#  UNICEF Guidance on AI for Children: Application to the Design of a Social
Robot For and With Autistic Children
††thanks: This project partly was funded by the University of the West of
England. ††thanks: 1Séverin Lemaignan and Joe Daly are with the Bristol
Robotics Laboratory, University of the West of England, Bristol, United
Kingdom<EMAIL_ADDRESS><EMAIL_ADDRESS>††thanks: 2Nigel
Newbutt is with the Department of Education, University of the West of
England, Bristol, United Kingdom<EMAIL_ADDRESS>and the College of
Education, University of Florida<EMAIL_ADDRESS>††thanks: 3Louis
Rice is with the Department of Architecture, University of the West of
England, Bristol, United Kingdom<EMAIL_ADDRESS>††thanks: 4Vicky Charisi
is with European Commission, Joint Research Centre, Sevilla, Spain
<EMAIL_ADDRESS>
Séverin Lemaignan1, Nigel Newbutt2, Louis Rice3, Joe Daly1, Vicky Charisi4

###### Abstract

For a period of three weeks in June 2021, we embedded a social robot (Softbank
Pepper) in a Special Educational Needs (SEN) school, with a focus on
supporting the well-being of autistic children. Our methodology to design and
embed the robot among this vulnerable population follows a comprehensive
participatory approach. We used the research project as a test-bed to
demonstrate in a complex real-world environment the importance and suitability
of the nine UNICEF guidelines on AI for Children. The UNICEF guidelines on AI
for Children closely align with several of the UN goals for sustainable
development, and, as such, we report here our contribution to these goals.

###### Index Terms:

social robotics; responsible AI; child-robot interaction; participatory
design; well-being; autism; UNICEF; UN Goals for Sustainable Development

##  I Introduction

###  I-A Autism

Autism is a lifelong neurodevelopmental condition that can impact how a person
perceives, communicates and interacts with the world. This is characterised by
significant and lasting differences (compared to typical development) in
social communications and interaction, restricted and repetitive patterns of
behaviour, interests or activities and sensory perception and responses.
Current data suggest that as many as 1 in 54 children in the United States of
America are on the autism spectrum [31] while other studies suggest a figure
of between 1 in 68 to 1 in 100 in the general population [29]. This represents
a substantial group of individuals for whom technology support could be
instrumental to their well-being.

###  I-B Context: Well-being in SEN schools

The well-being and emotional regulation of autistic people has been central in
supporting meaningful educational experiences [12]. In comparison to their
typically-developing peers, autistic children experience greater mental health
problems, such as anxiety, depression, anger, and possess lower self-concept
and these can impact education and other factors [13].

There have been a range of studies that have implemented technology to assist
autistic children in this regard [37] [40] [41]. However, these studies have
often focused on deficit-based models and do not often include users in their
design. Other strategies have also been used to support the well-being of
autistic pupils. For example, recent work has highlighted that focusing on
autistic intense or “special” interests in the classroom may be linked to
improved well-being [42]. While the well-being of autistic groups is not well
understood or defined, increasing awareness of the impact that well-being has
on students’ academic performance and their adult outcomes is well
acknowledged [2] [14]. With greater attention applied to the well-being of
autistic pupils, improved outcomes in areas of education and adult outcomes
are often observed. Therefore, considering well-being from an early age and in
a setting where this can be supported (classrooms) is worthy of investigation.
Moreover, doing so in collaboration with autistic children and their teachers
is paramount, if we are to be guided by their goals, ambitions and desires
(for example, linking with ‘special’ interests). Technology provides one
example of a way to help support the well-being of autistic groups in
meaningful and impactful ways.

Within the field of autism and technology there are calls to better include
and support autistic ‘voice’. For example, Cascio and colleagues suggest that
the “growth in autism research necessitates corresponding attention to autism
research ethics, including ethical and meaningful inclusion of diverse
participants” [7]. This suggests that ethics in this field is moving beyond
typical ethical review processes (i.e. IRB approval / Ethical Review) and
towards more meaningful / active engagement of diverse participants in our
work. Coupled with this, the approach to methodologies (broadly speaking) has
begun to move away from researching about autistic people, and started to move
towards an approach that includes autistic people in the design, development
and evaluation of research about them [18]. However, when considering the
nature of autistic children, there can often be further ethical considerations
[33] that include, locating ways for non-verbal individuals to contribute
[26], ensuring a gender balance, enabling participants to communicate in a
range of ways (i.e. Picture Exchange Communication System; PECS), and
prioritising their views and ways of communicating. Furthermore, there are
calls for researchers to involve autistic voice and input even before research
starts; building research that could be meaningful for them [30] [43] [28].
Within this project, we designed research that included a range of
stakeholders, initially autistic pupils and some teachers, through to parents
and an autistic adult (towards the end of the work). We also positioned the
research with autistic voice central; they would be ones directing what the
technology (Pepper the Robot) would do, where and why. In doing so we placed
their lived experiences central, and their ideas for a robot in their school.
This enabled and allowed their voices and input to direct what we did, which
is what we next discuss.

###  I-C The UNICEF guidance on AI for children

This work takes place in the context of the release by UNICEF of a landmark
report on artificial intelligence and children, entitled _Policy guidance on
AI for children_ [15]. This reports lays out 9 guidelines to design and build
AI systems aimed at children. These are:

1. Support children’s development and well-being;  2. Ensure inclusion of and for children;  3. Prioritise fairness and non-discrimination for children;  4. Protect children’s data and privacy;  5. Ensure safety for children;  6. Provide transparency, explainability, and accountability for children;  7. Empower governments and businesses with knowledge of AI and children’s rights;  8. Prepare children for present and future developments in AI;  9. Create an enabling environment.

These guidelines are based on the United Nations Convention on the Rights of
the Child (UNCRC) which came into force by the UN General Assembly in the UK
in 1989 and they and includes 54 articles that cover civil, political,
economic, social and cultural rights that all children everywhere are entitled
to [39]. Four of these articles play a fundamental role in realising all the
rights in the Convention for all children and are known as ‘General
Principles’ i.e. (i) non-discrimination (article 2); (ii) Best interest of the
child (article 3); (iii) Right to life survival and development (article 6);
and Right to be heard (article 12). Realising the need for the consideration
of the UNCRC in the context of digital environment, the UN committee adopted
Comment 25 [38], which states that the rights of children will now apply
online as they do offline. At the same time the United Nations International
Communication Union (ITU) has launched a set of guidelines on child’s online
protection with recommendations for all stakeholders on how to contribute to
the development of a safe and empowering online environment for children and
young people [21].

