# Nanoantennas and Nanoradars: The Future of Integrated Sensing and
Communication at the Nanoscale

M. Javad Fakhimi, Ozgur B. Akan, The authors are with the Center for neXt-
generation Communications (CXC), Department of Electrical and Electronics
Engineering, Koç University, Istanbul 34450, Turkey (e-mail: {mfakhimi22,
akan}@ku.edu.tr). O. B. Akan is also with the Internet of Everything (IoE)
Group, Electrical Engineering Division, Department of Engineering, University
of Cambridge, Cambridge CB3 0FA, UK (email: oba21@cam.ac.uk). This work was
supported by the AXA Research Fund (AXA Chair for Internet of Everything at
Koç University).

###### Abstract

Nanoantennas, operating at optical frequencies, are a transformative
technology with broad applications in 6G wireless communication, IoT, smart
cities, healthcare, and medical imaging. This paper explores their fundamental
aspects, applications, and advancements, aiming for a comprehensive
understanding of their potential in various applications. It begins by
investigating macroscopic and microscopic Maxwell’s equations governing
electromagnetic wave propagation at different scales. The study emphasizes the
critical role of Surface Plasmon Polariton (SPP) wave propagation in enhancing
light-matter interactions, contributing to high data rates, and enabling
miniaturization. Additionally, it explores using two-dimensional materials
like graphene for enhanced control in terahertz communication and sensing. The
paper also introduces the employment of nanoantennas as the main building
blocks of Nano-scale Radar (NR) systems for the first time in the literature.
NRs, integrated with communication signals, promise accurate radar sensing for
nanoparticles inside a nano-channel, making them a potential future
application in integrated sensing and communication (ISAC) systems. These
nano-scale radar systems detect and extract physical or electrical properties
of nanoparticles through transmitting, receiving, and processing
electromagnetic waves at ultra-high frequencies in the optical range. This
task requires nanoantennas as transmitters/receivers/transceivers, sharing the
same frequency band and hardware for high-performance sensing and resolution.

###### Index Terms:

Nanoantennas, light-matter interaction, Maxwell’s equations, terahertz
radiation, ultrafast data transmission, 6G wireless communications,
biosensors, photo-detection, integrated sensing and communication (ISAC).

##  I Introduction

Rapid advancement of wireless communication technologies has changed the way
we access information in our increasingly connected world. As we approach the
era of 6G wireless communication, the demand for higher data rates, lower
latency, and enhanced connectivity continues to dominate. To address these
challenges, researchers are exploring new technologies that can reveal the
unique properties of terahertz frequencies (0.1 to 10 THz) [1]. Among these
transformative technologies, nanoantennas are proposed as promising and
practical structures with the potential to reshape wireless communication,
revolutionize the Internet of Things (IoT) and healthcare, and advance medical
imaging [2]. These nanoscale structures enable hyper data transmission rates
and high capacity, making them ideal for addressing the burgeoning data
demands of the 6G era [4]. Furthermore, the development of nanoantennas as a
crucial building block of Nano-scale Radar systems (NRs) is critical.
Nanoantennas play a significant role in signal transmission and reception,
enabling the tracking of spatial and physical/electrical properties of
nanoparticles (targets) in nano-channels. Moreover, the use of nanoantennas in
the field of medicine has demonstrated exceptional potential in diverse
medical applications including imaging, biosensing, disease detection, drug
delivery, photodynamic therapy (PDT), real-time monitoring, and photothermal
therapy [234, 229, 230, 231, 232, 233, 228, 235, 236, 237]. The foundation of
nanoantennas lies in the principles of electromagnetic theory, expressed
through the macroscopic and microscopic Maxwell’s equations [27, 22, 14, 25].
These fundamental equations explain the propagation of electromagnetic waves,
including those at terahertz frequencies and optical frequencies, providing
insights into the intricate behavior of nanoantennas and their interactions
with electromagnetic radiation [5, 23]. Surface Plasmon Polaritons (SPPs)
emerge as a pivotal aspect of nanoantenna research, facilitating enhanced
light-matter interactions and confinement of terahertz waves [107] [20, 21,
24]. SPP wave propagation enables significant control over electromagnetic
fields, contributing to the high data rates and miniaturization capabilities
of nanoantennas [90]. Furthermore, the quantum mechanical perspective of
nanoantennas offers a unique insight into the behavior of these structures in
the microscopic world [6], [23, 33, 34, 36, 35, 19]. The coexistence of
classical and quantum phenomena in nanoantennas manifest new paths for quantum
communication and sensing, offering a paradigm shift in future applications
[7]. Quantum mechanics provides a deeper understanding of the underlying
mesoscopic and microscopic physics, and helps us to explain the fundamentals
of the interaction between the light and the matter, which is considered to be
the root of differences between the classical antenna theory, and the
nanoantenna radiation rules. Researchers have harnessed these principles to
fabricate nanoantennas that efficiently receive and transmit data while
minimizing energy consumption and space requirements [8]. Additionally, the
utilization of two-dimensional materials, such as graphene, for manufacturing
more efficient and more compact structures to fit in the nanoscale, has been
investigated in this paper [9]. To realize the full potential of nanoantennas
in real-world applications, researchers have focused on the fabrication and
characterization methods of these nanoscale structures [10]. Fabrication
techniques at the nanoscale, such as Photolithography [262], Electron Beam
Lithography (EBL) [263, 264], Focused Ion Beam Lithography (FIBL) [265, 266],
Nanoimprint Lithography (NIL) [267], Roll-to-Roll Printing [268], and Solid-
state Superionic Stamping [269] allow precise construction of nanoantennas
with subwavelength dimensions, making them suitable for integration into
highly compact devices and systems. Characterization methods, such as Optical
Microscopy (OM) [272], Scanning Electron Microscopy (SEM) [273], Scanning
Tunneling Microscopy (STM) [274], Transmission Electron Microscopy (TEM)
[275], and Atomic Force Microscopy (AFM) [276] enable engineers to analyze the
performance and functionality of nanoantennas under various conditions,
providing valuable insights for optimization and application-specific
customization.

This paper aims to underscore the significance of ongoing research and
progress in the realm of nanoantennas, emphasizing on their design,
manufacturing considerations, and foundational elements. Meanwhile, the
concept of nanoscale radar systems has been proposed for the first time.
Considering numerous emerging applications of nanoantennas and nanoradars in
future applications across diverse domains, notably including 6G wireless
communications and Internet of Things (IoT), and Medical applications, this
work tries to encourage researchers and come up with a starting point in
further work and studies.
The rest of the paper is organized as follows. The necessary concepts and
subjects to fully-understand the functionality of nanoantennas and nanoradars,
such as the governing radiation rules at nanoscale, as well as the SPP waves
propagation and excitation methodologies, are given in Sec. II. Then, the
principles and parameters of nanoantenna theory, and their applications, such
as in NR systems are discussed in Sec. III and in Sec. IV, respectively. At
last, Sec. V contains the conclusion and future research direction for
nanoradar systems.

