu can be
explicitly probed. The $RMT\times K_{\text{max}}$ was set to 7 and a $k$-mesh
of $9\times 9\times 9$ was sampled in the Brillouin zone. For the calculation
of CFPs, we followed the method proposed by Novák et al. 24, in which the
local Hamiltonian in the basis of Wannier functions is expanded by a series of
spherical tensor operators. In specific, the self-consistent field (SCF)
calculation was first performed without spin polarization and with 4f
electrons in the core. Subsequently, a non-SCF calculation was carried out
treating 4f as valence states so that the 4f states were allowed to hybridize
with the transition metal 3d states. In addition, we shifted the energy of 3d
states 0.4 Ry lower to assure appropriate hybridization strength. The Bloch
states from the 4f energy window were then transformed to Wannier functions
using the wien2wannier interface 25 followed by standard Wannierization
process by Wannier90 26.

The obtained CFPs were then used to construct the atomic Hamiltonian of Sm by
including also the Coulomb interactions ($\hat{H}_{U}$), the spin-orbit
coupling and the Sm-transition metal exchange coupling ($\hat{H}_{ex}$)

$\hat{H}_{at}=\hat{H}_{U}+\lambda\sum_{i}\hat{s}_{i}\hat{l}_{i}+\hat{H}_{CF}+\hat{H}_{ex}.$
(1)

The eigenvalue of the Hamiltonian was solved using the Lanczos algorithm as
implemented in Quanty code 27. By varying the magnetization direction
corresponding to the exchange coupling term, the eigenvalue was then obtained
as a function of azimuthal angle, which give rise to the single-ion
anisotropy.

##  3 Results and Discussion

###  3.1 Crystal Structure

Figure 1a shows the XRD patterns of the SmCo5-xCux films grown onto
001-oriented Al2O3 substrates. The main 006 reflection of the substrate
appears at 41.67∘. The 001-oriented Al2O3 promotes the growth of $c$-axis
textured Sm(Co,Cu)5 films, indicated by the presence of 00l-type reflections.
In the film with the lowest Cu content ($\\#1$), the main reflection 002 of
the SmCo5 phase is observed at 44.218∘. Upon increasing the Cu content, the
002 reflection shifts to 45.106∘ and with an even further increase of Cu
content ($\\#4$), the reflection shifts again to lower angles (inset Figure
1a). At the same time, the residual reflection of the Sm2Co17 phase decreases
and eventually disappears upon increasing Cu content. The observed non-linear
peak shift can be consistently explained as follows: First, the reduction of
the 2:17 phase shifts the peak towards the 1:5 peak, as observed previously
17. Then, the increasing Cu content shifts the peak back, due to the slightly
enlarged lattice constant of SmCu5, in agreement with Vegard’s law. Note that
the phase purity of the 1:5 phase is significantly higher than that of typical
sputtered thin films, however, also reflections from a small amount of
residual Sm-rich phases can still be detected around 29∘ and 30∘.
Nevertheless, the overall microstructure remains in a consistent way
predominantly 1:5 throughout the series.

Figure 1: (a) $\theta$-2$\theta$ X-ray diffraction patterns of ($\\#1$)
SmCo4.5Cu0.5, ($\\#2$) SmCo4Cu, ($\\#3$) SmCo3.5Cu1.5 and ($\\#4$) SmCo3Cu2
thin films grown onto single crystalline Al2O3 substrate at 550∘C. At 41.67∘
the 006 reflection of the Al2O3 substrate is marked with the plus sign. (b)
$\phi$ scan of the SmCo4Cu film (blue) grown on single crystalline Al2O3
substrate (grey) at 550∘C.(c) Pole figure of the SmCo5 reflection of the
SmCo4Cu film.

Azimuth scans have been used to confirm crystal lattice symmetry and epitaxial
relations. Here, we show a $\phi$-scan of the diffraction peak of the SmCo4Cu
sample with respect to the substrate. Figure 1b shows the 104 reflection (grey
2$\theta$=35.03∘ and $\chi$=38.02∘) of the substrate which is rhombohedral and
the SmCo4Cu sample (blue) which is hexagonal. The $\phi$-scan shows three
peaks which indicate the thee-fold symmetry of the Al2O3 substrate. Six peaks
are obtained for the SmCo4Cu film which shows the six-fold symmetry and proves
its hexagonal phase. The observed peaks correspond to the 201 reflection of
the SmCo5 phase. The pole figure of the 201 reflections, Figure 1c, shows the
high-intensity Bragg peaks in blue. The distribution of the reflections
indicates a crystalline, highly textured film.

