174 30 2021 April mymath  $\BODY$

# LiveStyle - An Application to Transfer Artistic Styles

Amogh G. Warkhandkar
Vidyalankar Institute of Technology
Mumbai
Maharashtra  India
<EMAIL_ADDRESS>
Omkar B. Bhambure
Vidyalankar Institute of Technology
Mumbai
Maharashtra  India
<EMAIL_ADDRESS>

###### Abstract

Art is a variety of human activities that include the production of visual,
auditory, or performing objects that express the creativity, creative
concepts, or technological abilities of the artist, intended primarily for
their beauty or emotional power to be appreciated. The renaissance of historic
and forgotten art has been made possible by modern developments in Artificial
Intelligence. Techniques for Computer Vision have long been related to such
arts. Style Transfer using Neural Networks refers to optimization techniques,
where a content image and a style image are taken and blended such that it
feels like the content image is reconstructed in the style image color
palette. This paper implements the Style Transfer using three different Neural
Networks in form of an application that is accessible to the general
population thereby reviving interest in lost art styles.

###### keywords:

Neural Networks, React, Docker, FastAPI, TensorFlow, Style Transfer

††terms: Artificial Intelligence, Machine Learning, Deep Learning, Image
Processing, Computer Vision, Software Engineering

##  1 Introduction

Art enables humans to express their creativity in various forms like visual,
auditory, sensory, etc. Photos are an essential part of modern human life. The
advancement in computers has paved a way for humans to connect to their roots.
This application 111Source code available at: LiveStyle. aims to explore these
lost arts and re-introduce the styles in modern human civilization. Users can
upload their photos along with a painting and have their photo brought back to
life as if the original artist has created it. Image Style Transfer is a task
that aims to render the content of one image with the style of another, which
is relevant and fascinating for both practical and scientific reasons. In
image processing applications such as mobile camera filters and artistic image
generation, Style Transfer techniques can be widely used. Furthermore, the
study of Style Transfer also shows the intrinsic features of photographs.
Style Transfer is daunting as it is difficult to achieve a clear distinction
of styles concerning the content image as art is subjective.

The flow, Fig. 1 of the application is elementary. The application accepts
content and style images as input from the user. To be able to upload images
smoothly and build a progressive web application, it is built using React [6]
and MaterialUI [5] library. The backend library used in this application is
FastAPI [2]. For version control, Git [3] was utilized, which gave a
contribution to Continuous Integration/Continuous Delivery (CI/CD). To provide
the application in a portable, light-weighted containerized environment,
Docker [1] was used. Docker handles infrastructure management and
significantly reduces the delay between writing code and running it in
production. The interface connecting the frontend and the backend must be
compatible and should be able to support Dynamic Rate Changes and Load
balancing features such as to not crash under heavy load.

Figure 1: Flow Diagram

##  2 Implementation

###  2.1 React (Frontend)

The frontend library used in this application is React. For building user
interfaces, React is a declarative, powerful, and scalable JavaScript library.
React is an open-source, component-based frontend library that is responsible
for the application’s view layer. React uses a declarative model that makes
thinking about the application simpler and strives to be both productive and
versatile. It designs simple views for each state in your application, and
when your data changes, React can efficiently update and make just the right
component. The code is made more predictable and easier to debug by the
declarative view. A React application consists of several modules, each
responsible for rendering a small piece of HTML that can be reused. To allow
complex applications to be built out of simple building blocks, components can
be nested within other components. For routing and managing the pages, the
react-router package was used. We used the component-based structure to store
our files for easier future upgrades and faster code reviews. Each file in the
components folder contains JavaScript code for rendering the elements on the
website.

User Interface and Usability is an important concept while building the
Frontend of an application. This requires proper planning and creative
thinking. The software which is being used to create these User Interface
elements should be carefully examined to suit the needs of the project.

###  2.2 FastAPI (Backend)

The backend library used in this application is FastAPI. FastAPI is a software
framework for the Python creation of web applications. For validating,
serializing, and deserializing data, FastAPI refers to Pydantic and type hints
and automatically generates OpenAPI documents. Asynchronous programming is
fully supported and can it can run with Uvicorn [8] and Gunicorn [4]. Backend
should be able to handle requests from all devices without bottlenecking.

The main.py file contains the logic for the backend. In this file, the
libraries are imported first, followed by defining the routes where the
frontend will send the content and style images. The server will process the
images and return the output image to the origin of the request.

###  2.3 TensorFlow (Deep Learning Library)

TensorFlow [7] is a free and open-source software library for machine
learning. It contains a comprehensive, flexible ecosystem of tools, libraries,
and community resources that enable researchers to advance the state of the
art in ML and that developers can easily create and deploy ML-based
applications. Machine Learning models can be built easily using higher-level
APIs like Keras. Training and Evaluation are supported in the cloud, on-
premise, in the browser, or on the device, regardless of the language being
used. The models were trained on an NVIDIA GTX 1660 Super with 6GB of GDDR6
Memory with CUDA support. The models were then exported as h5 files, which
were then loaded by the server. Models used for the Style Transfer must be
deployed on the backend which can support high-performance computing and
processing.

###  2.4 Model 1: VGG19

VGG-19 [13] is a 19 layer deep convolution neural network. In the
implementation by Gatys et al. [9] a pre-trained version of the network is
loaded which is trained on more than a million images 222ImageNet dataset
available here: ImageNet.. This model can classify images into 1000 object
categories. This model has learned rich feature representations for a wide
range of images. Over the iterations, Content Loss and Style Loss is
calculated at every iteration. These are combined in the equation of Total
Loss which the network tries to minimize.

The functions for Style and Content Loss are as follows -

L_style($\overrightarrow{a}$,$\overrightarrow{x}$ ) = ∑_l = 0^L w_lE_l

L_content($\overrightarrow{p}$,$\overrightarrow{x}$,l) = 12 ∑_i, j^ (F_ij^l -
P_ij^l)^2

###  2.5 Model 2: Inception-v3 + MobileNet-v2

Inception-v3 [14] is a 48 layer deep convolution neural network. This model
can also classify images into 1000 object categories. This model is made up of
symmetric and asymmetric building blocks. In the implementation by Ghiasi et
al. [10] used a pre-trained MobileNet-v2 [12] model to distill the trained
Inception-v3 model which allows the model to run on low computing devices.

AST utilizes a Style Transfer network that can map from content image to
stylized image. The specific style is chosen by providing the transfer network
with a style-specific set of normalization parameters. These parameters are
generated for any arbitrary painting via a separate neural network called the
style prediction network. The style-specific parameters can be taken as a
latent space of style, and we can “combine” different styles by taking the
weighted average between style representations of multiple styles. AST allows
interpolation between the style representation of arbitrary styles, and so
lets us do something like control the stylization strength. This is done by
calculating the style representation of both the content image and the style
image and taking their weighted average.

The functions for Style and Content loss are as follows -

L_style(x, s) = ∑_iϵs^ 1ni ∥ G[f_i(x)] - G[f_i(s)] ∥ ^2_F

L_content(x, c) = ∑_jϵc^ 1nj ∥ f_i(j)- f_j(s)∥ ^2_2

###  2.6 Model 3: CycleGAN

The generator tries to produce samples from the desired distribution and the
discriminator tries to predict if the sample is from the actual distribution
or produced by the generator. The benefit of the CycleGAN [16] model is that
it can be trained without paired examples. It does not require examples of
photographs before and after the translation to train the model, e.g. image of
the dog and a painting of the same dog in an artist’s style. Instead, the
model can use a collection of images from each domain and extract and harness
the underlying style of images in the collection to perform the translation.

The loss function of this model is composed of two parts: the Adversarial Loss
L(adv) and the Content Loss L(con). The content loss is used to constrain
semantic content between the input images and output images. The
hyperparameter lambda is used to control how much input image content is
retained in the image stylization. Its value varies with the specific Style
Transfer.

This model tries to convert from Domain X to Domain Y using adversarial loss.
As this mapping is highly under-constrained, it is coupled with an inverse
mapping and a cycle consistency loss is introduced.

The loss function in this implementation is as follows -

L(G, D) = L_adv(G,D) + λL_con(G,D)

##  3 Observations

###  3.1 User Interface

Figure 2: Dashboard

The Dashboard Page, Fig. 2 features two buttons that the user can click. This
opens the native file picker of the Operating System being used. After
selecting the content and style image the user can click the “Start Magic”
Button. This sends a GET request to the backend. The server processes the two
images and returns the output of the models. This output is then displayed on
the frontend.

Figure 3: Resources

The Resources Page, Fig. 3 displays the details of the models being used in
this application.