In a similar way, the recent developments in AI expanded the possibilities and
the opportunities for children’s well-being but they also resulted in further
concerns regarding emerging risks in relation to children’s rights. In this
context, UNICEF published a report with the above-mentioned Policy Guidelines,
which have several complementary focii: the children as individuals (e.g. (1),
(8)); the children at group/societal level (e.g. (2), (3)); technical
underpinnings and regulations ((4), (5)); or the children’s environment ((7),
(9)).

Together, they build a comprehensive framework that impact many aspects of the
AI system design. Importantly, they implicitly require the designers to ‘step
back’ from the system itself, and adopt a much wider perspective, taking into
account the end-users needs and circumstances, as well as the broad context
and environment of use. While these guidelines are meant to facilitate
research, development and policy regarding AI and Child’s Rights, they only
partially consider emerging issues that are related to robotics and the
opportunities and risks that emerge in relation to the embodied nature of
robotic artefacts for children. In addition, a children’s rights-based
approach rejects a traditional welfare approach to children’s needs and
vulnerabilities and instead recognises children as human beings with dignity,
agency and a distinct set of rights and entitlements, rather than as passive
objects of care and charity. When developing AI and AI-based robotic artefacts
for autistic children, these principles are paramount. In the following
sections, we discuss existing work on the intersection of ethical
considerations for social robots and autistic children and we indicate the
need for further systematic research towards this direction.

###  I-D Previous work on the ethics of child-robot interactions

Research in the field of child-robot interaction has already indicated that
the use of robots bring unique opportunities for autistic children and has a
powerful impact on their behaviour and development mainly because of the
embodied nature [4, 22, 10]; however, an increasing body of research has
started exploring the possible emerging risks and ethical considerations that
should be addressed even from the design process of robotic interventions for
autistic children [11]. One of the robot-specific characteristics that
contributes to those concerns is their dynamic (not static) nature which allow
robots’ navigation into the human physical environment and the physical
interaction with humans in a way that might have an impact on children’s basic
fundamental rights such as safety, privacy and autonomy. Systematic analyses
of ethical considerations in research of robots for autistic children is still
scattered and it usually appears as a reflection on existing practices such as
in [11] and [27]. In principle, the discussions on the ethical considerations
in research of robot for autistic children has mainly the form of anticipatory
ethics, meaning the anticipation of how future technologies will be applied
and what their consequences might be [5, 23]. The focus of these discussions
is mainly on harm prevention, data ethics issues, the robot autonomy and the
transparency of the algorithms [24, 11]. Especially for the design of robot-
supported therapeutic interventions, there is still uncertainty regarding the
degree of human supervision and the long-term effects of applications that
understand the stages of the human condition and their integration into
therapy [17].

To address the emerging design challenges in robot-assisted diagnosis and
interventions, one of the methodological approaches that has been widely used
is the participation of parents and autism clinicians and educators [20, 1,
36]. While in general stakeholders agree about the positive effects of robots
in therapy for autistic children, they indicate that an approach of robot
supervised autonomy would create more trust and would improve the quality of
the therapy.

Although this body of research contributes in a substantial way to our
understanding regarding the integration of trustworthy robots for autistic
children, it seems that only a few studies include autistic children in the
process of the design of robot-supported interventions for them. Considering
the participation of autistic children in the design process is a challenging
process which requires a delicate interplay between the pre-established
broader ethical principles and guidelines with micro-ethics, especially for
designs for marginalized children [34]. Spiel et al. highlight the importance
of immediate interaction between researchers and marginalized children in
participatory projects with in-situ micro-ethical judgements (ibid.).

We build upon this line and we consider the policy guidelines on AI and
Child’s Rights as published by UNICEF as well as our reflections on our
participatory design study with autistic children to re-examine the guidelines
in the specific context of the design of a social robot with and for children.
To our knowledge, this is the first systematic endeavour of the consideration
of these guidelines in the context of social robotics and autistic children.

##  II Overview of our approach

(a) One of the children focus groups: during one hour semi-structured
workshops, children could interact with the robot, and express their ideas
about the robot’s role. (b) Physical location of the robot during the study,
in a school corridor. In the foreground, one researcher passively observing
the interaction.

Figure 1: Photos of the co-design process and the study.

Building on interdisciplinary expertise (with the project’s three Principal
Investigators coming from robotics, education and architecture) and an
existing collaboration with the UK-based SEN school The Mendip School (which
hosts 144 autistic children), we co-designed a social robot to support the
children (based on their ideas), and deployed it for a total duration of three
weeks in the school. Indeed, our methodology has two main phases: the
iterative and participatory co-design of the robot behaviours and the context
for its use; a three-week in-situ deployment of the robot in the school; and
the observations of its use and impact on the complex and dynamic context of
the school.

The research methods employed during the first phase comprise of: (1) a half-
day workshop at the school, with two focus groups with the target population,
as well as unstructured interactions with other pupils (Fig. 1(a)); (2) a one-
day workshop among the research team, with an external invited academic,
expert in child-robot interactions and responsible AI. This workshop was
focused on mind-mapping the outcomes of the children focus groups; (3) a day
with the robot embedded in the staff room to familiarise teaching staff with
the robot’s capabilities; (4) a one hour focus group with the school’s
teachers, where the results of the children’s focus group were discussed and
built upon and further insights from the teachers about the integration of
Pepper into the school ecosystem; (5) a half-day workshop with an autistic
academic, to better account for the autistic community perspective on this
line of research and accordingly refine the project framing and interaction
design.