###  3.2 Magnetization measurements

Cu is usually used as dopant in SmCo5 thin films grown onto Cr or Ru buffer
layers 28, 29. As mentioned above, in this work, no additional underlayers
have been used, and the SmCo5-xCux films were deposited directly on top of
Al2O3 substrates. The hysteresis loops of the out-of-plane direction are shown
in Figure 2. The easy axis of magnetization is out of plane for all samples
while the hard axis is in-plane as shown in Figure 4. Starting from the film
with the lowest Cu concentration, a remanent magnetization of 0.6 T can be
observed and the coercivity reaches 1.08 T. Upon increasing the Cu ratio, the
coercivity drastically increases up to 1.64 T whereas the remanent and
saturation magnetization, as expected due to Cu dilution of Co moments,
decreases (see Fig. 3). A further increasing of Cu results in a rapid decrease
of coercivity to 1.23 T.

Figure 2: Magnetization curves of the SmCo5-xCux thin films measured in the
out-of-plane (OOP) direction as a function of applied field at 300 K. Figure
3: Demagnetization curves of the SmCo5-xCux thin films as a function of
applied field at 300 K.

There are two sites where Cu can substitute Co, namely Co2c and Co3g.30 As the
thin film microstructure is similar in all cases predominantly 1:5, we assume
that extrinsic contributions to the coercivity are comparable - but with the
exception of the distribution of the substitutional element copper. The Cu
distribution will be addressed in section 3.4. Therefore, we suggest that the
increased coercivity is at least partly correlated with an increased intrinsic
anisotropy resulting from Cu substitution at the Co3g sites. A similar
suggestions has been made for Y(Co,Cu)5 31, a system which also behaves in
thin films similar to the here investigated Sm(Co,Cu)5 17, 32. To corroborate
this hypothesis, we discuss the described DFT-based modelling in section 3.5.
The in-plane magnetization measurements shown in Figure 4 further support an
increased anisotropy for SmCo4Cu and SmCo3.5Cu1.5. Another factor leading to
increased intrinsic anisotropy, is the improved crystallization of the 1:5
phase due to Cu substitution 33. Interestingly, in the out-of-plane loops
shown in Figure 3, a small kink appears at zero field. This kink might be
associated with the presence of a residual soft magnetic nanocrystalline or
amorphous phase that is not strongly coupled to the hard magnetic phase34.

Figure 4: Magnetization curves of the SmCo5-xCux thin films measured in the
in-plane (IP) direction as a function of applied field at 300 K.

###  3.3 Element-specific measurements

X-ray magnetic circular dichroism (XMCD) spectra and element-specific
hysteresis curves have been recorded at the bending magnet beamline 6.3.1 of
the Advanced Light Source (ALS) at Lawrence Berkeley National Laboratory. The
measurements were carried out using an applied magnetic field of up to 1.9 T
with a fixed polarization, at ambient temperatures. The samples have been
measured in out-of-plane direction, parallel to the magnetic easy $c$-axis. As
a detection method X-ray excited optical luminescence (XEOL) was used, which
probes the full thickness of the film, due to the luminescence of oxygen in
the Al2O3 substrate35. In this way element-specific hysteresis loops have been
recorded at fixed energies at the XMCD maxima and minima for Co L2,3-edges
(779.2 and 794.5 eV) and Sm M4,5-edges (1082.5 and 1109.3 eV).

Figure 5: Element specific hysteresis loops for the Sm M4,5 (grey) and Co
L2,3-edges (blue) recorded at room temperature applying positive and negative
1.9 T external fields in out-of-plane direction for the following samples (a)
SmCo4.5Cu0.5 (b) SmCo4Cu (c) SmCo3.5Cu1.5 and (d) SmCo3Cu2.

The strength of element-specific hysteresis measurements is the quantitative
separation of the individual contributions from the Co and Sm magnetic moment
36. The normalized element-specific hysteresis curves are shown in Figure 5.
The Co and Sm curves do not deviate significantly for the SmCo4.5Cu0.5 film
shown in Figure 5a. This confirms the stronger exchange coupling between the
rare earth Sm and the transition metal Co. Upon the increase of Cu
substitution, a gap between Co and Sm appears in the second quadrant’s
demagnetizing curves. The measured data indicates that the substitution of Cu
in the Co sub-lattice causes a continuous decoupling of the Sm and Co moments
and softens the Co moment. The dilution with Cu is also the reason for the
reduced total magnetization of the films. In contrast, samarium moments are
only softened at higher Cu concentrations, also supported by the reduced
critical temperature of the Cu-rich compounds.