Figure 4: About

The About Page, Fig. 4 contains details of the application and the technology
stack being used.

###  3.2 Generated Images

Figure 5: Generated Images

Fig. 5 indicates the style and content image used for comparative and
qualitative analysis of the models. The style images are the paintings of
various artists. For the demonstration of the working of the application,
images of animals were taken as content images. This figure displays the
output images generated by the models for comparison. As there is no objective
metric to judge an artwork, one can only compare the output image with the
input images. Table 3.2 shows the quality of generated images in the form of
rankings with rank 1 being the best in the mentioned comparison metric.

Subjective Ranking of Generated Images  Model Sharpness Noise Color Accuracy
Distortion VGG19 2 2 3 1 Inception-v3 1 1 2 3 CycleGAN 3 3 1 2

##  4 Conclusions and Future Work

React allows us to build the frontend and user interface elements easily.
FastAPI helps us construct an endpoint where it is possible to host TensorFlow
models. Git enables the management of files and keeping track of software
versions. Docker is a great tool for containerizing the dependencies needed to
compile, build, run, and test isolated program instances.

Thus, for future work, it would be interesting to find efficient methods of
performing Style Transfer that can provide faster results. Users would be
offered more customization options for the generated image. The Style Transfer
application can be also extended to support live video feed.

## References

  * [1] Docker - a set of platform as a service products that use os-level virtualization to deliver software in packages called containers. https://www.docker.com/. Accessed: 2020-03-11.
  * [2] Fastapi - a modern, fast (high-performance), web framework for building apis with python 3.6+ based on standard python type hints. https://fastapi.tiangolo.com/. Accessed: 2020-03-11.
  * [3] Git - a free and open source distributed version control system. https://git-scm.com/. Accessed: 2020-03-11.
  * [4] Gunicorn - a python web server gateway interface http server. https://gunicorn.org/. Accessed: 2020-03-11.
  * [5] Material ui - a popular react ui framework. https://material-ui.com/. Accessed: 2020-03-11.
  * [6] React - a javascript library for building user interfaces. https://reactjs.org/. Accessed: 2020-03-11.
  * [7] Tensorflow - a free and open-source software library for machine learning. https://www.tensorflow.org/. Accessed: 2020-03-11.
  * [8] Uvicorn - a lightning-fast asgi server implementation, using uvloop and httptools. https://www.uvicorn.org/. Accessed: 2020-03-11.
  * [9] Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge. A neural algorithm of artistic style. CoRR, abs/1508.06576, 2015.
  * [10] Golnaz Ghiasi, Honglak Lee, Manjunath Kudlur, Vincent Dumoulin, and Jonathon Shlens. Exploring the structure of a real-time, arbitrary neural artistic stylization network. CoRR, abs/1705.06830, 2017.
  * [11] C. Hu, Y. Ding, and Y. Li. Image style transfer based on generative adversarial network. In 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), volume 1, pages 2098–2102, 2020.
  * [12] Mark Sandler, Andrew G. Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen. Inverted residuals and linear bottlenecks: Mobile networks for classification, detection and segmentation. CoRR, abs/1801.04381, 2018.
  * [13] Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition, 2015.
  * [14] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. CoRR, abs/1512.00567, 2015.
  * [15] Amogh G. Warkhandkar, Baasit Sharief, and Omkar B. Bhambure. Measuring performance of generative adversarial networks on devanagari script. International Journal of Computer Applications, 176(33):5–9, Jun 2020.
  * [16] Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A. Efros. Unpaired image-to-image translation using cycle-consistent adversarial networks. CoRR, abs/1703.10593, 2017.


# Rare top quark production and decays at ATLAS and CMS

K. Skovpen (on behalf of the ATLAS and CMS Collaborations)  IIHE - VUB,
Pleinlaan 2, 1050 Brussels, Belgium

###### Abstract

The most recent studies in the top quark sector are reviewed with the focus on
the rare production mechanisms and suppressed decays. The experimental results
obtained with the ATLAS and CMS detectors in proton-proton collisions at the
center-of-mass energy of 13 TeV include the measurements of the associated
production of top quark pairs with vector bosons (${\rm t\bar{t}W}$, ${\rm
t\bar{t}Z}$, ${\rm t\bar{t}\gamma}$), the first evidence for the ${\rm
t(\bar{t})\gamma q}$ process, the first observation of the ${\rm
t(\bar{t})Zq}$ production, the study of the ${\rm t\bar{t}+b\bar{b}}$ and
${\rm t\bar{t}+t\bar{t}}$ processes, as well as searches for lepton flavour
violation in top quark decays and effective field theory interpretations. The
experimental results show good agreement with the theoretical predictions.

††© 2019 CERN for the benefit of the ATLAS and CMS Collaborations. CC-BY-4.0
license.

##  1 Introduction

The top quark takes an important place in the standard model (SM). It’s mass
and distinctive experimental decay signature in the experiment makes it
possible to study a number of very rare production mechanisms, as well as
extremely rare decay modes. Top quarks produced in pairs, as well as singly
produced particles, were already observed at the LHC. The rare production of
top quarks with vector bosons and additional quarks is associated with small
cross sections and is also challenging due to complex final states. Study of
these rare processes allows us to probe interactions of the top quark with
other SM particles and to search for possible anomalous phenomena.

##  2 Study of associated production of top quark pairs with vector bosons

The study of the top quark pair production (${\rm t\bar{t}}$) in association
with a W or Z boson is important because these topologies can receive sizeable
contributions from new physics, and, in addition, the ${\rm t\bar{t}Z}$
production represents the main channel to directly measure the top quark
couplings to the Z boson. Moreover, the precise measurements of the production
cross sections of these processes are essential for the study of the ${\rm
t\bar{t}H}$ production where the ${\rm t\bar{t}W}$ and ${\rm t\bar{t}Z}$
events represent one of the dominant backgrounds in the multilepton analysis
channels.

The study of the ${\rm t\bar{t}W}$ production at ATLAS [1] is done in the
dilepton same-sign and trilepton channels, while the ${\rm t\bar{t}Z}$ process
is looked for in the dilepton opposite-sign, trilepton and four-lepton final
states [3]. In the analysis of the ${\rm t\bar{t}Z}$ process using dilepton
opposite-sign final states the prompt lepton background originates from ${\rm
Z}$+jets and ${\rm t\bar{t}}$ events, while these processes represent a non-
prompt lepton background in the other channels, also for the case of ${\rm
t\bar{t}W}$. The prompt lepton background is additionally associated with the
diboson production and is one of the dominant backgrounds along with the non-
prompt leptons. The analysis proceeds with defining control and signal regions
with multiple exclusive event categories based on the number of leptons split
into different flavour and sign with an additional selection based on the
number of jets and b-tagged jets.

The analysis of the ${\rm t\bar{t}Z}$ production allows to perform an
effective field theory (EFT) study of anomalous contributions to the ${\rm
t\bar{t}Z}$ vertex with obtaining constraints on the Wilson coefficients of
the respective dimension-six operators. In such interpretation the ${\rm
t\bar{t}Z}$ event rate can be expressed as a quadratic function of the Wilson
coefficients where the linear terms results from the interference between
beyond the SM (BSM) and SM operators. The fits to the measured distributions
are done to obtain the EFT constrains in the case when both the quadratic and
linear terms are kept, as well as when the quadratic terms are omitted. The
obtained constraints represent competitive results to the existing direct and
indirect limits. The inclusive ${\rm t\bar{t}Z}$ and ${\rm t\bar{t}W}$ cross
sections are measured with $\simeq$ 10% and $\simeq$ 20% precision,
respectively, and are comparable to next-to-leading order (NLO) theoretical
uncertainties, as shown in Fig. 1.

Figure 1: The results of the simultaneous fit to the ${\rm t\bar{t}Z}$ and
${\rm t\bar{t}W}$ cross sections with the 68% and 95% confidence level
contours compared to the NLO theoretical predictions [3].

The analysis at CMS [2] studies final states with two same-sign leptons for
the ${\rm t\bar{t}W}$ process, while the ${\rm t\bar{t}Z}$ production is
looked for in the trilepton and four lepton final states [4]. The prompt and
non-prompt lepton backgrounds are validated in control regions in data. The
analysis uses an improved multivariate-analysis-based (MVA) lepton
identification with respect to the previous iterations of these studies. The
${\rm t\bar{t}W}$ and ${\rm t\bar{t}Z}$ cross sections are extracted from
combined fit over several exclusive event categories defined by the final MVA-
based discriminant and the total number of jets and b-tagged jets. The
measured inclusive cross sections show good agreement with NLO predictions.