##  II Background Knowledge

###  II-A Antenna Theory

####  II-A1 Radiation in Macroscopic Range

According to the well-established principles of electromagnetism, antennas
function by radiating electromagnetic waves when influenced by sources like
electric and magnetic charges, as well as current densities [30]. To generate
electromagnetic waves, oscillating fields in time are required. By designing
the antenna structure appropriately, these waves can be guided along a
transmission line (the body of the antenna) before being emitted into free
space. When studying an antenna system, it is essential to identify the
sources of radiation and calculate the resulting electromagnetic fields using
relevant equations. This analysis helps us understand how the antenna radiates
energy into the surrounding space. Once the radiation has been determined, we
can evaluate the antenna’s performance using commonly used metrics such as
gain, directivity, and radiation efficiency. These measures provide insights
into the antenna’s effectiveness in transmitting or receiving electromagnetic
signals.[18, 17].

Maxwell’s equations, the fundamental equations of electromagnetism, have been
widely studied and applied in the low-frequency regime, which includes
microwave and infrared frequencies. At these frequencies, materials such as
metals with high conductivities inhibit the propagation of electromagnetic
waves within them. As a result, in microwave applications, metals are often
approximated as Perfect Electric Conductors (PECs) to simplify the analysis.
Generally speaking, Maxwell’s equations can solve all electromagnetic boundary
value problems, including antenna configurations. Assuming an $e^{j\omega t}$
time convention, in a lossless medium with $\sigma=0$, we may recap the
equations as follows:

$\displaystyle\nabla\cdot\textbf{D}=\rho_{e},$ (1)
$\displaystyle\nabla\cdot\textbf{B}=\rho_{m},$ (2)
$\displaystyle\nabla\times\textbf{E}=-\textbf{M}-j\omega\mu\textbf{H},$ (3)
$\displaystyle\nabla\times\textbf{H}=+\textbf{J}+j\omega\varepsilon\textbf{E},$
(4)

where E, D, B, H, and J are the electric field, electric displacement field,
magnetic flux, magnetic field, and electric current density, respectively,
while $\rho_{e}$, and $\rho_{m}$ denote the electric and magnetic charges. In
these equations, J, M, $\rho_{e}$, and $\rho_{m}$ can be considered as the
sources of radiation. Also, the Ohm’s Law can be restated as
$\nabla\cdot\textbf{J}=-j\omega\frac{\rho_{e}}{\varepsilon}$, and
$\nabla\cdot\textbf{M}=-j\omega\frac{\rho_{m}}{\mu}$ accordingly. Naturally
$\rho_{m}=0$, and the magnetic flux B is solenoidal, which implies
$\nabla\cdot\textbf{B}=\nabla\cdot\textbf{H}=0$. Inspired by these equations,
we may now investigate the microscopic regime, to realize the rules of
propagation at nano scales.

####  II-A2 Nanoscale Radiation

As observed in the preceding section, Maxwell’s equations exhibit no
dependency on the operating frequency. This implies that the relationships
between electromagnetic fields remain consistent throughout the entire
spectrum.However, the behavior of propagating waves changes when we increase
the frequency to the Terahertz gap and above. Three fundamental questions
arise in this case: First, what exactly happens to Maxwell’s equations at such
high frequencies? Do they remain unchanged, or do modifications need to be
made to account for the new effects and phenomena? Second, which materials can
tolerate these high-frequency radiations, and which substances are suitable
for constructing antennas to operate in this regime? Third, how do the
structure and propagation environment of the waves change at these
frequencies? Are there new behaviors, limitations, or advantages brought about
by the higher frequency range?
First of all, let’s examine the wave equation derived from Maxwell’s
equations. The radiated electric and magnetic fields can be expressed by
applying the curl operation to one of the curl equations of Maxwell’s
equations and substituting the resulting expression into the other equation.
By doing this, we obtain

$\displaystyle\nabla^{2}\textbf{E}-\mu\varepsilon\frac{\partial^{2}\textbf{E}}{\partial
t^{2}}=\nabla(\nabla\cdot\textbf{E})+\mu\frac{\partial\textbf{J}}{\partial
t},$ (5)

where the RHS represents how the sources are participating in radiation. Now
if we consider a source-free region with a uniform electric charge density
($\nabla\cdot E=0$), we obtain

$\displaystyle\nabla^{2}\textbf{E}-\mu\varepsilon\frac{\partial^{2}\textbf{E}}{\partial
t^{2}}=0.$ (6)

We have assumed constant values for permittivity and permeability, which holds
true in a homogeneous and isotropic environment. However, at the nanoscale,
the assumptions of homogeneity and isotropy may not be valid due to the
significant influence of quantum effects. In contrast to macroscopic regions,
where microwaves belong, the nanoscale is characterized by the presence of
quantum phenomena such as quantization, wave-particle duality, plasmonic
effect, uncertainty principle, and superposition [19]. These effects play
critical roles, and waves can interact with matter at molecular scales. One
notable quantum effect in nanoscale radiations is the plasmonic effect, which
gives rise to the generation of plasmons. Plasmons confine the electromagnetic
waves near the surface of a metal, leading to the formation of Surface Plasmon
Polaritons (SPP) or Localized Surface Plasmons (LSP) [20, 21]. SPPs are
propagating and dispersive electromagnetic waves that are coupled to the
electron plasma of a conductor at a dielectric interface. They exhibit unique
properties and allow for the confinement and manipulation of light at the
nanoscale. On the other hand, LSPs are non-propagating excitations of the
conduction electrons in metallic nanostructures, coupled to the
electromagnetic field. LSPs are responsible for enhanced light-matter
interactions and localized field enhancements. These two types of
electromagnetic waves are explained in more details in the next section. Since
quantum effects play a critical role at the nanoscale, they directly influence
the electrical and magnetic properties of the medium, including its
permittivity and permeability. This can lead to the medium becoming
anisotropic (with properties dependent on direction) or dispersive (with
properties dependent on frequency). To accurately describe these effects, we
represent permittivity and permeability as complex tensors in their most
general forms. Consequently, we obtain

$\displaystyle\nabla\times\nabla\times\textbf{E}-\mu_{0}\varepsilon_{0}\omega^{2}\underline{\mu}\underline{\varepsilon}\textbf{E}=0.$
(7)