Taken together, we found that the children felt Pepper could support their
emotional and general well-being. The children suggested that: “I kind of like
him”; “I’d say I am like excited with it”; “umm..umm… I’d say I am like
excited with it…also, like looking at her, it like”; and “robots are cool and
very, very helpful for learning”. In addition, one pupil suggested that: “I’m
not that much of a people person… I’m a robot person… I find it very hard to
make friends with an actual person”, while another said: “it could be a good
idea that it could like help others […] it could help with people’s feelings,
like if people are sad […] it could help them get better”. These ideas were
explored further in the focus groups. This feedback, along with ideas from the
teachers, also helped to provide clear ideas for what Pepper would do and the
interface we would build. Engaging with the pupils in this way ensured the
researchers were guided by what the children wanted to use Pepper for.

The research team also connected the children’s input with the teachers and
their approaches for teaching, learning and behaviour management in their
classes/school. It soon became clear through the teacher’s focus group that
adopting the school’s existing materials (emotional regulation sheets) would
be an appropriate way to ensure pupils could interface with Pepper in a manner
they would be familiar with (i.e. selecting an input that first asks how the
pupil is feeling: happy, sad, angry, frustrated, tired, etc…).

One of the key outcomes of this co-design phase is the focus and framing of
our research on the _well-being of autistic pupils in their school_ rather
than _robot-supported cognitive development support_. Indeed, contrary to
previous research on robots for autism that focus on using robots to teach
social skills to the children (eg [32, 16, 35, 6, 19]), our focus groups have
evidenced that more than social skills, there is a demand for social support
and well-being. As a result, the interactions with the robot are unstructured
(e.g. not scheduled), child-led and can be indifferently one-to-one or group-
based. Also, unlike most of the previous research in this field, the robot is
located in the school corridors, interacting autonomously with the children in
a mostly unstructured way, rather pre-arranged in-classroom interactions. This
gives more freedom to the children to choose when and how they want to
interact.

The second phase took place in the school where the robot was placed in a
communal area (see Fig. 1(b)), and could be interacted with by the children
(group of about 50 students, aged 13-16) at any time during the school day
(8.45am-3pm). Specifically, no dedicated interaction time was scheduled for
the pupils, as the researchers wanted to see whether and how robot usage
patterns would emerge from within the school eco-system. As such, the robot
has been used and observed in a naturalistic context. We argue that this
indeed helps to form a better understanding of the actual impact of the robot
on a school dynamics compared to structured face-to-face child-robot
interactions.

The methods used during this phase comprise of observations made by the
researcher while observing the robot from a distance, children’s self-reported
mood before and after interacting with the robot, quantitative measurements of
the interactions (robot’s logs), post-hoc questionnaires administered to the
children, and post-hoc group interviews with the teachers (note however that
the analysis of these observations is outside of the scope of this article).

##  III Application of the UNICEF guidelines to the study design and
implementation

Building upon the initiative of HRI2021 workshop on Child-Robot Interaction &
Child’s Fundamental Rights [8], our project with The Mendip School attempts to
explicitly address through its methodology the nine principles outlined in the
UNICEF policy guidance.

We present hereafter how each of the nine guidelines are accounted for in our
project and methodology.

###  III-A Principle 1 – Support children’s development and well-being

The principle _Support children’s development and well-being_ is directly
reflected in the aim of our work: we installed a social robot in a SEN school
for 3 weeks, with the primary role of the robot being to provide social
support and comfort to the pupils who requested it. The pupils’ well-being and
social needs were identified from the in-school focus groups with the children
themselves and their teachers; the robot’s eight basic behaviours (including
story telling, dancing, playing calm music, cuddling, telling jokes) were
identified during the focus groups as well, and available to the pupils during
the study.

###  III-B Principle 2 – Ensure inclusion of and for children

The inclusion _of_ and _for_ children is a fundamental aspect of the project:
the robot is explicitly aimed at autistic children, a group that might
otherwise be excluded of many AI-related technologies.

The children co-design the robot’s behaviours with the researcher. As such,
they are included from the onset in the creation of the AI system. The system
is developed primarily for the children, and its behaviour and performance is
only measured against the children’s needs.

###  III-C Principle 3 – Prioritise fairness and non-discrimination for
children

The study takes place in a very diverse environment, with a broad range of
mental and physical abilities. In particular, some children are non-verbal,
which could prevent them to interact with the system. To address this issue
(as well as the technical limitations of speech technology in a school
environment [25]), we have designed an hybrid interaction mechanism, where the
robot speaks to the children, and the children answer by pressing large, icon-
based, buttons.

The location of the robot, in a communal space (school corridor), enables
interaction with everyone, in a non-discriminatory way. Interactions are
child-led: because the children start themselves the interaction, the robot
can not discriminate against sub-groups (due to eg detection issue of children
in wheel chairs, etc.)

Finally, the robot’s behaviours are co-designed between the children, the
teachers and the researchers, taking the end-users voice into account at every
level, and ensuring fairness and non-discrimination in the resulting robot
capabilities.

###  III-D Principle 4 – Protect children’s data and privacy

Working with SEN children is ethically sensitive, and protecting the children
privacy is essential; the robot is equipped with cameras and microphone, that
could potentially be misused. While we sought the consent of both the parents
and the children to take part in the study (and the children could choose not
to interact with the robot), we additionally decided for the robot not to
store any personal information (no videos, no audio). Only anonymous data is
stored (like the position of children around the robot, but not their
identities). Furthermore the robot is not connected to the Internet
(preventing any possibility of malicious remote access).

###  III-E Principle 5 – Ensure safety for children

The robot is a physical device, which could in principle cause harm. The robot
is designed as a social agent, which might trigger undesired affective bonding
from some children (especially vulnerable children).

The choice of the robot (Softbank Pepper) is based on its general, risk-
assessed, safety to interact with human and children in particular. The height
of the robot (1.2m) is similar to the children. The robot’s arms are
lightweight and compliant, meaning they can not cause serious injury, even in
case of malfunction.

The psychological safety of the robot is investigated in the study; the on-
site researcher and school staff were trained to recognise children’s distress
if/when it happened. As such we were able to respond immediately should
anything happen. During our initial testing of Pepper in the school, and in
consultation with the school, we devised ways to minimise any adverse effects
of Pepper. For example, we de-briefed with teachers and school leaders every
day with a view to reflect on, and if necessary, adjust our practices in the
school. This included the teachers reporting anything adverse in their
classrooms related to Pepper’s presence.