By including more data it becomes possible to probe differential cross
sections of the ${\rm t\bar{t}Z}$ production. The study done at CMS measures
differential distributions of the ${\rm t\bar{t}Z}$ cross section using
kinematic variables sensitive to ${\rm t-Z}$ anomalous interactions [5]. The
measured cross sections are interpreted in two frameworks. The first approach
uses an anomalous-coupling Lagrangian based on the neutral vector and axial
vector current couplings, as well as the weak magnetic and electric dipole
interaction couplings. The second interpretation is EFT-based which considers
four dimension-six operators which induce electroweak dipole moments and
anomalous neutral-current interactions. The ${\rm t\bar{t}Z}$ inclusive cross
section in this recent analysis is now measured with an improved precision of
$\simeq$ 10%. Some measured differential cross sections are presented in Fig.
2.

Figure 2: Comparison between data and MC prediction for differential ${\rm
t\bar{t}Z}$ cross sections as a function of the transverse momentum of the Z
boson (left) and the cosine of the angle between the Z boson and the
negatively charged lepton from the Z boson decay in the Z boson rest frame
(right) [5]. The hatched band includes the theory uncertainties in the
prediction.

The analysis of the ${\rm t\bar{t}\gamma}$ production represents an important
study of the top-photon electroweak couplings where the kinematic
distributions of the radiated photon, such as transverse momentum, are
especially sensitive to new physics contributions. The measurement of the
${\rm t\bar{t}\gamma}$ differential cross section also provides an important
information on the ${\rm t\bar{t}}$ spin correlations and charge asymmetry,
and is complementary to the other ${\rm t\bar{t}}$ measurements. The ${\rm
t\bar{t}\gamma}$ process is studied at ATLAS in the channels with one or two
leptons and the results of the differential measurements are compared to
leading-order and NLO predictions [6]. The previously observed disagreements
in the large values in the distribution of the azimuthal angular difference
between the two leptons are now significantly mitigated with moving to the NLO
event generation.

##  3 Study of single top quark associated production with vector bosons

The associated production of a top quark with a photon (${\rm t(\bar{t})\gamma
q}$) is an important process which is sensitive to the charge, as well as the
electric and magnetic moments of the top quark. The search for this process is
done at CMS in the t-channel considering the final state with one muon, one
photon, one b-tagged jet and one forward jet [7].

Figure 3: The boosted decision tree output distribution for data and SM
predictions after the fit in the analysis of the ${\rm t(\bar{t})\gamma q}$
process [7].

The presence of the forward light flavour energetic jet is a very
characteristic signature of the single top quark associated production with
vector bosons. The dominant background includes the ${\rm t\bar{t}\gamma}$
production, among other contributions. The analysis uses a boosted decision
tree-based discriminator to suppress various backgrounds, as shown in Fig. 3.
This study resulted in the first evidence for this process at 4.4 (3.0)
$\sigma$ observed (expected).

The production of a top quark in association with a Z boson (${\rm
t(\bar{t})Zq}$) is sensitive to anomalous ${\rm WWZ}$ triple-gauge and ${\rm
tZ}$ couplings. The analysis of this production at CMS was done in the final
state with three leptons [8]. This study uses an improved lepton
identification which allowed to boost the final sensitivity in this search. A
simultaneous fit is performed over several event categories to extract the
signal with the sensitivity of 8.2 (7.7) $\sigma$ observed (expected) leading
to the first observation of this process. Comparisons after the final event
selection criteria between data and predictions are shown in Fig. 4.

Figure 4: Comparison between the number of expected and observed events for
the distributions of the recoiling jet $|\eta|$ (left) and the transverse
momentum of the ${\rm Z}$ boson after the additional selection on the final
discriminant (right) [8].

##  4 Study of top quark pair production in association with heavy flavour
quarks

The production of ${\rm t\bar{t}}$ with additional jets is associated with
large theoretical uncertainties due to the presence of two different scales of
the top quark mass and the jet transverse momentum. The measurements of the
associated production of top quark pairs with b quarks (${\rm
t\bar{t}+b\bar{b}}$) are done at ATLAS in single lepton and dilepton final
states [9]. This study is an important test of QCD predictions with providing
a better estimation of one of the main backgrounds in the ${\rm
t\bar{t}H(H\rightarrow b\bar{b})}$ analysis. The ${\rm t\bar{t}+b\bar{b}}$
component is extracted from data using MC templates defined by the flavour of
additional quark-jets. The measured inclusive fiducial cross sections
generally exceed the ${\rm t\bar{t}+b\bar{b}}$ NLO predictions but are still
compatible within the total uncertainties. The inclusive cross section is
measured with precision of $\simeq$ 20% and is better than in the theoretical
calculations. The measured cross sections in fidual region is presented in
Fig. 5.

Figure 5: The measured fiducial cross sections, with ${\rm t\bar{t}H}$ and
${\rm t\bar{t}V(V=W,Z,\gamma)}$ contributions subtracted from data, compared
with ${\rm t\bar{t}+b\bar{b}}$ predictions [9].

Another rare process that allows to study the QCD predictions is the four top
quark production that is also sensitive to the top quark Yukawa coupling. The
search for this process at ATLAS is done in single lepton and dilepton
opposite-sign channels [10]. Events are categorised based on the number of
jets and b-tagged jets. There are several validation regions defined to
perform the measurement of the b tagging efficiencies adapted to the topology
of these events and to extrapolate it to the signal regions. The results are
combined with the previously published dilepton same-sign and multilepton
results. This combination has resulted in the exclusion of the cross section
of ${\rm t\bar{t}+t\bar{t}}$ production down to $\simeq$ $5\times$ the
predicted value at 2.8 (1.0) $\sigma$ observed (expected), with the final
limits presented in Fig. 6. The analysis also includes an EFT interpretation
optimised for four-top contact interactions.

Figure 6: Summary of the 95% confidence level limits on the ${\rm
t\bar{t}+t\bar{t}}$ production relative to the SM prediction in the individual
channels and for the combination [10].

The search for the ${\rm t\bar{t}+t\bar{t}}$ production in similar final
states is also done at CMS [11]. Several event categories are defined based on
the number of reconstructed jets and b-tagged jets used in a simultaneous fit
to extract the signal. A combination with dilepton same-sign and trilepton
results is also performed. The EFT interpretation was done for four-fermion
operators which contribute to the ${\rm t\bar{t}+t\bar{t}}$ production. The
combined sensitivity to the ${\rm t\bar{t}+t\bar{t}}$ production reaches 1.4
(1.1) $\sigma$ observed (expected).

##  5 Effective field theory study in dilepton events

The EFT interpretations are becoming an essential part of many analysis
studying top quarks. The full EFT at NLO interpretation is done for the top
quark production in the dilepton final state at CMS, which mainly includes top
quarks produced in ${\rm t\bar{t}}$ and ${\rm t(\bar{t})W}$ processes [12].
There are several types of operators which contribute to the production of
these events, including operators associated with the ${\rm Wtb}$ couplings,
chromomagnetic dipole moment, triple gluon field and flavour-changing neutral
currents. The interference between the ${\rm t\bar{t}}$ and ${\rm
t(\bar{t})W}$ processes is removed. The EFT constrains are set through the fit
of a Neural Network discriminants trained to distinguish between the EFT
contributions and the SM prediction.

##  6 Search for lepton-flavour violation

In addition to the rare top quark production, one can also search for rare
decays of these particles. One such analysis is done at ATLAS to search for
the charged lepton-flavour violation (LFV) with the model-independent approach
in three-particle decays of top quarks [13]. The LFV decays of top quarks are
extremely suppressed in the SM and any deviations from these zero rates would
indicate the presence of new physics. The analysis is based on the study of
the three-lepton final state to set limits several EFT operators, including
the axial-vector, scalar, pseudo-scalar and lepton-quark interactions. The
probability of observing LFV top quark decays is excluded down to the $\simeq$
$10^{-5}$ level and this constraint is more stringent than the current
indirect limits set at $\simeq$ $10^{-3}$.

##  7 Conclusion

The experiments done with the ATLAS and CMS detectors at the LHC provide us
with a great opportunity to study very rare processes with top quarks.
Recently, we have observed for the first time the process with the production
of single top quarks in association with a Z boson, as well as have obtained
the first evidence for the single top quark production with a photon. The
study of the underlying physics in these processes and searches for BSM
phenomena will proceed with even more data in the coming years.