In comparison to the conventional wave equation for macroscale radiations, the
general form of the wave equation (7) in the nanoscale is often more complex
and challenging to solve. In many cases, analytical solutions for this
equation may not be readily available. Consequently, numerical methods, such
as the Finite-Difference Time-Domain (FDTD) and Finite Element Method (FEM),
are commonly employed to analyze the behavior of nanoscale radiations in
various geometries and structures. The FDTD method discretizes time and space,
allowing for the numerical approximation of the wave equation. By updating the
fields over small time steps and spatial grid points, the FDTD method can
simulate the propagation of electromagnetic waves and their interaction with
nanoscale structures. This method is widely used for its simplicity and
ability to handle a wide range of structures and geometries. On the other
hand, the Finite Element Method (FEM) approximates the solution of the wave
equation by dividing the domain into smaller elements. This numerical method
is particularly suitable for irregular geometries and complex material
properties. By solving the equation within each element and considering the
interactions between adjacent elements, the FEM can accurately model the
behavior of nanoscale radiations. Both FDTD and FEM, along with other
numerical methods, have become indispensable tools for analyzing and
understanding the behavior of nanoscale radiations, as they provide
computational solutions to the complex wave equations that describe these
phenomena [17, 22]. In the following section, we investigate the plasmonic
effect from a quantum mechanical point of view, to more deeply understand the
way behaviour of nanoantennas in terms of nanoscale radiations.

###  II-B Surface Plasmon Polaritons

####  II-B1 Definitions and Wave Equation

A fascinating phenomenon occurs when a conductor, such as a metal, comes into
proximity with an insulator, known as a dielectric. Confined electromagnetic
waves with a two-dimensional nature become capable of propagating along the
interface. These fascinating waves are referred to as Surface Plasmon
Polaritons (SPPs). The generation of SPPs arises from the intricate coupling
between the electromagnetic fields and the oscillations of electron charges
within the conductor. SPPs have fascinating applications in nanophotonics
[96], optoelectronics [97], and sensing [98] due to their unique ability to
confine electromagnetic waves (light) at the nanoscale. In the absence of
external sources, the nature of these waves can be explored through their wave
equation [23], i.e.,

$\displaystyle\nabla^{2}E-\frac{\varepsilon}{c^{2}}\frac{\partial^{2}E}{\partial
t^{2}}=0,$ (8)

where $c$ is the speed of light in vacuum.
Taking equation (8) into the fourier domain, and considering $k_{0}=\omega/c$
as the wave vector, we obtain the Helmholtz Wave Equation, i.e.,

$\displaystyle(\nabla^{2}+\varepsilon k_{0}^{2})E=0.$ (9)

Applying the appropriate boundary conditions can offer solutions to (9) within
various structures, which allows for two distinct sets of self-consistent
solutions: TE modes (s-polarized) where the electric field aligns parallel to
the interface, and TM modes (p-polarized) where the magnetic field aligns
parallel to the interface. As an illustration, let us examine the most basic
geometry suitable for supporting surface plasmon polaritons: a planar
interface between a metal and a non-absorbent half space. We now derive the
dispersion equation governing the propagation of waves within this
arrangement. When considering the confined propagation of SPPs along the
interface of two mediums, namely a conductor and an insulator, it is crucial
to note that there must be a normal component of the electric field with
respect to the surface. Consequently, the existence of s-polarized surface
oscillations is negated, and the analysis should solely focus on TM modes. By
considering propagation along the $x$ direction with a constant propagation
constant $\beta$, and assuming homogeneity by setting
$\frac{\partial}{\partial y}=0$, we can apply Maxwell’s curl equations to
derive the components of propagation. Through this analysis, we find that
$E_{x}$, $E_{z}$, and $H_{y}$ are the only three components that are non-zero
[23], i.e.,

$\displaystyle
E_{x,i}(z)=(-1)^{i}jA_{i}\frac{1}{\omega\varepsilon_{0}\varepsilon_{i}}k_{i}e^{j\beta
x}e^{-k_{i}z},$ (10) $\displaystyle
E_{z,i}(z)=-A_{1}\frac{\beta}{\omega\varepsilon_{0}\varepsilon_{i}}e^{j\beta
x}e^{(-1)^{i+1}k_{i}z},$ (11) $\displaystyle H_{y,i}(z)=A_{i}e^{j\beta
x}e^{(-1)^{i+1}k_{i}z},$ (12)

where $\varepsilon_{2}$ represents a constant value, $\varepsilon_{1}$ is
frequency-dependent, and $i=\\{1,2\\}$ depending on the region. It is
important to note that in order to fulfill the metallic behavior of the
environment, the real part of $\varepsilon_{1}$ must be negative
($Re\\{\varepsilon_{1}\\}<0$). It is essential to acknowledge that in optical
frequencies, the short wavelength of waves enables them to penetrate deeply
into the metal, causing it to exhibit dielectric-like behavior. However, our
intention is to address this phenomenon and prevent it from occurring. By
ensuring that the waves gradually fade and following the condition
$Re\\{\varepsilon_{1}\\}<0$, we can achieve this objective. Lastly, in the
regime of large wave vectors, the angular frequency and wave-vector of SPPs
can be found in (13), and (14), respectively, i.e.,

$\displaystyle\omega_{spp}=\frac{\omega_{p}}{\sqrt{1+\varepsilon_{2}}},$ (13)
$\displaystyle
k_{spp}=\frac{\omega_{spp}}{c}\sqrt{\frac{\varepsilon_{1}\varepsilon_{2}}{\varepsilon_{1}}+\varepsilon_{1}},$
(14)

where $\omega_{p}$ denotes the plasma frequency, as the threshold frequency
for the dielectric behavior of metal.

Taking the surface plasmon polaritons, and consequently, the localized surface
plasmons into the nanoantenna domain, one can comprehend their urgency for
describing the behaviour of nan-scale antennas. SPPs contribute to the
radiation enhancement in nanoantenna through their ability to concentrate
electromagnetic energy, enhance light-matter interactions, facilitate
directional radiation, achieve sub-wavelength resolution, and enable plasmon
resonance tuning [20, 21, 24]. Additionally, Localized Surface Plasmons (LSPs)
contribute to radiation enhancement of nanoantennas through resonant
absorption and scattering, field enhancement, the generation of hotspots,
strong coupling with nearby emitters, and the ability to tune their resonance
frequencies [90, 91, 92, 93, 94, 95]. Hence, SPP and LSP play crucial roles in
designing nanoantennas for various applications, including sensing, imaging,
and light manipulation at the nanoscale, and the physical explanation of their
existence can be managed through quantum mechanics.

Some of the most important ways to excite SPPs include through nanoantennas
[107, 43, 108, 110], prism-coupling [99, 23], grating-coupling [100],
attenuated total reflection (ATR) [101], and nanostructure-enhanced excitation
[102].