###  3.4 Structure and chemical composition of SmCo4Cu

The structure and elemental distribution of the SmCo4Cu sample was studied in
cross-sectional geometry. Figure 6(a) shows the structure of the sample in a
high resolution high-angle annular dark-field (HAADF) STEM image. Note the
presence of a characteristic "dumbbell" pattern in the Sm-Co-Cu that indicates
the formation of the 2:17 phase at the interface to the Al2O3 substrate. The
rest of the layer shows a uniform 1:5 phase formation. The thickness of the
2:17 phase is approximately 7 nm. The transition from 2:17 to 1:5 phase is
continuous i.e., no sharp boundaries were observed.

Figure 6: (a) High resolution HAADF STEM image showing the interface of the
SmCo4Cu thin film to the Al2O3 substrate. The dumbbell pattern of atomic
columns reveals the presence of the Sm2Co17 phase. (b) Chemical composition
mapping of Sm, Co and Cu extracted from STEM/EDX spectrum imaging. Note the
distribution of Co and Cu is not uniform.

Figure 6(b) shows the chemical composition distribution of individual elements
extracted from EDX/STEM spectrum imaging. The Co and Cu maps reveal
inhomogenous distribution of the elements suggesting Cu- and Co-rich regions.
These inhomogeneities might be responsible for the zero field kink in the
SQUID measurement which is absent in the XMCD measurement (see Figs. 3 and
Fig. 5).

###  3.5 Discussion

The magnetization curves of the SmCo5-xCux films measured at room temperature
were shown in Figure 2. It is clear that Cu substitution affects the
coercivity which reaches a maximum value of 1.64 T. The presence of Cu at low
concentrations enhances coercivity up to the SmCo3.5Cu1.5. Following the X-ray
diffraction patterns, the 1:5 phase is by far the predominant phase in all
samples. A small fraction of the 2:17 phase which seems to be induced from the
substrate (rather than to a nanoscale phase decomposition as described
previously 17, is fully suppressed for higher Cu substitution. XMCD element-
specific hysteresis loops proved that Cu substitution results in an increasing
decoupling of the Sm and Co moments. Last but not least, electron microscopy
measurements showed that the films are dominated by mainly the 1:5 phase.

We first discuss the effect of Cu substitution on the intrinsic magnetic
anisotropy of SmCo5, especially the single-ion anisotropy (SIA) of Sm3+. While
intuitively one would expect an immediate decrease of intrinsic magnetic
anisotropy, the computational methods described in Sec. 2.2 show a different
and more complex behavior. The calculated CFPs $B_{lm}$ for SmCo5,
Sm(Co4Cu3g)5 and Sm(Co4Cu2c)5 are listed in Table 1. SmCo5 belongs to the
point group D6h therefore only $B_{20}$, $B_{40}$, $B_{60}$ and $B_{6\pm 6}$
retain. The evaluated $B_{lm}$ values using the Wannier basis are comparable
with the measured values. In particular, the magnitude of $B_{20}$ is dominant
and determines largely the magnetic anisotropy of Sm3+, i.e., a negative
$B_{20}$ indicates uniaxial magnetic anisotropy. With Cu occupying the $3g$
sites, $B_{20}$ is more negative, indicating that the SIA of Sm3+ is indeed
enhanced. In contrast, the Cu doping on the $2c$ site tends to lower the SIA
of Sm3+. Note that the number of nonzero $B_{lm}$ parameters in Sm(Co4Cu3g)5
is larger due to the lower symmetry.

Table 1:  Crystal field parameters (in units of Kelvin) for Sm3+Co5, Sm3+(Co4Cu3g)5 and Sm3+(Co4Cu2c)5. For comparison, the experimental values are taken from Ref. 37 $B_{lm}$ |  SmCo5 | Expt. |  Sm(Co4Cu3g)5 |  Sm(Co4Cu2c)5
---|---|---|---|---
(K) |  |  |  |
$B_{20}$ | -1068 | -840 | -1380 | -867
$B_{2\pm 2}$ | - | - | 168 | -
$B_{40}$ | 5 | 200 | 77 | -91
$B_{4\pm 2}$ | - | - | 102 | -
$B_{4\pm 4}$ | - | - | -44 | -
$B_{60}$ | -473 | 0 | -472 | -430
$B_{6\pm 2}$ | - | - | -14 | -
$B_{6\pm 4}$ | - | - | -34 | -
$B_{6\pm 6}$ | 494 | 6 | 501 | 456