## References

## References

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  * [4] CMS Collaboration, JHEP 08, 011 (2018).
  * [5] CMS Collaboration, CMS-PAS-TOP-18-009, https://cds.cern.ch/record/2666205, 2019.
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# Nanoscale magnets embedded in a microstrip

Raphael Pachlatko  Laboratory for Solid State Physics, ETH Zurich, CH-8093
Zurich, Switzerland.  Nils Prumbaum  Laboratory for Solid State Physics, ETH
Zurich, CH-8093 Zurich, Switzerland.  Marc-Dominik Krass  Laboratory for Solid
State Physics, ETH Zurich, CH-8093 Zurich, Switzerland.  present address:
attocube systems AG, 85540 Haar, Germany  Urs Grob  Laboratory for Solid State
Physics, ETH Zurich, CH-8093 Zurich, Switzerland.  Christian L. Degen
Laboratory for Solid State Physics, ETH Zurich, CH-8093 Zurich, Switzerland.
Alexander Eichler  Laboratory for Solid State Physics, ETH Zurich, CH-8093
Zurich, Switzerland.

###### Abstract

Nanoscale magnetic resonance imaging (NanoMRI) is an active area of applied
research with potential use in structural biology and quantum engineering. The
success of this technological vision hinges on improving the instrument’s
sensitivity and functionality. A particular challenge is the optimization of
the magnetic field gradient required for spatial encoding, and of the radio-
frequency field used for spin control, in analogy to the components used in
clinical MRI. In this work, we present the fabrication and characterization of
a magnet-in-microstrip device that yields a compact form factor for both
elements. We find that our design leads to a number of advantages, among them
a fourfold increase of the magnetic field gradient compared to those achieved
with traditional fabrication methods. Our results can be useful for boosting
the efficiency of a variety of different experimental arrangements and
detection principles in the field of NanoMRI.

A growing community of scientists is working towards nanoscale magnetic
resonance imaging (NanoMRI) utilizing nanomechanical sensors Sidles (1991);
Rugar  _et al._ (2004); Degen  _et al._ (2009); Poggio and Degen (2010);
Vinante  _et al._ (2011); Nichol  _et al._ (2013); Haas  _et al._ (2022),
nitrogen-vacancy centers in diamond Ajoy  _et al._ (2015); Kost  _et al._
(2015); Shi  _et al._ (2015); Lovchinsky  _et al._ (2016), spin-dependent
tunnelling Baumann  _et al._ (2015); Willke  _et al._ (2018); Seifert  _et
al._ (2020); Chen  _et al._ (2022), or superconducting resonators Eichler  _et
al._ (2017); Bienfait  _et al._ (2016); Probst  _et al._ (2017). The vision of
NanoMRI is to enable three-dimensional, nondestructive imaging of individual
molecules or solid-state samples with atomic or near-atomic spatial resolution
Poggio  _et al._ (2007); Degen  _et al._ (2009); Grob  _et al._ (2019). Once
this milestone is reached, many useful applications in structural biology or
the design of quantum devices are within reach. However, detecting small
ensembles of nuclear spins (e.g., hydrogen atoms) across distances of tens or
hundreds of nanometers is an enormous challenge due to the small signal
generated by each nuclear spin. The community is therefore actively seeking to
optimize their measurement approaches.

Figure 1: Fabrication process for embedded nanomagnets: (a) FeCo alloy
($260\text{\,}\mathrm{nm}\text{/}$) is evaporated on a Si wafer, followed by a
spacer layer of Cr ($130\text{\,}\mathrm{nm}\text{/}$) and a SiN hardmask
($550\text{\,}\mathrm{nm}\text{/}$). (b) The SiN film is etched into
nanopillars using negative e-beam resist and reactive ion etching (RIE). (c)
Using the SiN nanopillars as hardmasks, the Cr and FeCo films are milled away
in a variable-angle ion milling step. (d) A Au film is evaporated and the
surface is milled at a glancing angle. (e) The Cr spacer is removed using Cr
etch, followed by iterative polishing steps using glancing angle ion milling
and additional Au layer evaporations. (f) The SiN hardmask steps (a) through
(c) are repeated to shape the microstrip from the Au film. The final
$15\text{\,}\mathrm{nm}\text{/}$ thick topping layer of Ti/Au is partially
shown for visibility purposes. Figure 2: Experimental arrangement. (a) To-
scale illustration of the cantilever (blue) and microstrip (yellow) assembly.
An influenza virus (pink) is fixated to a Si nanorod at the cantilever end and
positioned roughly $100\text{\,}\mathrm{nm}\text{/}$ above the nanomagnet
(red). The inset shows a zoom of the nanorod tip region marked by a white
square. (b) Scanning electron micrograph of magnet-in-microstrip device. The
dotted circle denotes its position within the gold surface. (c) Scanning
electron micrograph of a traditional magnet-on-microstrip device, consisting
of a FeCo nanomagnet in a truncated cone shape on top of a Au microstrip.

NanoMRI relies on the simultaneous application of strong magnetic field
gradients for spatial resolution and radio-frequency fields for coherent spin
control. Field gradients can be generated by means of nanoscale electromagnets
or permanent magnets Rugar  _et al._ (2004); Nichol  _et al._ (2012);
Longenecker  _et al._ (2012); Mamin  _et al._ (2012); Grob  _et al._ (2019),
while current-carrying elements like microstrips are used to produce RF fields
for spin manipulation Poggio  _et al._ (2007); Sasaki  _et al._ (2016). For
optimal signal generation, both components (field gradient source and
microstrip) should be placed as close to the sample as possible. This
geometrical optimization can be hard to achieve in a scanning probe setup,
especially when the instrument design is constrained by the necessity of
working in vacuum, at low temperatures, or in large external magnetic fields.

In this paper, we demonstrate on-chip integration of a nanoscale field
gradient source and a microstrip resulting in a compact geometry. By embedding
the nanomagnet within a gold (Au) microstrip, we can place an influenza virus
test sample within tens of nanometers from both elements, enabling optimal
static and RF field strengths. We test the performance of our magnet-in-
microstrip device in a force-detected NanoMRI setup and find that the field
gradient in $x$-direction, the direction of the cantilever oscillation, is
boosted by a factor of $\sim$4 relative to magnet-on-microstrip devices Grob
_et al._ (2019). As a consequence, we detect nuclear spin ensembles within an
averaging time of ten seconds, roughly one order of magnitude faster than in
previous work Krass  _et al._ (2022). We further demonstrate a best-effort
spatial resolution below $1\text{\,}\mathrm{nm}\text{/}$ in the direction of
oscillation. Our geometry offers a general pathway to improving the signal
acquisition in force-detected NanoMRI.

The process to create a microstrip with embedded nanomagnets is summarized in
Fig. 1 and explained in detail in the the appendix. In short, the fabrication
follows a top-down approach with a silicon nitride (SiN) hardmask to pattern a
FeCo layer (FeCo, 65$\%$/35$\%$ at.wt.) into the desired cylinder shape. In a
second step, a Au layer is applied, the hardmask is removed, and the
nanomagnet and Au film are ion milled at a glancing angle to create a planar
surface (typical root-mean-square variations on the order of
$7\text{\,}\mathrm{nm}\text{/}$) Yoo  _et al._ (2018). In a third step, the Au
layer is patterned into a microstrip using a second hardmask. Finally, the
microstrip and nanomagnet are covered with an additional thin Ti/Au layer
($15\text{\,}\mathrm{nm}\text{/}$) to minimize electrostatic work function
differences across the surface.

To test the functionality of the embedded nanomagnets, we mount them in a
magnetic resonance force microscopy (MRFM) setup Degen  _et al._ (2009);
Poggio and Degen (2010); Krass  _et al._ (2022). Our setup is suspended in a
vacuum chamber inside a liquid helium bath cryostat, with a base temperature
of $T=$4.7\text{\,}\mathrm{K}\text{/}$$ and a pressure of
$p=$1\text{\times}{10}^{-7}\text{\,}\mathrm{mbar}\text{/}$$. The force sensor
is an in-house fabricated silicon cantilever, see Fig. 2(a) Mamin and Rugar
(2001); Moores  _et al._ (2015); Grob  _et al._ (2019); Krass  _et al._
(2022). The cantilever has dimensions $150\text{\,}\mathrm{\SIUnitSymbolMicro
m}\text{/}$ $\times$ $4\text{\,}\mathrm{\SIUnitSymbolMicro m}\text{/}$
$\times$ $130\text{\,}\mathrm{nm}\text{/}$, a free resonance frequency
$f_{0}=$3500\text{\,}\mathrm{Hz}\text{/}$$, a mass
$m=$100\text{\,}\mathrm{pg}\text{/}$$, and a quality factor $Q=25000$ at
cryogenic temperatures around $4\text{\,}\mathrm{K}\text{/}$. These properties
result in an intrinsic spring constant of
$k_{0}=$50\text{\,}\mathrm{\SIUnitSymbolMicro N}\text{/}\mathrm{m}$$ and a
damping coefficient of
$\gamma_{0}=$8.8\text{\times}{10}^{-14}\text{\,}\mathrm{kg}\text{/}\mathrm{s}$$.
Sensor readout is achieved using a fiber-optic interferometer Rugar  _et al._
(1989); Héritier  _et al._ (2018). By analyzing the AC and DC components of
the reflected light, we extract the cantilever frequency and static
deflection, respectively. The laser temperature is stabilized to within
$10\text{\,}\mathrm{mK}\text{/}$ to reduce signal drift Krass  _et al._
(2022). Samples are attached to the end of the cantilever via a silicon
nanorod Krass  _et al._ (2022); Overweg  _et al._ (2015) and then brought into
close proximity with the nanomagnet on the chip. A SEM micrograph of the
magnet-in-microstrip device, which forms the main advance of this work, is
shown in Fig. 2(b) together with a traditional magnet-on-microstrip device,
shown in Fig. 2(c).