Additional excitation techniques, and other structures supporting SPPs
generation have been mentioned in the literature such as, Kretschmann
Configuration [103], Photonic Crystal-Based Excitation [104], Two-Photon
Excitation [105], Nanostructured Metasurfaces [106], which mostly share same
fundamentals or overlap in some certain ways, while focusing on a specific
application. For example, both the Kretschmann configuration and prism-
coupling techniques involve using a prism to couple incident light into a
metal-dielectric interface in favor of SPPs generation, and can sometimes be
used interchangeably. Generally, each excitation method offers unique
advantages and can be tailored for specific applications, ranging from sensing
and imaging to light-matter interactions, and energy harvesting. Since the
field of plasmonics is being explored and getting richer day by day, further
advancements and novel applications are expected for SPPs in the near future.

##  III Nanoantennas and Terahertz Radiation

In this section, we explore the fascinating world of nanoantenna theory and
search through the fundamental concepts of antenna parameters as we uncover
the mechanisms behind these nano-scale structures with profound applications
in various domains.

###  III-A Nano-antenna Theory

The emergence of 5G wireless communications, accompanied by research
advancements over the past fifteen years [125, 126], has assisted the
development of meticulously crafted configurations. These configurations are
tailored to provide the exponentially faster communication rates of the fifth-
generation (5G) and forthcoming sixth-generation (6G) technologies. To enable
their applications in various fields like biosensors, medical devices, energy
harvesting, light manipulation, optical communications, and nanoscale
communications, these configurations need the integration of miniaturized
components at the nanoscale [110]. In order to facilitate these advancements,
it is imperative to design practical broadband antennas for both transmitters
and receivers that can effectively handle these types of communications.
Specifically, these modern antennas need to exhibit compact sizes, low
profiles, high gains, wide bandwidths, and desirable radiation patterns. To
keep pace with the rapid technological progress, nanoantennas are gaining
prominence as they are primarily designed to operate within the terahertz
range, enabling communication rates on the order of terabits per second. The
nanoantenna serves as a crucial component responsible for the collection and
absorption of electromagnetic waves with wavelengths that are proportional to
its physical dimensions. By precisely tuning the size and shape of the
nanoantenna, it becomes capable of effectively capturing and interacting with
electromagnetic waves of specific wavelengths [113]. Typically, a nanoantenna
comprises a ground plane, a resonant cavity, and the transmitter/receiver
section, which is the antenna itself. When electromagnetic waves with a
specific frequency encounter the metal surface, they initiate the generation
of Surface Plasmons (SPs) at the same frequency as the incident waves. The
generated Alternating Current (AC) must be converted into a Direct Current
(DC) to power an external load, as suggested by the transmission-line model.
In other words, the absorbed incident waves are subsequently reflected and
concentrated within the cavity using the ground plane section [109, 113]. As
previously mentioned, the electromagnetic fields E and H are not subject to
any additional conditions beyond the satisfaction of Maxwell’s equations. This
implies that as long as these equations are fulfilled, the propagation of
electromagnetic waves is permitted, irrespective of their frequency.
Nevertheless, classical Maxwell’s equations are rendered inadequate at higher
frequencies, such as in the optical range. This is attributed to the frequency
variability of characteristic parameters in the participating media.
Consequently, electromagnetic waves can penetrate the metal, inducing plasmon
modes with wavelengths shorter than the free-space wavelength ($\lambda_{0}$),
significantly changing the antenna’s properties. [107].
Another difference between the classical and optical antenna theories lies in
the feeding technology of the antennas. In radio wave antennas, impedance-
matched transmission lines and waveguides, such as the coaxial cables, are
being employed to feed these structures [179]. Within the optical regime, the
small dimensions of optical nanostructures make wiring between the antenna and
the feed port (or transmitter/receiver) challenging. In this scenario, the
transmitter or receiver can take the form of molecules, quantum dots, or
tunnel junctions, connecting to the antenna through mechanisms involving
energy or charge transfer [107, 110]. The most important optical antennas that
have been investigated in the literature as shown in figure (1) are metallic
nanowires and nanoloops [180, 181, 182, 183, 184, 185, 186, 212, 213], which
form the main building blocks of other nanoantennas as well, coupled-dipole
antennas [187, 188, 189, 190], bow-tie antennas [195, 196, 197, 198, 199,
200], hertzian dimer antennas [208], nanoparticle antennas [191, 192, 193,
194], yagi-uda nanoantennas [201, 202, 203, 204, 205, 206, 207], cross
antennas [209, 210], and square-spiral nanoantennas [211].

Figure 1: Scanning Electron Microscopy (SEM) images showcasing different types
of optical antennas: (a) coupled-dipole antenna, (b) nanoparticle antenna, (c)
bow-tie antenna, (d) square-spiral antenna, (e) cross antenna, (f) Yagi-Uda
antenna, (g) Hertzian dimer antenna, (h) Nanoloop antenna, (i) Nanowire
antenna. Images (a-g) captured from [107]. TABLE I: Table of existing
nanoantennas for different applications

Structure | Material | Max Size (nm) | Central Wavelength (nm) | Gain (dB)/ Emission Intensity ($\mathbf{10^{6}}$ counts/s) | Bandwidth (nm) | Reference
---|---|---|---|---|---|---
Nanowire | InP | diameter = 100 , length = 3130 | 850 | N.m | 10 | [180]
Nanowire | Gold/Si$O_{2}$ | N.m | 40-1630 | 911 | 25 | [181]
Nanowire | GaAs/InAs | diameter = 220 , length = 2000 | 950 | N.m | 70 | [182]
Nanowire | Au/GaAs | diameter = 220 , length = 2000 | 830 | N.m | N.m | [183]
Nanowire | Gold/ITO | 80 nm and 140 nm arms | 1100 | N.m | around 50 | [186]
Nanoloop | Silver | 500 | 1340 | 8.2 | 10 | [212]
Nanoloop | Gold | 500 | N.m | 7.5 | N.m | [213]
Bow-tie | Gold/Si$O_{2}$/ITO/PMMA | 500 | 820 | N.m | 100 | [195]
Bow-tie | Gold/Si/Cr | 200 | 1100(c$m^{-1}$) | variable | variable | [196]
Bow-tie | Gold/ITO/Glass | 300 | 780 | N.m/0.1 | 50 | [197]
Bow-tie | Gold/ITO/Glass | 575 | 660-808 | N.m | N.m | [198]
Bow-tie | SiC/Glass/GaN/Gold | 600 | 900(c$m^{-1}$) | N.m | 20(c$m^{-1}$) | [200]
Nano-particle | Gold | 60,100 | 740-1170 | N.m |  10 | [191]
Nano-particle | Gold | 20,40,80 | 633 | E.F=40 | 15,23 | [192]
Nano-particle | Gold/Silver | 10 | 680 | N.m | 20 | [193]
Yagi-Uda | Various(e.g. Si) | 150 | 520 | 12 |  50 | [201]
Yagi-Uda | Gold/PC403 | 300 | 1500 | 20(Array),6(Single) | 100 | [202]
Yagi-Uda | Silver/Silica | 150 | 620 | 3 | 25 | [203]
Yagi-Uda | Si | 130 | 490,570 |  8(at $\lambda=500nm$) | 20 | [204]
Yagi-Uda | Gold/Glass/PMMA | 198(feed)/1200(substrate) | 1000 |  9 |  50 | [205]
Yagi-Uda | Silver/a-Si | 390(feed) | 1000 | N.m | 10 | [206]
Yagi-Uda | Gold/Ti$O_{2}$ | 40(feed) | 780 |  7 | 50 | [207]
Cross | Si/quartz/Glass | 200 | 400-700 | N.m | 10 | [209]
Cross | Gold/Glass | 120 |  800 | E.F=40 | 100 | [210]
Square-spiral | Gold/Ti/Ti$O_{x}$ | 5500 | 5-30 $\mu m$ | 2.5 | 10 | [211]
Coupled-dipole | Gold | 110 | 830 | N.m/1.15 | 50 | [187]
Hertzian-dipole | Gold/Silver/$Si_{3}N_{4}$ | 150 | 500 | 1.46 |  30 | [208]