Another important aspect to be considered is the exchange coupling between the
Sm and its neighboring Co atoms, which can be weakened upon introducing non-
magnetic Cu atoms into the system. The exchange field in SmCo5 is set to be
250 T according to previous experimental 38 and theoretical 39 work, while for
Sm(Co4Cu3g)5 and Sm(Co4Cu2c)5, the exchange fields are rescaled based on the
calculated $J_{\text{SmCo}}$ values contributed from the first-nearest
neighbors of Sm with respect to the $J_{\text{SmCo}}$ of SmCo5. Such exchange
coupling parameters are evaluated using the OpenMX 40 code in the LDA + $U$
regime with $U=6.7$ eV and $J=0.7$ eV for the Sm 4f states. Accordingly, we
set the exchange fields of Sm(Co4Cu3g)5 and Sm(Co4Cu2c)5 to 201 and 230 T,
respectively. The Coulomb interaction parameters and the spin-orbit coupling
strength are taken from Ref. 41. By varying the exchange field direction which
is represented by the azimuthal angle $\theta$, Fig. 7 shows the eigenvalue
$E_{ani}$ of $\hat{H}_{at}$ as a function of $\theta$. It can be explicitly
observed that Sm3+ in Sm(Co4Cu3g)5 possesses the highest SIA while in
Sm(Co4Cu2c)5 the SIA is the lowest. By fitting the energy curve to

$E_{ani}(\theta)=K_{1}\mathrm{sin}^{2}\theta+K_{2}\mathrm{sin}^{4}\theta+K_{3}\mathrm{sin}^{6}\theta,$
(2)

we obtain $K_{1}$ of 21 meV, 24 meV and 17 meV for SmCo5, Sm(Co4Cu3g)5 and
Sm(Co4Cu2c)5, respectively. Note here that we omit the $K_{3}^{\prime}$ term
associated with $K_{3}^{\prime}\mathrm{sin}^{6}\theta\mathrm{cos}6\phi$ since
we find $K_{3}^{\prime}$ rather small. Besides, the Cu doping effect on the Co
sites can be approximated using YCo5 as a prototype. It has been reported that
the $3g$ sites doping of Cu increases the magnetic anisotropy of YCo5 31 while
the $2c$ sites doping is expected to reduce the magnetic anisotropy due to a
larger $K_{1}$ of Co2c than that of Co3g. In addition, according to our DFT
calculations, the $2c$-site doping is slightly energetically favorable by
about 7 meV/atom as compared to the $3g$-site doping. This strongly implies a
statistically random distribution on both sites in reality, as 7 meV amounts
to a temperature of 81 K. Therefore, considering the contrasting roles played
by Cu with different Wyckoff position occupations, a non-monotonous change of
magnetic anisotropy with Cu doping content can be expected, as also observed
in the Ce-Co based systems 42, 43. It is intriguing to characterize the
intrinsic properties of the Cu-substituted SmCo5 system to bridge the gap
between the microscopic equilibrium properties and the macroscopic coercivity
44.

Figure 7: Ground state energy of Sm 4f shell in SmCo5 (orange circle),
Sm(Co4Cu3g)5 (blue hexagons), Sm(Co4Cu2c)5 (green triangle) as a function of
magnetization direction represented by the azimuthal angle $\theta$.

From a macroscopic point of view, Sm-rich precipitates as well as the
formation of many different Sm-Co phases and structural disorder phenomena are
strongly connected to high coercivity values in sputtered Sm-Co films. 18.
Sayama _et al._ 45 suggested that the origin of high perpendicular anisotropy
in SmCo5 thin films on Cu buffer layers are related to the diffusion of Cu
atoms into the SmCo5 structure. Note that this diffusion effect is excluded in
the presented study. The layer of Sm2Co17 at the substrate interface will not
contribute to a systematic change of the hysteresis itself. As the defect
structure is similar throughout the consistent series of thin films, we
correlate the increased coercivity to the increased intrinsic
magnetocrystalline anisotropy upon Cu substitution and to the inhomogeneous
distribution of Cu and Co. Since other phases, grain and phase boundaries can
be excluded for our thin film model systems, we suggest the nanoscale
disproportionation of Cu and Co as additional novel source of coercivity
leading to enhanced pinning with increased Cu substitution.