One final consideration was that of an ‘exit’ strategy for Pepper. Autistic
children can become tied to routine quickly [3] and removing Pepper, having
become used to seeing Pepper, could have proven to be a problem for some of
the children. In this regard, we ensured that Pepper said ‘good-bye’ at a
school assembly and that pupils had a chance to see Pepper before the study
ended. However, this issue does need careful and considered thought in work of
this nature.

###  III-F Principle 6 – Provide transparency, explainability, and
accountability for children

The robot can be perceived as a ‘magical being’ by the children, hiding its
decision making process behind interaction tricks.

What ‘transparency’ or ‘explainability’ means from the perspective of a child
is however not entirely clear yet. To shed some light on that question, we
have exhaustively recorded every question asked by the children about the
robot during the 3-week study, collecting 60+ questions. During the study the
on-site researcher answered children’s questions about the robot to the best
of their ability in terms appropriate to the children’s level of
understanding. These answers and subsequent conversations were also recorded.

While still being analysed, this data should enable a better understanding of
what an explainable and transparent behaviour means from the unique
perspective of the autistic children.

Contributing to the robot’s behavioural transparency, however, is the fact
that the interaction is mostly child-led: the robot’s behaviours are triggered
and chosen by the children, who remain ‘in control’ of the interaction,
ensuring a level of explainability and accountability.

###  III-G Principle 7 – Empower governments and businesses with knowledge of
AI and children’s rights

Deploying an autonomous robot in a school for a meaningful period of time will
raised awareness about the potential benefits of AI in a sensitive societal
environment. It will also start the discussion with all stakeholders (from the
children and their parents, to the Department for Education of the government)
around the interplay between AI and children.

As a part of the project the university (UWE) produced a press release across
several online platforms, including the university’s website and TikTok. By
sharing news about the project the public, and by extension government and
businesses, were informed of the potential benefits of AI in this environment.
We reviewed outgoing publications relating to the project to ensure that it
was a fair and reasonable representation of the project’s aims (to support the
well-being of the children), the robot’s role in the school, and its impact on
the pupils.

###  III-H Principle 8 – Prepare children for present and future developments
in AI

The children are given the opportunity to freely interact with a robot for a
meaningful period of time, experimenting with how AI and technology might
impact their lives, and better understanding what they think would be
beneficial (or not) to them.

The project will help prepare children for AI through exposure to these
technologies during their educational experience in a school setting. As
digital natives, this exposure can assist in familiarising children with
technologies as they develop, and respond to the needs of the children. The
use of co-design should further empower children to engage with the
development of these technologies.

###  III-I Principle 9 – Create an enabling environment

By embedding a robot in a rich, child-centered environment like a school, with
a range of stakeholders able to interact with the robot (children, teacher,
other school staff, parents), this study contributes to creating an enabling
environment, where AI is not only an abstract concept, but a physical,
situated system that each stakeholder can question and challenge. The
behaviours of the social robot are designed to help empower and enable the
children with their educational and emotional needs.

##  IV Discussion and outlook

###  IV-A Limits of the approach

Our experiences, while positive, did reveal two key cases that need to be
discussed. The first was a pupil who did not want to interact with Pepper at
all. In fact they entered the school via a separate door to make sure they did
not even see Pepper. This was managed by the school and the pupil’s teacher.
There was also a case of a pupil who was in a classroom adjacent to where
Pepper was based in the corridor. This pupil did not like Pepper either,
repeatedly stating “That stupid robot, I don’t like it, when it is going?”. So
in these two instances managing the situation, and being carefully aware of it
was paramount. In the case of the first child we found out that they are
fearful of any digital technologies that do not work correctly (i.e. not
specific to robots). In the case of the second pupil, we worked very closely
with their teacher. We de-briefed with the teacher each afternoon (after
school) to ensure that the pupil was not distressed or having their education
impacted in anyway. While we offered in several occasions to interrupt the
study, the school insisted for it to continue, and interestingly, we were
informed by this pupil’s teacher, that he was in class more (he would
previously spend lots of time in the corridor) and engaged in learning in much
more meaningful ways. So issues like this need (1) recognising (that not all
pupils will like a robot like Pepper) and (2) managing carefully if/when they
arise.

In another case we also found that some pupils, and one in particular, seemed
to become emotionally attached to Pepper. She enjoyed spending time with
Pepper listening to stories, calming music and also touching Pepper (stroking
the head and holding hands, for example). As a result of removing Pepper at
the end of the study, we had a follow up email from her parent (father), the
day after the study ended in the school, who commented: ”On Friday evening she
[the pupil] came to me and said that she was sad. When I asked why she said
that Pepper had left today. She went on to say that she will miss Pepper and
hopes to meet again one day. This is a very strong reaction for [her]. through
the project she has gone out of her way to tell us when she interacted with
Pepper. Seeing Pepper quickly became the highlight of her days”. While this
project attempted to minimise potential distress from the robot’s removal,
this interesting response also raises issues for researchers who need to very
carefully manage the emotional attachment some may have with a social robot
over a long period of time.

###  IV-B Relation to the UN sustainable development goals

The UNICEF guidelines align with several of the UN goals for sustainable
development. In particular, UN goals 3 (Good Health and Well-being) and 4
(Quality Education) align with the UNICEF Principle 1 _Support children’s
development and well-being_ outlined above, and UN goal 10 (Reduced
Inequalities) with UNICEF Principle 3 _Prioritise fairness and non-
discrimination for children_. With the overall agendas of both organisations
closely aligned, our work informs and contributes to both sets of goals.

###  IV-C Conclusion and next steps

While relatively broad in its wording, the UNICEF guidance sets a high bar for
the design of ethical and responsible AI systems, by forcing the designers to
account for the wider context of use of the technology. This policy guidance
did not have robotics as a specific focus, and consequently, one might
question whether such guidance would be appropriate and applicable to embodied
AI systems like robots.