In this table, ”N.m” indicates that the information is not mentioned in the
specific reference. However, it is possible to deduce or estimate some values
based on our background knowledge. For example, we might infer that a nanowire
antenna provides a gain of around 2 dB. Additionally, in the sixth column,
”E.F” represents the Efficiency Factor, measuring the nanoantenna’s
enhancement efficiency— a parameter assessing the structure’s ability to
enhance light-matter interactions.

Relying on these fundamental concepts, we may now define and look into the
nanoantenna parameters in the next section.

###  III-B Antenna Parameters

The evaluation metrics for optical nanoantennas closely are similar to those
of classical antenna structures. However, due to the plasmonic effect and
shorter wavelengths involved, they require recalibration. Specifically, this
recalibration can be applied to any frequency band where quantum effects
cannot be disregarded. Consequently, the following equations and definitions
can be extended to other frequencies with the inclusion of specific
considerations, if necessary.

####  III-B1 Radiation Pattern

The radiation pattern provides a visual representation of an antenna’s
radiation properties in terms of its spatial coordination. Typically presented
graphically, the pattern is defined within the spherical coordination system,
i.e.,

$\displaystyle P_{rad}=\int_{0}^{\pi}\int_{0}^{2\pi}p(\theta,\phi)sin\theta
d\phi d\theta,$ (15)

where $p(\theta,\phi)$ is the normalized power density. Once the necessary
calculations have been performed, we can plot the radiated power as a function
of $\theta$ or $\phi$ and analyze and report the resulting radiation pattern.

####  III-B2 Directivity

Every antenna design aims to selectively transmit or receive propagations in
specific directions while minimizing the influence of waves coming from other
orientations. The directivity metric quantifies the antenna’s ability to
achieve this objective. It measures the antenna’s performance in focusing
transmitted or received waves. The directivity can be calculated within the
spherical coordinate system using

$\displaystyle D(\theta,\phi)=\frac{4\pi}{P_{rad}}p(\theta,\phi).$ (16)

It is worth noting that the directivity can be defined independently for each
axis, meaning that separate partial directivities can be calculated for the
$\theta$-axis and the $\phi$-axis.

####  III-B3 Effciency

Generally, the efficiency of an antenna is defined as

$\displaystyle\eta_{rad}=\frac{P_{rad}}{P_{rad}+P_{loss}},$ (17)

in which the $P_{rad}$ , and $P_{loss}$ are the radiated power and the power
dissipated to heat, respectively. The total power can be calculated utilizing
the oscillating electric field E of the nanoparticle transmitter and the
Poynting Vector, which determines both the value and the direction of power
dissipation. At the same time, the $P_{rad}$ is radiated from the entire nano-
system, i.e., both the nanoantenna and the nanoparticle.

####  III-B4 Gain

The gain of an antenna can be defined as the ratio of the intensity in a
specific direction (typically chosen as the direction of maximum radiation or
maximum directivity) to the total input power dissipated by a hypothetical
lossless isotropic reference antenna, whose gain is known, i.e.,

$\displaystyle G=\frac{4\pi}{P}p(\theta,\phi)=\eta_{rad}D,$ (18)

where $p(\theta,\phi)$ is the normalized angular power density. A commonly
used choice for the reference antenna is a dipole. For example, if we consider
a nanoparticle with a current density represented by $J(r,t)\approx
Re\\{J(r)e^{-j\omega t}\\}$ (which can be approximated as an oscillating
dipole centered at the point-charge distribution of the nanoparticle) and a
dipole moment denoted as $p(t)\approx Re\\{pe^{-j\omega t}\\}$, we can
calculate the radiated power using [107]

$\displaystyle
P=\frac{|p|^{2}}{4\pi\varepsilon_{0}\varepsilon}\frac{n^{3}\omega^{4}}{3c^{3}}.$
(19)

To calculate the normalized radiation pattern, we take into account that $n$
represents the dispersion-free index of refraction, and must be one for the
sake of the causality of the system. Additionally, when considering the unit
solid angle as $d\Omega=\sin\theta d\theta d\phi$, the normalized radiation
pattern can be expressed as

$\displaystyle P_{n}=\frac{3}{8\pi}sin^{2}\theta,$ (20)

where $P_{n}$ denotes the normalized radiation pattern as visually shown in
Fig. 2. Consequently, we may calculate the directivity of this particular
antenna using (18).

(a) Single nano-particle (b) 4-elements array of nano-particles

Figure 2: Normalized Radiation Pattern : (a) A single nanoparticle functioning
as a radiative nanodipole. As anticipated, this miniature component exhibits
low gain and low directivity. (b) Four nanodipoles, when the maximum radiation
in $\theta=0^{\circ}$ is desired ($\beta=-kd$). As expected, there is a
noteworthy improvement in both directivity and gain, although the presence of
null points is unavoidable.