##  4 Conclusions

In summary, our investigation of highly crystalline SmCo5-xCux thin films
grown by MBE on Al2O3 substrates in combination with advanced computational
and characterization methods, has revealed that copper substitution enhances
the intrinsic magnetic anisotropy, correlated with an increase in coercivity.
As a second source of increased coercivity, we have identified with EDX
measurements a disproportionation of Cu- and Co-rich areas within an otherwise
homogeneous 1:5 phase structure. The practical applicability of Cu to replace
Co is limited of course by the induced overall reduction of the total magnetic
moment, acting negatively on the ($BH$)${}_{\text{max}}$ product. XMCD
provided clear evidence of a decoupling of Sm and Co moments in the presence
of Cu. Electron microscopy confirmed the presence of small amount of
interfacial Sm2Co17 phase in the highly crystalline SmCo5 layer. Based on thin
film model systems, our study provides novel insight into the complex
materials science and hardening mechanisms in rare-earth-based permanent
magnetic materials by disentangling different sources of intrinsic and
extrinsic contributions to the hysteresis behavior.

We acknowledge the financial support from the Deutsche Forschungsge-meinschaft
(DFG) in the framework of the CRC/TRR 270 (Project ID. 405553726), projects
A02, A03, A05, B05, and Z01/02. JPP acknowledges the German Research
Foundation (Deutsche Forschungsgemeinschaft - DFG) for the funding under
project 429646908. The authors acknowledge Janghyun Jo for experimental help
and Lea Risters for FIB sample preparation. This research used resources of
the Advanced Light Source, which is a DOE Office of Science User Facility
under contract no. DE-AC02-05CH11231.

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Brain-inspired Distributed Cognitive Architecture

Leendert A Remmelzwaal 1*, Amit K Mishra 1, George F R Ellis 2

1 Department of Electrical Engineering, University of Cape Town, Rondebosch,
Cape Town, South Africa 7700.

2 Department of Mathematics and Applied Mathematics, University of Cape Town,
Rondebosch, Cape Town, South Africa 7700.

* Corresponding author<EMAIL_ADDRESS>
###### Abstract

In this paper we present a brain-inspired cognitive architecture that
incorporates sensory processing, classification, contextual prediction, and
emotional tagging. The cognitive architecture is implemented as three modular
web-servers, meaning that it can be deployed centrally or across a network for
servers. The experiments reveal two distinct operations of behaviour, namely
high- and low-salience modes of operations, which closely model attention in
the brain. In addition to modelling the cortex, we have demonstrated that a
bio-inspired architecture introduced processing efficiencies. The software has
been published as an open source platform, and can be easily extended by
future research teams. This research lays the foundations for bio-realistic
attention direction and sensory selection, and we believe that it is a key
step towards achieving a bio-realistic artificial intelligent system.

Keywords: distributed cognitive architecture, affect, cortex, prediction,
corticothalamic connections

##  1 Introduction

In this paper we attempt to answer the question: Can we design a cognitive
architecture based on the modular structure and functionality of the human
brain? Cortical modules such as the corticothalamic circuitry [1], the
ascending arousal systems [2] and vision [3] are the result of of millions of
years of Darwinian natural selection and survival-driven optimisation [4] [5];
in this research we allow these structures to guide the design of our
cognitive architecture. We present a proof-of-concept of a distributed,
computationally efficient, brain-inspired cognitive architecture that has the
ability to tag events with salience and direct cortical attention to the most
salient current event. In addition, this framework is open-source and can be
easily extended by future research teams.

##  2 Motivation

Why did we embark on this project? It is common knowledge that bio-realistic
artificial intelligence is still a distant dream to many researchers. We
embarked on this work because we believe that a modular bio-inspired cognitive
architecture could be a key step in designing a bio-realistic artificially
intelligent system. This is a radically different approach to taking existing
software (e.g. OpenCV) and adapting it to be more brain-like - it is
fundamentally brain-inspired. It be one of the key steps towards achieving
artificial intelligence.

A second motivation was to build a model to simulate some of the behaviours of
key modules in the human brain, including the thalamus, cortex, arousal
system, basal ganglia, and amygdala, to help medical practitioners understand
the effects of disease or accidental damage to the cortex. Wouldn’t it be
great if we could simulate the effect of removing the thalamus, impeding the
release of noradrenaline, or triggering a seizure without relying on living
test subjects? A more extended model of the kind we present could help here.

A third motivation was to design a cognitive architecture with future research
in mind. The distributed modular nature of the architecture we present is
designed to be extended, whereby a research group can independently enhance a
single module in the system, such as the arousal system, without affecting the
working of the entire system. This would allow multiple research teams to
incrementally enhance the entire system by focusing on only a part of the
system.

A fourth motivation was to design a scaleable infrastructure that could be
deployed on a distributed network of servers, allowing the system to exist
outside of just a single server. The implementation leverages a distributed
network of servers, giving autonomy to computational units inspired by key
units in the human brain. While this allows the system to scale, it also
allows the system as a whole to build in redundancies that will improve
efficiency and robustness of the system as a whole.