While the scope of our study is modest (in total, 13 days of actual robot
deployment in the school), it is also a challenging one: working with
vulnerable populations (autistic children, some of them with additional
cognitive, emotional and/or physical disabilities) in their naturalistic
environment (the school) and over a period of time long enough to go past the
initial excitement (novelty effect) and explore longer-term adoption, is far
from routine.

In that context, we found that the UNICEF guidance aligned closely with our
user-centered approach and provided a strong ethical framing for the work. We
suggest that future work in the field of social robotics, and especially with
children, carefully consider the UNICEF guidance in their ethical and
practical approach of designing child-robot interfaces. In fact we suggest
that the field should be led by users (in this context children) and that
child-led-robot-interactions, designed with and for them, represents a major
step in co-designing the future in this field.

To address the many implementation complexities, UNICEF invited governments
and business sector to pilot the policy guidelines in various fields. Two of
the pilot case-studies are closely related to our research: (i) the adoption
of the guidelines for the Haru social-robot for typically developing children
and (ii) the AutismVR Imisi 3D application that teaches people how to interact
with autistic children. While results of the first case-study has contributed
to the development of a framework on social robots and child’s fundamental
rights [9], this was developed with the participation of neurotypical children
and further long-term and systematic research is needed for its examination
with autistic children.

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# Magneto-Acoustic Waves in antiferromagnetic CuMnAs excited by Surface
Acoustic Waves

M. Waqas Khaliq<EMAIL_ADDRESS>Dept. of Condensed Matter Physics,
University of Barcelona, 08028-Barcelona, Spain  ALBA Synchrotron Light
Facility, 08290-Cerdanyola del Valles, Barcelona, Spain  Oliver Amin  School
of Physics and Astronomy, University of Nottingham, NG7 2RD, UK  Alberto
Hernández-Mínguez  Paul Drude Institute for Solid State Electronics,
10117-Berlin, Germany  Marc Rovirola  Dept. of Condensed Matter Physics,
University of Barcelona, 08028-Barcelona, Spain  Institute of Nanoscience and
Nanotechnology (IN2UB), University of Barcelona, 08028-Barcelona, Spain  Blai
Casals  Institute of Nanoscience and Nanotechnology (IN2UB), University of
Barcelona, 08028-Barcelona, Spain  Dept. of Applied Physics, University of
Barcelona, 08028-Barcelona, Spain  Khalid Omari  Dept. of Electronic
Engineering, Royal Holloway, University of London, TW20 0EX-Egham, UK  Sandra
Ruiz-Gómez  Max Planck Institute for Chemical Physics of Solids,
01187-Dresden, Germany  Simone Finizio  Swiss Light Source, Paul Scherrer
Institute, 5232-Villigen, Switzerland  Richard P. Campion  School of Physics
and Astronomy, University of Nottingham, NG7 2RD, UK  Kevin W. Edmonds  School
of Physics and Astronomy, University of Nottingham, NG7 2RD, UK  Vít Novák
Institute of Physics AS CR, 16253-Prague, Czech Republic  Anna Mandziak
SOLARIS National Synchrotron Radiation Centre, 30-392-Kraków, Poland  Lucia
Aballe  ALBA Synchrotron Light Facility, 08290-Cerdanyola del Valles,
Barcelona, Spain  Miguel Angel Niño  ALBA Synchrotron Light Facility,
08290-Cerdanyola del Valles, Barcelona, Spain  Joan Manel Hernàndez  Dept. of
Condensed Matter Physics, University of Barcelona, 08028-Barcelona, Spain
Institute of Nanoscience and Nanotechnology (IN2UB), University of Barcelona,
08028-Barcelona, Spain  Peter Wadley  School of Physics and Astronomy,
University of Nottingham, NG7 2RD, UK  Ferran Macià<EMAIL_ADDRESS>Dept.
of Condensed Matter Physics, University of Barcelona, 08028-Barcelona, Spain
Institute of Nanoscience and Nanotechnology (IN2UB), University of Barcelona,
08028-Barcelona, Spain  Michael Foerster<EMAIL_ADDRESS>ALBA Synchrotron
Light Facility, 08290-Cerdanyola del Valles, Barcelona, Spain

# Néel vector waves in antiferromagnetic CuMnAs excited by Surface Acoustic
Waves

M. Waqas Khaliq<EMAIL_ADDRESS>Dept. of Condensed Matter Physics,
University of Barcelona, 08028-Barcelona, Spain  ALBA Synchrotron Light
Facility, 08290-Cerdanyola del Valles, Barcelona, Spain  Oliver Amin  School
of Physics and Astronomy, University of Nottingham, NG7 2RD, UK  Alberto
Hernández-Mínguez  Paul Drude Institute for Solid State Electronics,
10117-Berlin, Germany  Marc Rovirola  Dept. of Condensed Matter Physics,
University of Barcelona, 08028-Barcelona, Spain  Institute of Nanoscience and
Nanotechnology (IN2UB), University of Barcelona, 08028-Barcelona, Spain  Blai
Casals  Institute of Nanoscience and Nanotechnology (IN2UB), University of
Barcelona, 08028-Barcelona, Spain  Dept. of Applied Physics, University of
Barcelona, 08028-Barcelona, Spain  Khalid Omari  Dept. of Electronic
Engineering, Royal Holloway, University of London, TW20 0EX-Egham, UK  Sandra
Ruiz-Gómez  Max Planck Institute for Chemical Physics of Solids,
01187-Dresden, Germany  Simone Finizio  Swiss Light Source, Paul Scherrer
Institute, 5232-Villigen, Switzerland  Richard P. Campion  School of Physics
and Astronomy, University of Nottingham, NG7 2RD, UK  Kevin W. Edmonds  School
of Physics and Astronomy, University of Nottingham, NG7 2RD, UK  Vít Novák
Institute of Physics AS CR, 16253-Prague, Czech Republic  Anna Mandziak
SOLARIS National Synchrotron Radiation Centre, 30-392-Kraków, Poland  Lucia
Aballe  ALBA Synchrotron Light Facility, 08290-Cerdanyola del Valles,
Barcelona, Spain  Miguel Angel Niño  ALBA Synchrotron Light Facility,
08290-Cerdanyola del Valles, Barcelona, Spain  Joan Manel Hernàndez  Dept. of
Condensed Matter Physics, University of Barcelona, 08028-Barcelona, Spain
Institute of Nanoscience and Nanotechnology (IN2UB), University of Barcelona,
08028-Barcelona, Spain  Peter Wadley  School of Physics and Astronomy,
University of Nottingham, NG7 2RD, UK  Ferran Macià<EMAIL_ADDRESS>Dept.
of Condensed Matter Physics, University of Barcelona, 08028-Barcelona, Spain
Institute of Nanoscience and Nanotechnology (IN2UB), University of Barcelona,
08028-Barcelona, Spain  Michael Foerster<EMAIL_ADDRESS>ALBA Synchrotron
Light Facility, 08290-Cerdanyola del Valles, Barcelona, Spain