####  III-B5 The Local Density of States

One notable distinction between classical and optical antenna theory lies in
the definition of input impedance. In the context of optical nanoantennas, the
concepts of current and voltage lack clear definitions, as the source is not
directly connected to the antenna. Instead, since the power source is
typically an emitter in the optical regime, we can calculate the Local Density
of States (LDOS) of the antenna. LDOS can be used to determine the density (or
number) of states for the emitted photon out of the transmitter to occupy,
allowing us to assess how its energy is dissipated [108]. As per this
definition, a higher value of LDOS corresponds to better antenna performance.
Thus, to enhance power dissipation, an optical nanoantenna must be optimized
with higher values of LDOS. For instance, when considering a dipole emitter,
we can calculate the LDOS using

$\displaystyle\rho_{p}(r_{0},\omega)=\frac{12\varepsilon_{0}}{\pi\omega^{2}}\frac{P}{|p|^{2}},$
(21)

where $r_{0}$ and $P$ denote the location of dipole $p$, and the total
radiated power, respectively [114].

####  III-B6 Radiation Resistance

The radiation Resistance is defined as

$\displaystyle R_{rad}=\frac{P_{rad}}{I_{max}^{2}/2},$ (22)

where $R_{rad}$, $P_{rad}$, and $I_{max}$ are the radiation resistance, the
radiation power, and the maximum current in the antenna’s circuit,
respectively. As explained earlier, the antenna element can be represented as
a load in the circuit model. In this context, the radiation performance of the
antenna increases with a larger radiation resistance. Specifically, for a
dipole antenna, the radiation resistance can be expressed as

$\displaystyle R_{rad}=\frac{2\pi}{3}Z_{0}(\frac{\Delta l}{\lambda})^{2},$
(23)

where $\Delta l$ is the antenna length, $Z_{0}\approx 377\Omega$ is the wave-
impedance in free space [107].

####  III-B7 Antenna’s Effective Wavelength

When constructing an antenna, the design rules in both radio wave frequencies
(including millimeter waves) and optical range illustrate a correlation
between the operational frequency and the physical dimensions of the antenna.
For instance, a half-wave antenna’s length can be determined as
$\Delta\ell=\frac{1}{2}\lambda$. In the case of array antennas, achieving
maximum array efficiency requires careful spatial placement of the elements.
This involves positioning the elements at distances proportional to the
wavelength. A good example is the Hansen-Woodyard End-Fire Array, where the
spacing parameter $d$ is given by $d=\frac{N-1}{N}\frac{\lambda}{4}$. Here,
$N$ represents the number of array elements, and $d$ represents the required
spacing between them [18]. In general, the essential dimensions of antennas
can be expressed as $\ell$ = (constant value) $\times$ $\lambda$, emphasizing
the linear relationship between antenna length ($\ell$) and the wavelength of
radiation. However, in the case of optical frequencies, modifications need to
be made as the previously mentioned equation no longer holds. This is because
the PEC approximation is no longer valid at these frequencies. Consequently,
at optical frequencies, the antenna’s response to incident waves changes due
to the presence of a shorter effective wavelength $\lambda_{eff}$. This
effective wavelength is determined by the material properties of the antenna,
including the plasma frequency, conductivity, and penetration depth, i.e.,

$\displaystyle\lambda_{eff}=n_{1}+n_{2}\frac{\lambda}{\lambda_{p}},$ (24)

where $n_{1}$ and $n_{2}$ are constants depending on the antenna geometry, and
$\lambda_{p}$ is the plasma wavelength [116].
Calculating $\lambda_{eff}$ according to (24) can be a complex task, often
requiring the use of numerical and experimental methods. However, the
linearity of this equation indicates that classical antennas can theoretically
be linearly miniaturized into optical antennas. This miniaturization can be
achieved by utilizing a Scaling Ratio (SR), i.e.,

$\displaystyle SR=\frac{\lambda_{eff}\lambda_{1}}{\lambda_{2}},$ (25)

where $\lambda_{1}$ represents the operational frequency of the optical
antenna, and $\lambda_{2}$ corresponds to the operational frequency of its
classical counterpart [107].

In the following section, we inquire into the current applications of
nanoantennas, while also introducing an emerging application, namely nano-
scale radar systems.

##  IV Applications of Nanoantennas

###  IV-A 6G Wireless Communications

Building upon the advancements of previous generations, such as 1G
(commercialized in the 1980s) which provided limited voice calling
capabilities and limited transfer rates, to the current development and
commercialization of 5G in the 2020s, which offers applications for the
Internet of Things (IoT) and massive broadband services with significantly
higher rates of up to 10 Gbps, the forthcoming 6G is anticipated to assist in
developing a fully-digital world with hyper data transfer rates of up to 1
Tbps. To highlight the significance of 6G, similar Key Performance Indicators
(KPIs) or characteristics will be employed, as outlined in Table (II). When
compared to the current most powerful existing generation, i.e., 5G, 6G offers
noteworthy advancements. It is expected to be 100 times more reliable,
possessing increased stability and dependability for various applications.
Additionally, 6G offers data transfer rates that are 50 times faster, enabling
even more efficient communication. Furthermore, 6G reduces latency by 10
times, resulting in significantly faster response times for in-time
applications. These improvements in reliability, data transfer rate, and
latency introduce 6G as a highly promising technology for future communication
systems [253, 254, 255].

TABLE II: Comparison between 6G and 5G KPIs [252, 249] KPI |  5G |  6G
---|---|---
Peak Data Rate |  $20$ $Gbps$ |  over $1$ $Tbps$
Experienced Data Rate |  $100$ $Mbps$ |  $1$ $Gbps$
Latency |  $1$ $ms$ |  $10-100$ $\mu s$
Jitter |  Not Specified |  lower than $1$ $\mu s$
Enhanced Energy Efficiency |  Not Specified |  $1$ $pJ/b$
Reliability |  Error rate $<10^{-5}$ |  Error rate $<10^{-7}$
Enhanced Spectral Efficiency |  around $30$ $b/s/Hz$ |  $100$ $b/s/Hz$
Connection Density and Mobility |  $500$ $km/h$ |  beyond $1000$ $km/h$