Finally, we hope that building on the research presented in this paper will
eventually shed light on the nature of consciousness [6] [7].

##  3 Key Structures in the Brain

Our bio-inspired cognitive architecture is modelled closely after a number of
key structures in the human brain, namely the thalamus, cortex, arousal
system, basal ganglia, and amygdala (see Fig 1). These structures play
important roles in sensory processing, prediction, emotional tagging,
attention direction and consciousness [8] [9]. We model some aspects of these
interactions.

Figure 1: Some of the key autonomic and voluntary pathways between the
thalamus, amygdala, arousal system, basal ganglia and the prefrontal cortex.

###  3.1 Arousal System

Emotional tagging of events and memories is key component to consciousness,
intelligence and directing attention in the cortex [10] [11] [12] [9]. The
arousal system releases neuromodulators such as dopamine and noradrenaline
into the cortex via diffuse projections [13] [14] [11]. These long-range
diffuse projections allow neuromodulators to impact an entire pattern of
synaptic connections proportional to their synaptic activation level and the
strength of the neuromodulator released. The release of noradrenaline has a
direct impact of increasing gain on perception, which in turn also directs
cortical attention to the object or event causing the emotional response. For
example, if someone has a fear of dogs, then seeing a dog may make them feel
afraid, which in turn commands their attention to the dog. In this paper we
model the way (a) the arousal system releases noradrenaline, and (b) how the
noradrenaline level decays over time.

###  3.2 Thalamus

The thalamus acts as a relay station between visual, auditory and
somatosensory sensory inputs and the prefrontal cortex [1] [15] [16] [17]. The
connections between the thalamus and the prefrontal cortex are referred to as
corticothalamic connections [18] and consist of both bottom-up (feed forward)
and top-down (feedback) connections [4]. The thalamus is responsible for
comparing the incoming signal to the top-down prediction signal, and sending a
moderated signal back to the cortex if the difference between the predicted
signal and incoming data exceeds a threshold. This threshold can be affected
by the level of neuromodulators released from the arousal system, which is a
key feature of our model. In this paper we focus on modelling the way the
thalamus (a) relays incoming information to the cortex, (b) receives a
contextual prediction from the cortex, and (c) compares the incoming
information to the prediction before deciding whether to send the next image
to the cortex.

###  3.3 Prefrontal Cortex

These prefrontal cortex is responsible for a range of key functions such as
cognition [19], attention direction [20] [21] [22] [23], sensory selection
[24] [25] [26], awareness [27], emotional control [28], prediction [5], and
salience detection [29]. In this paper we focus on modelling the way the
cortex (a) receives sensory inputs, (b) classifies the sensory input, (c)
creates a prediction of the current context, (d) produces a desire to act, and
(e) triggers the release of neuromodulators. The model is a simplified
integrated representation of these features.

##  4 Scope of Work

In summary: we present a proof-of-concept of a brain-inspired cognitive
architecture that incorporates sensory processing, classification, contextual
prediction, and emotional tagging. The architecture is robust and modular;
meaning that it can be deployed centrally or across a network for servers. The
software produced by this research has been published as an open source
platform, and can be easily extended by future research teams.

##  5 Related Work

Our work builds on existing research by connecting together models of
individual modules in the brain, namely models of the thalamus, cortex,
arousal system.

In modelling the thalamus, cortex and corticothalamic connections, we reviewed
the work done on predictive processing by Andy Clark [30], as well as the
models of Retinal Predictive Coding [31], Linear Predictive Coding [32],
Cortical Predictive Coding [33], Restricted Boltzmann machine (RBM) [34], Free
Energy Predictive Coding [35], BC-DIM Predictive Coding [36], Predictive
Sparse Decomposition [37], Stacked Denoising Auto-encoders [38], Deep
Predictive Coding Networks [39], PredNet [40], Multilevel Predictor Estimator
[41], Deep Predictive Coding [42], LPCNet [43], and the CTNN model [44]. We
have chosen to incorporate the CTNN model because unlike the other models, it
is input agnostic, multi-modal and computationally efficient.

In modelling the arousal system, we reviewed the work done on modelling non-
local effects of neuromodulators by Edelman and the primary emotional systems
identified by Panksepp [11]. These models include the Husbands’ model of gas
diffusion [45], Juvina’s model of valuation and arousal[46], Edelman’s brain-
based devices [2] [47], and the Salience-affected Neural Network (SANN) [48].
We have chosen to use the SANN architecture to model the effects of salience
because unlike the other models salience affects a specific neural activation
pattern as a whole, tagging it with a salience signal, and with one-time
learning.