Figure 3: Device characterization using MRFM. (a) Simulated spatial
distribution of magnetic isolines in a vertical cut through the magnet center.
Colored lines indicate positions with constant Larmor frequency
$f_{\mathrm{L}}$, separated by $1\text{\,}\mathrm{MHz}\text{/}$ each. The
shaded area corresponds to a resonant slice between
$f_{\mathrm{L}}=$256.5\text{\,}\mathrm{MHz}\text{/}$$ and
$254.5\text{\,}\mathrm{MHz}\text{/}$. The grey raster lines are separated by
$50\text{\,}\mathrm{nm}\text{/}$. The solid black line indicates the standoff
height $h$ at which the magnetization of the nanomagnet was determined in (e).
The tip-magnet distance is equal to $h+d$ with
$d=$15\text{\,}\mathrm{nm}\text{/}$$. (b) Two-dimensional MRFM scan at
$h=$100\text{\,}\mathrm{nm}\text{/}$$ above the surface with an integration
time of $60\text{\,}\mathrm{s}\text{/}$, a center frequency of
$245\text{\,}\mathrm{MHz}\text{/}$, and a frequency deviation of
$800\text{\,}\mathrm{kHz}\text{/}$ (half peak-to-peak frequency chirp range).
The colorscale ranges from 0 to $1500\text{\,}\mathrm{a}\mathrm{N}^{2}$ with
higher values capped to improve the visibility of the signal regions. (c) One-
dimensional MRFM scan measured at $h=$50\text{\,}\mathrm{nm}\text{/}$$ with
identical parameters to (b). The signal rise is fitted to a hyperbolic tangent
step function. (d) One-dimensional MRFM scans measured at
$h=$20\text{\,}\mathrm{nm}\text{/}$$ with $60\text{\,}\mathrm{s}\text{/}$
integration time and $1\text{\,}\mathrm{MHz}\text{/}$ pulse bandwidth. The
center frequencies shown are $245\text{\,}\mathrm{MHz}\text{/}$ at zero
inversion bandwidth (open blue points), $245\text{\,}\mathrm{MHz}\text{/}$ at
$1\text{\,}\mathrm{MHz}\text{/}$ inversion bandwidth (red), and
$248\text{\,}\mathrm{MHz}\text{/}$ at $1\text{\,}\mathrm{MHz}\text{/}$
inversion bandwidth (yellow). Black squares mark the positions where the
signal reaches $50\text{\,}\mathrm{\char 37\relax}\text{/}$ of its maximal
value for each center frequency. (e) Static MRFM spectrum measured at
$h=$100\text{\,}\mathrm{nm}\text{/}$$, shown as a black line in (a). The
signal integration time was $600\text{\,}\mathrm{s}\text{/}$ with a standard
inversion bandwidth of $1\text{\,}\mathrm{MHz}\text{/}$.  Figure 4: Direct
comparison of magnet-in-microstrip (a-c) and magnet-on-microstrip (d-f)
devices. Both scans image an identical virus sample Krass  _et al._ (2022).
(a) Two-dimensional MRFM measurement recorded with the magnet-in-microstrip
device with an integration time of $10\text{\,}\mathrm{s}\text{/}$ and a
$h=$100\text{\,}\mathrm{nm}\text{/}$$. The colorscale ranges from 0 to
$1000\text{\,}\mathrm{a}\mathrm{N}^{2}$ with higher values capped to improve
the visibility of the signal regions. An embedded nanomagnet with a diameter
of ca. $500\text{\,}\mathrm{nm}\text{/}$ was used for this dataset. (b)
Signal-to-noise ratio (SNR) for the data shown in (a). The colorscale ranges
from 1 (bright) to 8 (dark) with higher SNR values capped to illustrate signal
shells characteristic for this nanomagnet shape. (c) Approach trajectories of
the cantilever towards the magnet-in-microstrip sample. The blue shape
indicates the surface topography as determined in a separate atomic force
microscopy scan. Approximately vertical lines correspond to individual
trajectories starting from the top and terminating when either the frequency
shift of amplitude drop triggers a stop condition. The color scale of the
trajectories indicates the measured frequency, ranging from
$3\text{\,}\mathrm{kHz}\text{/}$ (dark blue) to
$9\text{\,}\mathrm{kHz}\text{/}$ (yellow). (d) Two-dimensional MRFM
measurement recorded with a magnet-on-microstrip device Krass  _et al._ (2022)
with a signal integration time of $60\text{\,}\mathrm{s}\text{/}$ and
$h=$100\text{\,}\mathrm{nm}\text{/}$$. The colorscale ranges from 0 to
$500\text{\,}\mathrm{a}\mathrm{N}^{2}$ with higher values capped to improve
the visibility of the signal regions. The cantilever and influenza sample are
identical to the ones used in this work. (e) SNR for the data shown in (d).
The colorscale is identical to the one in (b). (f) Approach trajectories of
the cantilever towards the magnet-on-microstrip sample, analogous to (c).

In MRFM, the magnetic field gradient $G_{x}=\partial B_{z}/\partial x$ in the
direction of the mechanical sensor displacement $x$ serves two purposes: (i)
it enables spatial selectivity, and (ii) it transduces a spin’s
$z$-magnetization $\mu_{z}$ into a force $F_{x}=\mu_{z}G_{x}$ that is measured
with the mechanical sensor (i.e., the cantilever). Coherent inversion of the
spins with a rate equal to $2f_{0}$ leads to a force at $f_{0}$, thereby
driving the cantilever into resonant oscillation and maximizing the readout
efficiency. We use rapid adiabatic passage pulses with hyperbolic secant
modulation for high-fidelity spin inversions Grob  _et al._ (2019); Tannús and
Garwood (1996). The pulse bandwidth is tuned to invert spins within a selected
narrow range of Larmor frequencies centered around a carrier frequency
referred to as “center frequency”, which translates into a “resonant slice” in
real space, cf. Fig. 3(a). The sharpness of the pulse window function, i.e.,
the distance over which the pulse inversion efficiency drops from “on” to
“off”, determines the spatial resolution Degen  _et al._ (2009); Grob  _et
al._ (2019). A static magnetic field $B_{0}=$5.6\text{\,}\mathrm{T}\text{/}$$
applied in $z$ direction serves to magnetize the nanomagnet and to provide a
quantization axis for the spins.

In our first experiment, we scan the influenza sample over the embedded
nanomagnet at a constant height and invert the proton spins (1H nuclei)
contained therein in a Larmor frequency range of
$244\text{\,}\mathrm{MHz}\text{/}$ to $246\text{\,}\mathrm{MHz}\text{/}$, cf.
Fig. 3(a). Measuring the force generated by the inverted spins in the sample,
we obtain the double crescent shape characteristic for this protocol Degen
_et al._ (2009), cf. Fig. 3(b). The dependence of the signal strength on the
scan position defines a one-dimensional spatial resolution Grob  _et al._
(2019). In Fig. 3(c), the maximum slope of the hyperbolic tangent function is
compared to the standard variation
$\sigma=$47.3\text{\,}{\mathrm{aN}}^{2}\text{/}$$, yielding a resolution of
$0.91\text{\,}\mathrm{nm}\text{/}$. This value is on par with the value
reported previously with a magnet-on-microstrip device in a carefully
optimized setting Grob  _et al._ (2019).