To enable the advanced capabilities of 6G, it is essential to identify a
suitable operational frequency range within the spectrum and establish a well-
aligned infrastructure. The most promising and untapped frequency range for 6G
implementation, which can support faster data transfer rates and wider
bandwidth, is known as the Terahertz Gap. This frequency range lies between
0.1 to 10 THz and offers exceptional potential for exceeding the limits of
wireless communication in terms of speed and capacity. By exploring the
capabilities of the Terahertz Gap, 6G can unlock new opportunities for hyper-
connected and high-speed digital applications. The choice of the Terahertz Gap
as the preferred frequency range for 6G implementation stems from several
fundamental reasons. These reasons include limitations in the sub-6GHz band
due to spectrum scarcity, insufficient bandwidth available in the millimeter
wave (mmWave) range, constraints associated with the optical bands, and
potential adverse effects of higher frequencies on the human body. These
factors collectively highlight the need to explore alternative frequency
ranges, such as the Terahertz Gap, to overcome these limitations and drive the
development of 6G technology [119]. Terahertz waves possess several
advantageous characteristics, making them well-suited for a range of
applications. Their abilities include high resolution, the capability to
penetrate non-conductive materials, sufficient bandwidth, and non-destructive
properties—especially beneficial for medical purposes like cancer detection.
These properties enable terahertz waves to provide important benefits and find
applications in wireless communications and the upcoming 6G era. Along with
many other usages, terahertz waves find applications in THz radars and
sensing, enabling advanced navigation, collision avoidance for autonomous
vehicles, and improved security screening [138]. They also offer wireless
backhaul solutions, supporting the increasing demand for data transfer between
base stations and core networks [139]. On the other hand, terahertz waves do
face some limitations that should be taken into consideration. One of these
limitations is the current lack of high-power THz wave transmitters. This
means that the transmission distance of terahertz waves can be limited,
affecting their range in certain applications. Additionally, terahertz waves
are sensitive to high absorption coefficients caused by molecular absorptions
in the propagation environment. This means that when terahertz waves interact
with objects and obstacles, they can experience significant signal loss. This
absorption phenomenon limits the ability of terahertz waves to penetrate
certain materials and reduces their effectiveness in certain scenarios. To
compensate for these limitations and harness the exceptional features of 6G
communications, the implementation of highly directional antennas with
sufficient gains and bandwidth is a must. These specialized antennas play a
crucial role in overcoming the drawbacks associated with terahertz waves,
enabling enhanced range and coverage. By offering high directivity, they focus
the transmission beam in a specific direction, improving the efficiency of
terahertz communication systems. Additionally, the large gains provided by
these antennas boost signal strength, facilitating long-range communication
capabilities. Furthermore, their wide bandwidth capabilities accommodate the
high data rates required in 6G communications, supporting bandwidth-intensive
applications and ensuring optimal performance. In this domain, antennas that
operate in the frequency range of 0.1 to 10 THz are typically extremely small,
often measuring in the nanoscale or sub-wavelength scale. Due to the reduced
wavelength of electromagnetic waves within this range (ranging from 0.1 to 1
millimeter), conventional antennas designed for 4G or 5G communications (below
100 GHz) become ineffective. Hence, the field of antenna engineering and
technologies faces numerous challenges in order to surpass these constraints
and unlock the full potential of 6G applications, necessitating the
utilization of nanoantennas. Terahertz antennas as the crucial components with
the capability of addressing the challenges posed by 6G communications, can be
divided into three major groups based on the manufacturing material: metallic
or plasmonic antennas, novel antenna structures such as Metamaterial
(MtM)-based and Graphene-based antennas, and dielectric antennas, which are
outlined below, and compared in Table (III).

Antenna Type | Gain (dBi) | Compatibility | Average Size | Fabrication Difficulties
---|---|---|---|---
Metallic Antennas | High 20-30 | Limited | Sub-millimeter | Low
Dielectric Antennas | Moderate 2-25 | Broad | Micrometer | Moderate
Graphene-based Antennas | Extremely low $<1$ | Limited | Nanometer | Moderate
Metamaterial-based Antennas | Moderate 2-5 | Limited | Sub-millimeter | High

TABLE III: Comparison Between Various Terahertz Antennas : [149, 150, 151,
152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166,
167, 168, 169, 170]

Metallic antennas, Dielectric antennas, and New-material-based antennas
(Graphene and Metamaterials). It highlights the key metrics such as lower cost
and production complexity for metallic antennas, easy and advanced integration
for dielectric antennas, and compatibility of new-material-based antennas with
nano-channels.

####  IV-A1 Metallic Antennae

Metallic antennas are manufactured using noble metals such as gold and silver,
as well as copper, aluminium, or metal alloys that may include small
percentages of palladium or platinum to improve stability and durability
[129]. These antennas operate based on the principle of plasmonics, offering
advantages such as low manufacturing cost, small sizes, and simple geometries.
However, a major disadvantage of metallic antennas is their limited
compatibility with planar structures, making it challenging to incorporate
them into modern technologies. Furthermore, their use of mechanical adjustment
methods for antenna positioning can reduce the overall performance [129]. An
example of a metallic antenna is the Horn antenna, which may feature a tapered
or conical design [149, 150]. Another instance is the travelling-wave corner
cube antenna, which is characterized by simple open structures, low
manufacturing requirements, and low coupling efficiencies [151, 152]. Despite
their limitations, these types of antennas serve as practical options within
the realm of metallic antenna technology.

####  IV-A2 Dielectric Antennae

Dielectric antennas possess several significant characteristics, including low
input impedance, ease of fabrication and integration, and low cost [129].
Unlike metal antennas, dielectric antennas have minimal free-electron
movement, resulting in reduced losses and improved propagation properties.
Various geometries, such as butterfly, dual U-shaped, logarithmic periodic,
log periodic sinusoidal, and bent-wire configurations, have been proposed
using genetic algorithms [129, 153]. However, one notable drawback of these
antennas is the presence of surface wave effects. These effects occur when the
radiation angle exceeds the cutoff angle, leading to trapped energy in the
dielectric substrate and high energy loss. To overcome this issue, techniques
such as loading lenses, reducing substrate thickness, and replacing the
dielectric material with electromagnetic bandgap (EBG) structures have been
employed [154].

(a) Controlled drug-delivery from LTSLs by photothermal heating from MGNs in
triple-negative breast cancer cells. The near infrared irradiations of the
laser source with $\lambda=808$nm is converted to heat by the multibranched
gold nanoantennas (MGNs), and doxorubicin (DOX) is delivered from the LTSLs in
breast cancer cells [228].

(b) Ultrasensitive detection of Ebola virus (EBOV) antigens using a 3D
plasmonic nanoantenna-based sensor as an on-chip immunoassay platform, for
early-stage disease detection. The structure containing silica-based pillars
between the gold nanodisks and backplane forms nanocavities, that can absorb
laser irradiations at a certain wavelength. The figure on right, depicts the
structure of EBOV sandwich assay on the nanoantenna array platform [229].

(c) Bull’s eye structure with and without dot radiator, capable of forming a
high-intensity focused beam and polarization-independent extraordinary optical
transmission (EOT), applicable to biosensors. The figure on the bottom-left
demonstrates the focused light beam from bull’s eye structure without, and the
middle-one illustrates the focused light beam with the existence of a dot
radiator, respectively. The figure on the bottom-right shows the electric
field amplitude at $\lambda=800$nm, with and without the dot radiator [231].