###### Abstract

Magnetoelastic effects in antiferromagnetic CuMnAs are investigated by
applying dynamic strain in the 0.01% range through surface acoustic waves in
the GaAs substrate. The magnetic state of the CuMnAs/GaAs is characterized by
a multitude of submicron-sized domains which we image by x-ray magnetic linear
dichroism combined with photoemission electron microscopy. Within the explored
strain range, CuMnAs shows magnetoelastic effects in the form of Néel vector
waves with micrometer wavelength, which corresponds to an averaged overall
spin-axis rotation up to 2.4∘ driven by the time-dependent strain from the
surface acoustic wave. Measurements at different temperatures indicate a
reduction of the wave amplitude when lowering the temperature. However, no
domain wall motion has been detected on the nanosecond timescale.

††preprint: APS/123-QED

Antiferromagnets (AFM) have become a focus of recent research in spintronics,
mostly thanks to their potential advantages for future devices. Their low
stray fields and robustness versus external magnetic fields are favorable for
the further down-scaling of memory elements and their high-frequency internal
resonances promise higher intrinsic speed limits for operation. However,
together with these advantages also challenges arise, for example, related
with the readout and mostly the writing process. Magnetic field control,
although not completely impossible [1], is impractical due to the field
magnitudes required ($\gtrsim$ 2 T) in order to overcome exchange energy and
modify the magnetization of the two sublattices existing in the AFM. Domain
modification by electrical currents has been demonstrated through Spin
transfer/orbit torque [2] as well as through thermoelastic effects [3].
Specifically for CuMnAs, the manipulation of antiferromagnetic domains in thin
films has been studied by means of injecting current pulses [2, 4, 5] and
defects [6]. Other approaches use the transitions to a ferromagnetic (FM)
phase, like in FeRh [7], or the coupling with a FM [8], which compromise many
of the potential advantages (stray fields, speed) of AFM materials for usage
in a real device. On the other hand the appearance of closure domain-like
features in patterned AFM samples has been attributed to magnetoelastic
effects caused by shape dependent strain [9], which suggests that much smaller
energies than the exchange energy may be enough to rotate the Néel vector.

Surface acoustic waves (SAW), are propagating elastic deformations in the
upper micrometric layer of a crystal. SAW can be conveniently excited in
piezoelectric materials by radiofrequency electrical signals applied to an
antenna-like structure named interdigitated transducer (IDT). Typical strain
amplitudes achieved under UHV conditions can reach the range of $2\times
10^{-4}$ for LiNbO3 in the hundreds of MHz to GHz frequency regime [10]. There
is a sizable interaction of SAW with FM systems in heterostructures, which is
driven by the transfer of the time dependent strain state of the
underlayer/substrate into the FM overlayer. The interaction is mediated by the
magnetoelastic effect and has been investigated by a growing number of groups
[11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23] (see review articles [24,
25, 26] and references therein). GaAs, apart from being a suitable substrate
for epitaxial CuMnAs growth, has substantial applications in optoelectronic
devices due to its outstanding photovoltaic properties [27] and robust
piezoelectricity [28].

Figure 1:  Schematic illustration of the stroboscopic experiment. The CuMnAs
thin film is analyzed in the PEEM microscope. SAWs are generated in the GaAs
substrate by applying an electrical signal which is synchronized with the
synchrotron frequency (repetition rate time of the X-ray pulses). The PEEM
image is formed by photoelectrons emitted from the sample under the X-ray
illumination.

In this paper we generate Néel vector waves in collinear antiferromagnet
CuMnAs induced by the time dependent strain from the supporting GaAs
substrate. We obtain direct images using stroboscopic X-ray magnetic linear
dichroism combined with photoemission electron microscopy (XMLD-PEEM) of both
dynamic strain and Néel vector oscillations with a quantification of the
amplitude of the spin axis rotation up to 2.4∘. The overall amplitude of the
observed Néel vector oscillations decreased with lowering temperature.

Experiments were carried out at the CIRCE beamline of the ALBA Synchrotron
light source [29]. The CuMnAs epitaxial thin films with 45 nm thickness were
grown on lattice-matched GaAs substrate by molecular beam epitaxy as described
previously [30]. To generate SAWs with a frequency tuned to the synchrotron
repetition rate (500 MHz), IDT with a finger periodicity of 5.73 $\mu$m (which
determines the SAW wavelength) were patterned and deposited on the GaAs by
electron beam lithography and metal evaporation. The sample was mounted on a
printed circuit board (PCB) inside the sample holder and the IDT were
contacted with wire bonds to apply electrical signals. The schematic
illustration of the experiment is presented in Fig. 1. The radiofrequency
signal applied to the IDT generates a SAW beam [31] traveling along the [110]
crystalline direction of the GaAs substrate and confined to a depth in the
order of the SAW wavelength [26]. The SAW causes a periodic in-plane, parallel
to the SAW propagation direction, and out-of-plane change in the substrate
lattice constant which is transferred as strain to the CuMnAs film.

In order to assess the structure of the CuMnAs thin film, X-ray diffraction
(XRD) measurements were carried out on the sample using a laboratory
diffractometer before the synchrotron experiment. The XRD pattern in Fig. 2a
shows the peaks of both the CuMnAs film (black) and the GaAs substrate (red)
with respect to $2\theta$. The tetragonal crystal system (P4/nmm) and the
planes (001), (002), (003), and (004) of the antiferromagnetic thin film were
identified with the JCDPS-ICDD 01-082-3986 [32, 33].