Attempts have previously been made to map the connectome, such as the Blue
Brain Project [49], but we are focused on the functional structures of each
module in the brain, rather than the connections between individual neurons.
Our research is therefore at a level abstracted from mapping the connectome.

##  6 Implementation

The distributed computational model we use is represented in Fig 2, developed
for computational purposes from the set of interactions summarised in Fig 1.
The thalamus, arousal system and memory structures were each modelled as their
own independent Python Flask web-servers. Each web-server could be called by
any other web-server, to request information (e.g. current state of the
cortex) or to request an action (e.g. classifying a new image). For more
technical information on the API protocols see [50].

Figure 2: Overview of the distributed system architecture.

###  6.1 Modelling the Arousal System

The arousal system was modelled as the first stand-alone server. It maintains
as an internal state the levels of neuromodulators such as noradrenaline in
the cortex. Any other system (e.g. the cortex) may trigger the release of a
neuromodulator, and may request the current level of neuromodulator.

Once a neoromodulator has been released, it is programmed to decay at a fixed
rate as represented in (1).Here $x$ is the number of seconds that has passed.

$y=e^{-0.2*x}$ (1)

###  6.2 Modelling the Thalamus

The thalamus system was modelled as the second stand-alone web-server. It
calculates an internal threshold depending on the level of noradrenaline
released by the arousal system. If the difference between the incoming image
and the contextual prediction generated by the cortex system exceeds the
threshold, the thalamus sends the input image to the cortex system for
processing. A key point here is that the thalamus threshold is directly
impacted by the level of noradrenaline release by the arousal system. The
higher the level of noradrenaline, the lower the internal threshold in the
thalamus system. This models the work of Solms and Friston on the relation
between arousal and conscousness.

###  6.3 Modelling the Cortex

The cortex system was modelled as the third stand-alone web-server, and
incorporates the SANN [48] and CTNN models previously published by Remmelzwaal
et al [44]. The cortex system accepts an input image, and processes this input
image using a number of internal models, including an auto-encoder (based on
the CTNN) to reduce image dimensionality and to generate a reconstruction of
the current context, and deep neural network (based on the SANN) that
classifies and returns the salience tag for each image (see Fig 2).

Figure 3: Illustration of information flow in the cortex system.

###  6.4 Graphical User Interface (GUI)

A graphical user interface (GUI) shown in Fig 4 was created to allow the
researcher to vary the input image, and to give the researcher visibility of
key variables in the system (e.g. thalamus threshold, cortical prediction,
neuromodulator level).

Figure 4: The Graphical User Interface (GUI)

##  7 Dataset

In this paper we generated a dataset of black and white silhouettes of animals
in three classes, namely Bird, Cat and Dog (shown in Fig 1). This dataset was
inspired by [51], and is a novel dataset presented for the first time here.

##  8 Training

In this model only the memory structures in the Cortical System are trainable.
Via the GUI or API interface the researcher can initiate training of both the
auto-encoder (used to predict context) and the SANN (to classify and determine
salience). For the purposes of demonstration, a single image of a DOG was
tagged with one-time salience training.

##  9 Results and Discussion

The experiments conducted demonstrate various key results.

###  9.1 Low- and high-salience modes of operation

Firstly, the experiments reveal two distinct operations of behaviour, which
closely model attention in the brain.

The first mode of operation occurs when the system is presented with an input
image that has a low-salience association (see Fig 5A). When initially
presented with such an image, the thalamus notices a significant change in
input, and engages the cortex to classify the image. The cortex is engaged for
one computational iteration, and returns a contextual prediction to the
thalamus. For subsequent viewings with the same image the thalamus does not
engage the cortex. During this mode, the cortex directs attention momentarily
to identify an object, but then the cortex is free to assign attention to
another classification or prediction task.

Figure 5: Two modes of operation. Low-salience (A) and High-salience (B)
inputs

The second mode of operation occurs when the system experiences an input image
with a high associated salience (see Fig 5B). Similar to the first mode of
operation, the thalamus notices a significant change in input, and engages the
cortex to classify the image. Again, the cortex is engaged for one
computational iteration, and returns a contextual prediction to the thalamus.
However this time, the cortex identifies a high salience association with the
high level concept, and two things happen: firstly, the cortex triggers a
release of noradrenaline from the arousal system, and secondly, the release of
noradrenaline lowers the internal threshold of the thalamus System. This has a
direct impact on how the system processes subsequent images. With a lower
internal threshold, the thalamus System sends images to the cortex more
frequently, demanding the computational power and attention of the cortex
system. In this mode, the cortex directs all of its attention to the incoming
images, ignoring other lower-priority tasks.