In a next step, we characterize $G_{x}$ by testing that the signal from
different resonant slices corresponds to different spatial regions. In Fig.
3(d), we plot the results of two lateral scans with center frequencies
differing by $\Delta{f}=$3\text{\,}\mathrm{MHz}\text{/}$$. We find that the
two slices are shifted by a distance of
$\Delta{x}=$32\text{\,}\mathrm{nm}\text{/}$$, corresponding to
$G_{x}=\frac{\Delta{f}}{\gamma\Delta{x}}=$2.1\text{\,}\mathrm{MT}\text{/}\mathrm{m}$$,
where $\gamma=$42.58\text{\,}\mathrm{MHz}\text{/}\mathrm{T}$$ is the
gyromagnetic ratio of protons. This is roughly four times larger than the best
value ($G_{x}=$0.56\text{\,}\mathrm{MT}\text{/}\mathrm{m}$$) we measured with
the traditional magnet-on-microstrip design in our setup, which are evaporated
through resist masks Grob  _et al._ (2019). (Note that the gradient can be
further increased by using other materials Longenecker  _et al._ (2012); Mamin
_et al._ (2012).) We ascribe the improvement in $G_{x}$ to the sharper magnet
edge and steeper magnet sidewalls achieved through the ion-milling-based
fabrication process.

The magnetization of the nanomagnet is estimated from the signal onset at the
high-frequency edge of the slice Mamin  _et al._ (2007). First, we perform
scans at a fixed height above the nanomagnet. This scan allows us to determine
the center of the nanomagnet. The magnetization is then determined by
positioning the cantilever in a signal region along the x-axis with the
distance from the nanomagnet center known, and acquiring an MRFM spectrum
Longenecker  _et al._ (2012), shown in 3(e). From the center frequency-
dependent signal decay, we calculate a conservative saturation magnetization
of $2.0\text{\,}\mathrm{T}\text{/}$.This magnetization is about
$9\text{\,}\mathrm{\char 37\relax}\text{/}$ higher compared to other FeCo
nanomagnets measured in the same setup Krass  _et al._ (2022), indicating
excellent material properties.

Our measurements indicate several immediate advantages of the magnet-in-
microstrip over the magnet-on-microstrip design. Firstly, the much larger
$G_{x}$ directly improves the speed and signal-to-noise ratio (SNR) of NanoMRI
scans. In MRFM, the SNR is proportional to $G_{x}^{2}$ Degen  _et al._ (2007).
An improvement by a factor of four in $G_{x}$ therefore translates into an SNR
improvement by a factor of 16 per spin under ideal conditions. However, an
increase in $G_{x}$ leads to physically thinner resonance slices, reducing the
addressed spin sample volume and therefore also the signal. As a consequence,
we observe an SNR value that is only 2 to 3 times greater than previously
measured with magnet-on-microstrip devices.

We can leverage the high SNR to reduce the acquisition time per data point. In
Fig. 4(a) and (b), we show a two-dimensional spatial scan measured with an
integration time of only $10\text{\,}\mathrm{s}\text{/}$ per pixel, compared
to typical integration times between $60\text{\,}\mathrm{s}\text{/}$ and
$300\text{\,}\mathrm{s}\text{/}$ in earlier measurements Moores  _et al._
(2015); Grob  _et al._ (2019); Krass  _et al._ (2022). The SNR we obtain
reaches values above 8 over a wide range of the scan. The signal strength and
SNR clearly surpass the results obtained for the same sample with a magnet-on-
microstrip device Krass  _et al._ (2022), where a sixfold longer integration
time and smaller tip-magnet distance were used, see Fig. 4(d) and (e). This
speedup, enabled by the large $G_{x}$ values, will be valuable when performing
large three-dimensional scans of extended nanoscale samples.

A further advantage of the magnet-in-microstrip design is the reduction of
topographical features which drastically reduces static cantilever bending
close to the surface. In Fig. 4(c) and (f), we compare the surface approach
trajectories measured over the two device types. In contrast to the
traditional nanomagnets, whose profile is found to induce significant bending
and feedback instabilities Krass  _et al._ (2022), the nearly seamless surface
of the embedded magnets allows for faithful scanning down to a few tens of
nanometers surface promimity and facilitates data acquisition at crucial
positions close to the magnet edges.

Finally, a recent study highlighted the connection between topographical
features and non-contact friction (NCF) Héritier  _et al._ (2021) via
dielectric interactions Kuehn  _et al._ (2006); Yazdanian  _et al._ (2008). We
expected that the embedded magnet design could be advantageous for obtaining
lower NCF. However, contrary to our expectations, we generally observed an
increase of NCF compared to magnet-on-microstrip devices. We also found a
strong dependence of the NCF on the top layer material, with devices capped by
a Au layer performing significantly better than others where the magnet is
left exposed. Looking forward, we speculate that the NCF could be further
reduced by covering the magnet and the Au microstrip with a conducting,
atomically flat layer. Such two-dimensional materials are being researched
intensively for applications in wearable electronics Orts Mercadillo  _et al._
(2022) and offer many advantages in various areas of nanotechnology. Besides
screening unwanted electrical interactions, a layer consisting of only a few
atomic layers would also allow a closer approach of the scanning tip to the
magnet surface and make larger gradients accessible. Under such conditions, we
expect that the magnet-in-microstrip design would result in an up to tenfold
improvement of $G_{x}$. Applied to MRFM, a tenfold higher $G_{x}$ would
translate into a hundredfold increase in signal strength and SNR or a
reduction in averaging time by four orders of magnitude at constant SNR. The
magnet-in-microstrip design can therefore significantly boost the performance
of NanoMRI instruments.

## Acknowledgments

We thank the operations team of the FIRST cleanroom, especially Sandro Loosli
and Petra Burkard, as well as the operations team of the Binnig and Rohrer
Nanotechnology Center (BRNC) at IBM Rüschlikon, especially Ute Drechsler,
Richard Stutz and Dr. Diana Dávila Pineda, their continued support and
expertise has enabled the development of this new device generation.

##  Appendix A Appendix A: Fabrication of Embedded Nanomagnets

###  A.1 Wafer Preparation

The fabrication process begins at the wafer scale in order to streamline the
initial processes. The wafers used were $10\text{\,}\mathrm{cm}\text{/}$
(4-inch) silicon wafers made by MicroChemicals GmbH (article number
WTD40525250B1314S102, boron-doped,
$1\text{\,}\mathrm{\SIUnitSymbolOhm}\text{\,}\mathrm{cm}\text{/}10\text{\,}\mathrm{\SIUnitSymbolOhm}\text{\,}\mathrm{cm}\text{/}$,
$100\text{\,}\mathrm{nm}\text{/}$ oxide layer). In order to ensure consistent
alignment between multiple fabrication steps, markers are etched into the
wafer itself to provide physical landmarks. While these markers can be seen
optically, their main purpose is to enable visibility under SEM.

  * •

Using Laser Writing (DWL 66+, Heidelberg Instruments) and the optical resist
AZ 1518 (MicroChemicals GmbH), a periodic grid of alignment markers and chip
borders is exposed into the resist layer on top of the wafer and developed
using AZ 400K 1:4 MIC developer (MicroChemicals GmbH). Standard hardbake and
cleaning measures are performed.

  * •

Using a two-stage RIE etch process (PlasmaPro 80 RIE, Oxford Systems), the
silicon oxide layer is breached and subsequently the underlying silicon is
etched to a depth of roughly $2\text{\,}\mathrm{\SIUnitSymbolMicro
m}\text{/}$. The process step for the oxide breach uses CF4 at
$150\text{\,}\mathrm{W}\text{/}$, $65\text{\,}\mathrm{mtorr}\text{/}$,
$20\text{\,}\mathrm{sccm}\text{/}$ for $2.5\text{\,}\mathrm{min}\text{/}$. The
process step for the silicon etch uses SF6 & O2 at
$100\text{\,}\mathrm{W}\text{/}$, $100\text{\,}\mathrm{mtorr}\text{/}$,
$50\text{\,}\mathrm{sccm}\text{/}$/$10\text{\,}\mathrm{sccm}\text{/}$ for
$2\text{\,}\mathrm{min}\text{/}$. Etch depth into the silicon is not relevant
as long as it is within micron range and does not deform the markers in the
process. After the RIE etch, the resist is lifted and the wafer is cleaned in
solvent baths of acetone and isopropyl alcohol as well as in a mild oxygen
plasma.

  * •

Using a BAK 501 LL electron beam evaporator (Evatec AG),
$10\text{\,}\mathrm{nm}\text{/}$ of titanium and
$260\text{\,}\mathrm{nm}\text{/}$ of FeCo alloy (65$\%$/35$\%$ at.wt.) are
evaporated onto the wafer surface without any masking to achieve a uniform
film. The titanium serves as adhesion layer, while the FeCo will form the
nanomagnets. Once the evaporation is complete, the wafer is subjected to
glancing angle IBE in order to further planarize the surface and remove
surface impurities, removing ca. $10\text{\,}\mathrm{nm}\text{/}$ of material
in the process. Once complete, $3\text{\,}\mathrm{nm}\text{/}$ of platinum are
evaporated onto the FeCo layer as a protection against oxidation, followed by
$130\text{\,}\mathrm{nm}\text{/}$ of Cr, which serves as a spacer layer for
the following process.