X-ray absorption spectroscopy (XAS) measurements of the CuMnAs were performed
detecting low energy secondary electrons in the PEEM while scanning the photon
energy. The beamline is equipped with an undulator that enables the control of
the incoming X-ray polarization, for example between linear horizontal
(electric field vector in the sample plane) and vertical (electric field
vector under 16 degree to the sample normal) polarization directions. X-ray
absorption spectra at the Mn $L_{3,2}$ edges, with both polarizations, and
their difference (linear dichroism, XMLD), are depicted in Fig. 2b at $T\simeq
235$ K. The features of the XMLD spectrum (blue line), marked by black boxes,
are similar to previously published data [34].

Figure 2:  (a) XRD measurements of 45 nm thin film of CuMnAs on GaAs showing
the film (black) and substrate (red) peaks b) XAS and XMLD spectra of
CuMnAs/GaAs at the Mn $L_{3,2}$ edges, obtained with horizontal (black) and
vertical (red) linear polarization. c) An XMLD image of CuMnAs thin film at
$T\simeq 223$ K by employing linear horizontal polarization component without
applying SAW signal. d) Quantification of the SAW: Average intensity (number
of emitted electrons) from the red and blue rectangles in the inset as a
function of sample bias voltage. The inset shows the XPEEM image of the SAW in
GaAs at 0.7 V bias.

The imaging of antiferromagnetic domains in the CuMnAs thin film is performed
by PEEM employing XMLD contrast. Such a contrast is obtained by subtracting
different images taken with linear horizontal polarization at energies before
and at the $L_{3}$ absorption peak (See details of the methodology in Supp.
Mater. I). Fig. 2c shows the domains arrangement in the film at 223 K nominal
temperature without any SAW applied. Equivalent images taken with linear
vertical polarization (electric field vector 16∘ to the sample normal) did not
show visible contrast, confirming a dominant in-plane Néel vector.

In order to quantify the dynamic magnetoelastic effects in the CuMnAs film, it
is necessary to determine first the amplitude of the SAW-induced strain in the
GaAs substrate. The inset of Fig. 2d depicts an XPEEM image of the sample
surface, measured with 500 MHz pulsed synchrotron X-rays. An intensity
contrast with a periodicity matching the SAW wavelength, i.e., 5.73 $\mu$m, is
evident in the sample region not covered by the CuMnAs film. This contrast
originates from the oscillating piezoelectric potential accompanying the
strain wave at the surface of the GaAs substrate. The photoelectron spectra
displayed in Fig. 2d show the average intensity (number of detected electrons)
at the areas marked by the red and blue rectangles in the inset image,
recorded as a function of the bias voltage applied to the substrate for a
fixed energy analyzer configuration. The voltage shift between these curves,
obtained by selecting the positions of maximum slope in both spectra, amounts
to 0.35 V and corresponds to the peak-to-peak amplitude of the oscillating
piezoelectric potential. This value is used to calculate the amplitude of the
strain field by numerically solving the coupled differential equations of the
mechanical and electrical displacement, obtaining values in the range of 0.01%
at the sample surface. Details on stroboscopic XPEEM measurements with
synchronized SAW can be found in [10].

Figure 3: XMLD images of CuMnAs with opposite electronic phases $\psi=0$∘ in
a) and $\psi=180$∘ in b) of the SAW excitation at $T\simeq 223$ K. The
direction of SAW propagation is perpendicular to the dashed lines which are
separated by one wavelength of 5.73 $\mu$m. c) Image obtained by subtracting
the images at opposite phases, at the same contrast scale. No evident
variations in the domain boundaries can be observed.

Fig. 3 shows XMLD images taken while the SAW is applied. The bright and dark
areas in Fig. 3 correspond to domains with spin axis parallel and
perpendicular to the x-ray polarization, with a typical domain size below one
micrometer. The presence of domains with continuously differing gray scale
contrast indicates the absence of significant in-plane anisotropy for the spin
axis in the sample. Between Fig. 3a and 3b, the phase of the radiofrequency
signal exciting the SAW was shifted by 180∘. Thus, the phase of the SAW in any
given position is inverted for the stroboscopic measurement, i.e., when the
X-rays hit the sample. A close inspection of the individual images shown in
Figs. 3a and 3b as well the difference image (Fig. 3c), same gray scale, does
not show any observable change of the domain boundaries. The domain wall
motion in the CuMnAs in the present experiment is thus either negligible or
below the detection limit. The difference images are used to eliminate the
static domain contrast and enhance the dynamic changes induced by the SAW.

Figure 4: Néel vector wave (spin axis rotation wave) in CuMnAs observed by
XMLD-PEEM. a) example of an XMLD difference image from opposite SAW phases.
Line profiles are taken along the blue rectangle box, in the propagation
direction of the SAW (red arrow). (b-d) XMLD signal (in %) along the line
profile after averaging at $T\simeq 223$ K in b), $T\simeq 233$ K, in c) and
$T\simeq 296$ K in d). The black line in each plot shows the data and the
green line is the sinusoidal function curve fitted to the data points.
(insets) fit results for single profiles before averaging: fitted phase shift
as a function of the shift $\psi$ in the electrical excitation.

Several difference images equivalent to the one shown in Fig. 3c, were
recorded while changing the electronic phase by a small amount (typically
15∘). Broad line profiles along the direction of the SAW (blue box in Fig. 4a)
were extracted. For each line profile a background correction is performed by
subtracting the signal averaged over exactly one wavelength in order to
highlight the oscillatory component(s). Please refer to Supp. Mater. II for
details on the data analysis. The line profiles obtained for all phases are
then averaged, after shifting them to account for the corresponding phase
difference of the SAW from the electronic signal. The results are plotted in
Fig. 4b–d and correspond to different data sets at $T=223$ K, $T=233$ K, and
$T=296$ K, respectively. Green lines are best fits with sinusoidal functions,
which is used to obtain the amplitude of the Néel vector (spin axis rotation)
signal. In the insets of Fig. 4b–d, we show as a validation the result for
each single profile, i.e., the fitted experimental phase shift in units of the
wavelength $\lambda$. These values are not used for further analysis, but
their excellent agreement with the electronic phase $\psi$ applied to the IDT
clearly demonstrates that the Néel vector oscillations are driven by SAW.