###  9.2 Confirming neuroscience understanding

Secondly, the simulations conducted in this paper confirm neuroscience
understanding: that the thalamus and arousal system indeed play a crucial
roles in processing incoming data, and directing cortical attention. Without
the Thalamus constantly comparing incoming signals to a contextual prediction,
our brains would be overwhelmed with a non-stop overload of incoming
information to process. Without salience tagging of memories and event enabled
by the Arousal system, the cortex wouldn’t receive the guidance it requires to
direct its attention to the most salient issue

###  9.3 Optimizing performance of complex tasks

In addition to modelling the thalamus and arousal system, we have also
demonstrated that a bio-inspired architecture can optimize performance of
complex processing tasks (e.g. classification and prediction). We have show
that our architecture frees up computational processing during low-salience
events, especially when there is high levels of similarity between one image
an another. We experience this in the physical world almost every day when we
drive - driving at a steady speed down a road is a highly monotonous task,
whereby your brain is free to engage in conversations or to sing along to a
song on the radio. However, if a person steps onto the road, your attention is
immediately directed to managing the salience situation, abandoning almost
instantly the conversation you were having, or song you were singing. We have
demonstrated this effect by adding 2 sub-systems inspired by the functions of
the the thalamus and the arousal system in the brain.

###  9.4 Attention, salience and prediction

In this paper we have demonstrated with the aid of a fully-connected
simulation of the thalamus, arousal system and cortex, how these three key
structures rely on each other to allow for:

  1. 1.

Attention direction [20] [21] [22] [23]; in our model the cortex is engaged
with only those images that differ significantly from the predicted context, a
level which is modulated by the affective response from previous images. This
has the impact of freeing up the cortex for other processing during low-
salience events (e.g. driving a car), and demands attention from the cortex
during high-salience events (e.g. seeing a dog). What is important to observe
is that in our model the cortex is not actively directing its own attention;
it is a emergent feature guided by the arousal system and the thalamus.

  2. 2.

Salience detection [29]; our system allows for events to be tagged with affect
(salience), and for that salience to be returned along with the label if that
combination of sensory inputs is experiences again in the future.

  3. 3.

A simple form of prediction [5]; the cortex generates a prediction of the
current context by leveraging an auto-encoder. The model sends this prediction
of the current context back to the thalamus system to be compared with
incoming sensory information.

###  9.5 Extensibility

We have designed a cognitive architecture specifically with future research in
mind. The distributed modular nature of the architecture we present here
allows research groups to independently enhance and extend a single module in
the system (e.g. the arousal system) without affecting the functioning of the
entire system. The inner working of each module is unknown to other modules,
and each module can only interact with other modules using predefined API
calls. For example, the thalamus can send the cortex a new sensory image, and
can request a prediction, but it is not concerned with how the cortex
processes the information it is sent, or generates the prediction. We believe
that this approach to designing the cognitive architecture is key in allowing
this research to be extended in the future.

##  10 Future Work

In this paper we have presented an open-source brain-inspired distributed
cognitive architecture. The Graphical User Interface (GUI) allows researchers
to examine and interact with the model. We hope that future cognitive
architectures can benefit from the principles presented in this paper.

This model can be extended to include recurrent connections, or to include
prediction over a period of time (e.g. to simulate autonomous behaviour when
driving a car). While the current model accepts only images, it can be
extended to multi-modal inputs such as auditory of somatosensory inputs, as is
demonstrated in [44]. It may also be valuable to explore whether the system
proposed in this paper experiences subjective states, using an evaluation
framework such as the Independent Core Observer Model (ICOM) [53].

We believe that this system could be extended to allow for:

  1. 1.

Emotional control [28]; how does an emotional response translate to an action?
that can guide behaviour of the system. This would extend the ”desire to act”
feature of our current system.

  2. 2.

Sensory selection [24] [25] [26]; how can the cortex select to attention from
a specific sensory input? This would require extending the thalamus model to
allow sending of multiple modes of sensory data, and for the cortex to be able
to process multiple modes of sensory data independently of each other.

The ultimate aim of this paper has been to introduce a cognitive architecture
that may help lead to better modelling and understanding of awareness [27],
consciousness [6], and subjective experiences [52]. We believe the design
principles introduced here can be important in that search.

##  11 Supporting Material

The source code as well as records of the tests conducted in this paper are
publicly available online [50]. For additional information, please contact the
corresponding author.

##  12 Acknowledgements

This research did not receive any specific grant from funding agencies in the
public, commercial, or not-for-profit sectors.

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