###  A.2 Hardmask Fabrication

To shape the nanomagnets from the bulk FeCo film, Argon Ion Beam Etching is
used, with Cr acting as a spacer layer and a hardmask made from silicon
nitride (SiN) acting as physical shield from the argon ion impacts. Before the
hardmask is grown, the wafer is diced into smaller chips
($10\times$9\text{\,}\mathrm{mm}\text{/}$$) for ease of handling and
prototyping.

  * •

Using PECVD, a SiN layer with a thickness of $550\text{\,}\mathrm{nm}\text{/}$
is grown on top of the Cr layer. The step is performed at a process
temperateure of $300\text{\,}\mathrm{\SIUnitSymbolCelsius}\text{/}$.

  * •

In order to pattern the SiN layer, a negative resist (ma-n 2410, micro resist
technology GmbH) is applied and exposed via electron beam lithography (EBL).
Prior to resist application, the chips need to be washed thoroughly in acetone
and isopropyl alcohol to improve resist adhesion to the SiN film.

  * •

Using the engraved alignment markers, the core positions on the chip are
located and exposed with simple circular patterns to form resist pillars.
Three different magnets are implemented on each microstrip with a spacing of
$5\text{\,}\mathrm{\SIUnitSymbolMicro m}\text{/}$ between the centers. The
radii written in the exposure pattern are $250\text{\,}\mathrm{nm}\text{/}$,
$250\text{\,}\mathrm{nm}\text{/}$, and $325\text{\,}\mathrm{nm}\text{/}$, with
corresponding exposure doses of
$350\text{\,}\mathrm{\SIUnitSymbolMicro}\mathrm{C}\mathrm{/}\mathrm{c}\mathrm{m}^{2}$,
$787.5\text{\,}\mathrm{\SIUnitSymbolMicro}\mathrm{C}\mathrm{/}\mathrm{c}\mathrm{m}^{2}$,
and
$350\text{\,}\mathrm{\SIUnitSymbolMicro}\mathrm{C}\mathrm{/}\mathrm{c}\mathrm{m}^{2}$.
The doses and radii control the width of the resulting resist pillars, and
therefore the sizes of the nanomagnets. Development of the resist is achieved
with MF 319, a TMAH-based developer.

  * •

Using a two-step RIE process, the resist pattern is then transferred to the
SiN film, resulting in the desired SiN hardmask shape. The RIE steps consist
of a $180\text{\,}\mathrm{s}\text{/}$ etch with
$30\text{\,}\mathrm{sccm}\text{/}$ of SF6 at $150\text{\,}\mathrm{W}\text{/}$
and $30\text{\,}\mathrm{mtorr}\text{/}$, followed by a
$225\text{\,}\mathrm{s}\text{/}$ etch with
$50\text{\,}\mathrm{sccm}\text{/}$/$5\text{\,}\mathrm{sccm}\text{/}$ of
CHF3/O2 at $100\text{\,}\mathrm{W}\text{/}$ and
$55\text{\,}\mathrm{mtorr}\text{/}$ (the second step employs a mixed process
gas). The first etch step produces sloped sidewalls while the second produces
nearly vertical sidewalls. A combination of the two different etch steps
assures an optimized etch profile for IBE.

  * •

Once the RIE process is complete, the negative resist is lifted in a solvent
bath of NMP or DMSO at $80\text{\,}\mathrm{\SIUnitSymbolCelsius}\text{/}$. NMP
is more effective at lifting quickly and cleanly, however DMSO, which is
nontoxic, and has also yielded satifactory results.

###  A.3 IBE-assisted Creation of Nanomagnets

Now that the hardmask is in place, Ion Milling of the metallic substrate can
begin.

  * •

Using an Ionfab 300 Plus (Oxford Instruments), the entire metallic film
surrounding the SiN hardmask is eroded with argon ion bombardment at a beam
current of $500\text{\,}\mathrm{mA}\text{/}$ while the sample holder is
rotated around its central axis. The critical issue in this process is to
achieve both an optimal pattern transfer of the hardmask while simultaneously
mitigating redeposition. To that end, the incidence angle of the ion beam is
varied during the milling step, and the material ejection is monitored using
secondary ion mass spectroscopy (SIMS). The process begins with an incidence
angle of $20\text{\,}\mathrm{\SIUnitSymbolDegree}\text{/}$ (from the surface
normal, $0\text{\,}\mathrm{\SIUnitSymbolDegree}\text{/}$ being perpendicular
to the surface) for $21\text{\,}\mathrm{min}\text{/}$, eroding
$130\text{\,}\mathrm{nm}\text{/}$ of Cr, $3\text{\,}\mathrm{nm}\text{/}$ of Pt
and most of the $250\text{\,}\mathrm{nm}\text{/}$, approximately
$235\text{\,}\mathrm{nm}\text{/}$.

  * •

In order to remove redeposited material from the newly created metal pillars’
sidewalls, the incidence angle is changed to
$80\text{\,}\mathrm{\SIUnitSymbolDegree}\text{/}$ for
$10\text{\,}\mathrm{min}\text{/}$ at constant rotation. SIMS readout at such
glancing angles is impaired and milling rates for such angles are best
calibrated using test- and sacrificial samples.

  * •

The steep $20\text{\,}\mathrm{\SIUnitSymbolDegree}\text{/}$ angle is resumed
for the final step until the elemental signal of the Ti adhesion layer
diminishes in the SIMS readout, which typically occurs after around
$100\text{\,}\mathrm{s}\text{/}$. This decrease marks the breach of the final
metal layer and the completion of the nanomagnet stacks.

  * •

The shape of the nanomagnets is examined using AFM and SEM; to that end, a
sacrificial sample is immersed in Cr etch (Cerium Nitrate solution) in order
to selectively dissolve the Cr spacer separating the hardmask and the newly
created nanomagnet. The existence of the spacer allows the selective
separation of the nanomagnet from its hardmask, however the mask needs to
remain at this stage for regular samples. Using SEM, the milling outcome and
general shape of the nanomagnet is examined, while AFM yields the precise
height of the nanomagnet with respect to the silicon surface. In the specific
example of the sample measured in this publication, the height of the
nanomagnets was determined to be $260\text{\,}\mathrm{nm}\text{/}$.

###  A.4 Au Layer Evaporation

Using the determined height of the nanomagnet, a variable-angle evaporation of
Ti and Au is conducted to evaporate the Au film onto the silicon surface,
thereby covering the free-standing nanomagnet arrays with a Ti/Au metal film.
In order to monitor the process, 4 sacrificial chips are created before the
evaporation and covered with chromium as a stopping layer. These chips are
then coated along with the regular samples.

The coating process needs to leave the chromium spacer exposed for a future
liftoff. The sides of the nanomagnets need to be coated homogeneously, ideally
in a relatively passive material, and the final Au film needs to match the
height of the nanomagnet as closely as possible to minimize topographic
features.

  * •

Starting the evaporation at an angle of
$60\text{\,}\mathrm{\SIUnitSymbolDegree}\text{/}$ to the surface normal with a
rotating sample holder, $20\text{\,}\mathrm{nm}\text{/}$ of Ti is evaporated
onto the surface of the chip as well as onto the sidewalls of the nanomagnet
arrays. The effective layer thickness on the chip surface due to tilt is
$10\text{\,}\mathrm{nm}\text{/}$. The Ti film serves as a crucial adhesion
film for the sidewalls of the nanomagnets as well, ensuring no gaps will form
between the FeCo and Au films. This is vital to avoid galvanic corrosion
effects which are facilitated by trapped moisture in potential gaps, an effect
which has been observed on previous device iterations.

  * •

Using the same incidence angle, $100\text{\,}\mathrm{nm}\text{/}$ of Au is
evaporated, homogeneously coating the surface and sidewalls in a (in case of
the sidewalls relatively thin) layer of Au. The effective Au layer thickness
on the chip surface due to tilt is $50\text{\,}\mathrm{nm}\text{/}$ at this
point.

  * •

With an incidence angle of $0\text{\,}\mathrm{\SIUnitSymbolDegree}\text{/}$,
$100\text{\,}\mathrm{nm}\text{/}$ Au are evaporated onto the chip, translating
directly to effective layer thickness and mostly leaving the sidewalls
unaffected.