Temperature | Max. XMLD | SAW orientation | Strain | XMLD wave | Néel vector | Efficiency
---|---|---|---|---|---|---
| Domain Contrast | w.r.t. X-rays | Amplitude | amplitude | rotation |
(K) | ($\times 10^{-3}$) | (degrees) | ($\times 10^{-4}$) | ($\times 10^{-4}$) | (degrees) | (degrees/strain $\times 10^{-4}$)
223 | $7.6\pm 1.0$ | 65 | $0.86\pm 0.05$ | 1.33 | $1.56\pm 0.21$ | $1.81\pm 0.27$
233 | $6.4\pm 1.0$ | 65 | $0.86\pm 0.05$ | 1.29 | $1.80\pm 0.29$ | $2.09\pm 0.36$
296 | $5.3\pm 0.7$ | 90 | $0.75\pm 0.05$ | 2.26 | $2.44\pm 0.33$ | $3.26\pm 0.49$
Table 1: Quantitative results of the Neel vector wave in CuMnAs at different
temperatures.

Now we turn to the quantitative analysis of the Néel vector rotation
amplitudes for the three data sets at different temperatures, i.e., $T=223$ K,
$T=233$ K, and $T=296$ K. The results are summarized in Table 1. Due to
experimental constraints, the low-temperature data was taken with an angle of
65∘ between SAW and probing X-rays, while room temperature data was taken with
a 90∘ angle. The conversion of XMLD amplitude to rotation in degrees as well
as the correction factor for the reduced sensitivity under 65∘ is calculated
in Supp. Mater. III. As mentioned above, the applied strain from the SAW has
been calculated from the voltage shift of the secondary electron spectra
detected in the PEEM (Fig. 2d). The typical strain amplitudes achieved in our
experiments on GaAs are ($0.75-1.5)\times 10^{-4}$. The XMLD wave amplitude
was determined from fits to the averaged line profiles in Fig. 4b–d. In order
to convert those numbers into the corresponding rotation of the spin axis, we
take first into account the temperature dependence of the XMLD contrast in
CuMnAs. The XMLD wave amplitude is thus normalized to the maximum XMLD
contrast for each temperature in the static domain image (See, Fig. 2c for
example), taken as average in three different locations each. We then consider
the film to be populated by an equal portion of domains in all direction,
i.e., without net in-plane anisotropy and calculate for each domain _i)_ the
rotation angle as fraction of a maximum angle $\phi_{0}$ as explained in Supp.
Mater. III and _ii)_ the sensitivity of the XMLD signal to this rotation as
function of the domain spin axis.

Figure 5: Efficiency of the magnetoacoustic wave excitation in CuMnAs as a
function of temperature in comparison with all data at 500 MHz SAW. The area
for Ferromagnetic samples corresponds to Ni and FeSi at room temperature (RT)
and covers the range between zero external field and resonance.

The numbers reported in Table 1 show that the spin axis rotation wave driven
by SAW in CuMnAs can reach a sizable 2.44∘ at room temperature. We plotted in
Fig. 5 the efficiency of the SAW induced Néel vector wave defined as the
overall variation divided by the SAW strain for the three temperatures. A
similar quantity is added into the graph for magnetoacoustic waves in
ferromagnetic samples measured through XMCD [21, 35]. Values for ferromagnetic
Ni [21] and Heusler alloy Fe3Si [35] are shown as broad bands, because they
depend on the external applied magnetic field, showing a resonance like peak
(Nickel showed an efficency of 2 to 4.5 and FeSi from 1.6 to 4.1). These
results indicate a sizable dynamic magneto-elastic effect in CuMnAs induced by
SAW and a comparable efficiency like in FM materials. We notice here that
detection of spin axis rotation in CuMnAs is more challenging compared to
magnetoacoustic waves in ferromagnets mainly because the XMLD contrast is
weaker and because the sample is in a multi-domain state.

A reduction of the Néel vector wave excitation efficiency when lowering
temperature, i.e., from 3.3∘/$10^{-4}$ strain at room temperature (296 K) to
1.8∘/$10^{-4}$ at 223 K has been observed. While this apparent reduction needs
further experiments to verify [36, 37], it may indicate an increase of the
energy barrier for the spin axis rotation as temperature lowers (a type of
“freezing”).

In summary, we have investigated high frequency (500 MHz) magnetoelastic
effects in antiferromagnetic CuMnAs excited by SAW in the GaAs substrate using
XMLD-PEEM. An averaged magneto-acoustic wave signal can be detected in XMLD,
corresponding to a rotation of the spin axis in the individual domains by up
to $\pm 2.4^{\circ}$. The efficiency of the Néel vector excitation in CuMnAs
is a proof that magnetoelastic effects are a viable way to manipulate
antiferromagnetic systems, even on the subnanosecond time scale. Moreover, in
static condition, the CuMnAs thin film is characterized by a multidomain
configuration of submicron size. No SAW-induced motion of domain walls has
been detected, which could be related to intrinsic pinning due the film
microstructure. For the future, a combination of GaP substrates with ZnO based
IDT can enable to study SAW driven effects in high quality single domain
patterns of CuMnAs.

###### Acknowledgements.

The authors are thankful to Spanish Ministry of Science, Innovation,
Universities, and DOC-FAM who have received funding from the European Union’s
Horizon 2020 research and innovation program under the Marie Sklodowska-Curie
grant agreement No. 754397. MWK MF, MAN, and LA acknowledge the funding from
MICINN through grant numbers RTI2018-095303-B-C53 and PID2021-122980OB-C54.
FM, MR, BC, and JMH are grateful to funding from
MCIN/AEI/10.13039/501100011033 through grant number: PID2020-113024GB-100. OA,
KO, KWE, RPC, and PW acknowledge funding from EU FET Open RIA Grant no 766566.
VN is grateful to MEYS Grant No. LM2018110. This work has also been supported
by the ALBA inHouse Research Program.

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