  * •

In a third evaporation step, with an incidence angle of
$30\text{\,}\mathrm{\SIUnitSymbolDegree}\text{/}$,
$173\text{\,}\mathrm{nm}\text{/}$ of Au is evaporated. This angle is chosen to
minimize shadowing effects from the rotating evaporation while still
maintaining coverage of the sidewalls.The effective layer thickness on the
chip surface due to tilt is $150\text{\,}\mathrm{nm}\text{/}$.

  * •

Using a sacrificial chip introduced previously, the Au layer is masked with AZ
1518 analogously to the marker fabrication, and is etched down with RIE using
CHF3/O2 with a flow of
$45\text{\,}\mathrm{sccm}\text{/}$/$2\text{\,}\mathrm{sccm}\text{/}$ for
$13.5\text{\,}\mathrm{min}\text{/}$ at $300\text{\,}\mathrm{W}\text{/}$ and
$25\text{\,}\mathrm{mtorr}\text{/}$. Conclusion of the etch is verified in SEM
and the layer thickness is confirmed to be $300\text{\,}\mathrm{nm}\text{/}$
via AFM, yielding a total thickness of $310\text{\,}\mathrm{nm}\text{/}$ when
including the Ti adhesion layer.

###  A.5 Hardmask Liftoff and Polishing

Once the height of the Au film has been determined, the distance between the
Au layer surface and the nanomagnet surface can be calculated to be
$50\text{\,}\mathrm{nm}\text{/}$ in total. It is generally easier to overshoot
the Au layer thickness and then erode the excess than the reverse case, as the
following steps show:

  * •

Using the same CHF3/O2 RIE recipe with a shorter runtime of
$107\text{\,}\mathrm{s}\text{/}$, $40\text{\,}\mathrm{nm}\text{/}$ of Au are
removed in an anisotropic vertical etch, leaving
$10\text{\,}\mathrm{nm}\text{/}$ of Au layer above the nanomagnet level. Note
that vertical IBE has proven to be unreliable to remove material in a strictly
vertical etch without shadowing, which in this case must be avoided.
Furthermore, care must be taken not to overetch the layer, as a breach of the
nanomagnet sidewalls would be catastrophic, corroding the material in the
process.

  * •

In order to remove the SiN hardmasks and expose the nanomagnets, the sidewalls
of the Cr spacer on top need to be exposed. To do so without eroding the
surrounding Au film, $10\text{\,}\mathrm{nm}\text{/}$ Cr and
$20\text{\,}\mathrm{nm}\text{/}$ Ti are evaporated vertically as a protection
layer and the whole chip is ion milled at
$80\text{\,}\mathrm{\SIUnitSymbolDegree}\text{/}$ incidence angle for
$18\text{\,}\mathrm{min}\text{/}$, which roughly is the time the protection
layer can withstand the ion milling at this angle.

  * •

After the glancing angle ion milling step, the protection layer and the SiN
hardmasks are removed via Cr etch and the topography is examined in SEM and
AFM.

  * •

In case of overshooting the desired milling depth, a generous layer of Au/Ti
can be applied and polished down iteratively to avoid another overshoot.

  * •

In case of insufficient milling depth, an application of another Cr/Ti
protection layer and a further milling step can reduce the layer thickness
further without compromising topography or the nanomagnet itself. Protection
layers are especially desirable if the topography is indufficiently flat,
otherwise a simple glancing angle milling step can be sufficient.

  * •

Once the nanomagnets are visible below the Au layer in SEM, gentle polishing
steps can be made to remove any remaining Au islands on top of the nanomagnet.
This is made easier by the comparably high milling rate of Au compared to FeCo
at all angles.

  * •

For the embedded nanomagnets measured in this paper, 5 iterative polishing
steps have produced a suitable result.

Figure 5: Comparison of spin inversion pulse characteristics between
microstrip device generations for $60\text{\,}\mathrm{s}\text{/}$ integration
time. Color scale bars are limited to $800\text{\,}\mathrm{a}\mathrm{N}^{2}$
for better low-signal contrast. (a) Spin signal recorded with the magnet-in-
microstrip device from Fig. 2(b) at a distance of
$90\text{\,}\mathrm{nm}\text{/}$ above the microstrip surface (sample-magnet
separation of $110\text{\,}\mathrm{nm}\text{/}$). High signal is obtained
roughly above $400\text{\,}\mathrm{kHz}\text{/}$ FM deviation and
$0.5\text{\,}\mathrm{V}\text{/}$ AWG amplitude, with a measured maximum of
$2000\text{\,}\mathrm{a}\mathrm{N}^{2}$. (b) Spin signal recorded with a
conventional magnet-on-microstrip design from Fig. 2(c) at a sample-magnet
separation of $30\text{\,}\mathrm{nm}\text{/}$. The maximum measured spin
signal is $900\text{\,}\mathrm{a}\mathrm{N}^{2}$.

###  A.6 Microstrip Creation from Au film

The current state of the sample chip is a planar Au film with FeCo nanomagnets
embedded in its surface. In order to pattern a microstrip from the bulk film,
the following steps are taken:

  * •

Using e-beam evaporation and PECVD, a $130\text{\,}\mathrm{nm}\text{/}$ thick
Cr film and a $250\text{\,}\mathrm{nm}\text{/}$ thick SiN film are applied to
the sample chip.

  * •

Using EBL, a resist bilayer of 50K/950K PMMA (Allresist GmbH) is exposed with
a dose of
$200\text{\,}\mathrm{\SIUnitSymbolMicro}\mathrm{C}\mathrm{/}\mathrm{c}\mathrm{m}^{2}$.
Alignment is again achieved using the etched alignment markers in the bulk
substrate.

  * •

The resist is developed using MIBK developer and
$40\text{\,}\mathrm{nm}\text{/}$ of Cr are evaporated onto the now exposed SiN
layer, forming a Cr film in the shape of the desired microstrip, positioned
above the nanomagnet array buried below. The resist is then lifted using a
solvent bath.

  * •

The Cr layer acts as etch ask for a RIE etch step to etch the SiN film using
the CHF3/O2 recipe mentioned before, forming the hardmask for the next milling
step. A hybrid etch recipe is not critical in this case.

  * •

The chip is ion milled at $20\text{\,}\mathrm{\SIUnitSymbolDegree}\text{/}$
for a relatively short $10\text{\,}\mathrm{min}\text{/}$ until Ti is visible
in the SIMS, signalling the removal of the Au layer, then polished at
$80\text{\,}\mathrm{\SIUnitSymbolDegree}\text{/}$ for
$5\text{\,}\mathrm{min}\text{/}$ to remove redeposited material from the
microstrip sidewalls and the Cr spacer sidewalls. A final
$2\text{\,}\mathrm{min}\text{/}$ are spent milling at
$20\text{\,}\mathrm{\SIUnitSymbolDegree}\text{/}$ to breach the Ti layer and
expose the surrounding silicon oxide substrate. This concludes the patterning
of the microstrip and allows the SiN hardmask to be lifted using a Cr etch.

  * •

A final layer of Ti/Au with thickness of $3\text{\,}\mathrm{nm}\text{/}$ and
$12\text{\,}\mathrm{nm}\text{/}$, respectively, is applied identically to the
$40\text{\,}\mathrm{nm}\text{/}$ thick Cr layer using EBL and PMMA resist.

The final step adding the Ti/Au layer was only performed in response to high
noise levels during measurements and poor SNR, significantly improving device
performance. This leaves the finished embedded nanomagnet microstrip to be
wirebonded to a suitable PCB to be incorporated into the MRFM system. In case
of multiple microstrips on a chip unit (which is preferable to increase
yield), dicing of the chip is required beforehand. The final microstrip chip
has a dimension of $5\text{\,}\mathrm{mm}\text{/}$ x
$1.5\text{\,}\mathrm{mm}\text{/}$. Fixation to the supporting PCB is achieved
by using dried PMMA 950K as glue.

##  Appendix B Adiabaticity Plots

The performance characteristics of the two microstrip generations is compared
in Fig. 5. Spin signal is measured in dependence of RF pulse strength (shown
here as AWG Amplitude) and frequency deviation. A considerable increase in
spin signal is observed for the planar microstrip design in comparison to the
stacked microstrip design.

Figure 6: SNR data corresponding to Fig. 3(c) showing maximum values of 25 at
$60\text{\,}\mathrm{s}\text{/}$ integration time, demonstrating excellent
noise characteristics for the embedded nanomagnet design.

##  Appendix C SNR Measurements for Resolution Determination Measurement

As a supplement for Fig. 3(c), the increase in SNR is shown for the
corresponding measurement.

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