Figure 3: Different examples of using nanoantennas in medical applications

####  IV-A3 Graphene-based and MtM-based Antennas

Currently, there are interesting developments in material science that propose
using carbon nanotubes and metamaterials for new and improved dipoles. These
innovations offer significant advancements in radiation performance compared
to traditional options. For example, carbon nanotube dipoles exhibit
resonances within a specific frequency range below the terahertz gap but
rapidly lose effectiveness outside this range [155, 156]. Another promising
option is bundles of carbon nanotubes wrapped in two dielectric layers,
outperforming single-walled carbon nanotubes [157]. To further enhance
bandwidth radiation characteristics and enable the fabrication of integrated
antennas, researchers are exploring the application of two-dimensional planar
materials, such as graphene, which possesses exceptional electrical
properties. Graphene allows for dynamic control by adjusting bias voltage and
facilitates the production of surface plasmonics. In contrast to graphene,
metals cannot naturally couple with and convert electromagnetic waves in free
space, leading to rapid attenuation of surface plasmon polaritons (SPPs)
parallel to the metal-substrate interface [158]. Consequently, metallic
antennas, which are subject to skin effects and have limited size, fail to
meet the requirements of THz antennas. However, graphene exhibits a wide range
of light absorption and regulation, emphasizing in-band transition dominance
in the terahertz band. The collective oscillation of plasma in graphene
enables excellent surface plasmon material properties, characterized by
stronger binding and lower loss. Moreover, graphene allows for continuous
electrical tuning [159, 161, 162]. Indeed, further research on new antenna
materials will be crucial for achieving optimal performance. These novel
materials can overcome traditional antennas’ limitations, presenting
advantages such as high gain, wide bandwidth, low loss, and cost-
effectiveness. Therefore, alongside graphene, it is also worth exploring
metamaterials and other two-dimensional materials in antenna design [169,
170].

Typically, terahertz antennas discussed in the existing literature are on the
scale of micrometers to millimeters. In other words, practical antennas that
operate in the terahertz frequency range and are at the nano-scale are rarely
investigated in current literature, which implies a high need for further
research and development to manufacture nanoantennas suitable for terahertz
communication. Most of the terahertz nanoantennas are constructed using
graphene, but they face challenges such as low gains and efficiencies. For
instance, the use of bundled single-walled carbon nanotubes (SWCNTs) in the
novel technology proposed in [156] aims to shift the operational frequency of
the nano-dipole from higher values within the optical range to the terahertz
gap. However, this approach exhibits a low performance in various radiative
aspects, making it impractical for wireless communication components.
Consequently, there is a critical need for advancements in the development of
nanoantennas that can overcome these limitations and increase their
feasibility for terahertz communication systems.

###  IV-B Nanoantennas in Medical Applications

Nanotechnology has opened the way for novel advancements in the field of
medicine, and one of its most fascinating components is the utilization of
nanoantennas. These tiny structures, designed to interact with light on the
nanoscale, have emerged as promising tools for revolutionizing medical
applications in different ways such as, imaging, biosensing and disease
detection, drug delivery, photodynamic therapy (PDT), real-time monitoring,
and photothermal therapy [234, 229, 230, 231, 232, 233, 228, 235, 236, 237].
For instance, nanoantennas are harnessed for innovative drug delivery
strategies. Exploiting their plasmonic properties, they can generate localized
heat when exposed to near-infrared light. As proposed by Yu-Chuan Ou et al.
[228], this controlled photothermal effect triggers the release of therapeutic
agents from liposomal drug carriers, like low-temperature-sensitive liposomes
(LTSLs). By co-delivering multibranched gold nanoantennas (MGNs) and LTSLs,
targeted drug delivery is achieved with minimal damage to healthy tissue. The
MGNs’ unique geometry enhances light-to-heat conversion efficiency, enabling
controlled drug release. This approach offers several advantages: precise drug
delivery at the tumor site, noninvasiveness, and improved therapeutic efficacy
in aggressive conditions like triple-negative breast cancer. MGN-mediated
photothermal hyperthermia overcomes multidrug resistance and enhances drug
delivery. This combination of nanoantennas and LTSLs opens avenues for
clinically relevant noninvasive drug delivery platforms with potential to
revolutionize cancer treatment [228]. Nanoantennas have emerged as a
groundbreaking tool in molecular sensing and detection as well, as exemplified
by Zang et al., in [229], when in the context of detecting the Ebola virus
(EBOV) antigen, the significance of using nanoantennas is of greatest
importance. The urgency to combat highly lethal pathogens like Ebola
underscores the need for precise and sensitive diagnostics. Current methods,
such as reverse transcriptase polymerase chain reaction (RT-PCR) and
immunoassays, pose limitations in terms of sensitivity and controlled
environments, and this is where nanoantennas shine. These plasmonic
nanostructures possess the unique ability to interact intensely with
biological elements due to their nanoscale characteristics. Through innovative
techniques like nanoimprint lithography, Zang et al. have constructed 3D
nanoantenna arrays. These structures exhibit optical resonance, magnifying
fluorescence signal levels for early antigen detection. This leap in
sensitivity is staggering, when detection of EBOV soluble glycoprotein (sGP)
in human plama down to $220$ fg m$L^{-1}$ is achieved, with improvements of up
to 240,000-fold compared to the $53$ ng m$L^{-1}$ EBOV antegien detection
limit of the standard immunoassays. The scalable fabrication process further
enhances their applicability, aligning with established assay formats. Zang’s
work showcases the transformative potential of nanoantennas, not only for
Ebola but as a universal platform for diagnosing an array of diseases with
unparalleled sensitivity and precision [229].

Numerous other publications within the literature demonstrate various
applications involving nanoantennas and nanostructures in the realm of medical
science. One such instance emphasizes on the employement of dielectric dot
radiators, represented by the optical dot antenna (ODA), as a flexible way for
arranging electromagnetic properties, thereby supporting transmission and
directivity within bull’s eye structures applicable to biosensors and
nanophotonics [231]. Equally notable is the integration of plasmonic
nanoantennas, manufactured by the indium-tin oxide (ITO) nanorod arrays,
which, as an alternative to conventional plasmonic materials like gold and
silver, avoid their inherent limitations—such as high loss and cost [234].
These examples are only a few out of many in the literature. However, it
remains evident that further dedicated research endeavors are required to
develop the maturity of this field.

###  IV-C Nanoantennas for Enhanced Sensing in Nanoradar Systems

Figure 4: The NR system Block Diagram comprises seven main blocks: Antenna and
Transceiver responsible for wave transmission and reception, Signal Processing
Unit for processing and analysis of received signals, Monitoring for
displaying and visualizing processed radar data and other information, Sensors
for providing feedback and control signals to the monitoring unit, Integrated
Circuits (ICs) for controlling actuator movements and receiving data from
sensors, and Resonator for ensuring frequency stability. All of these
components can be integrated into a unified processor unit.

####  IV-C1 Discussions

A radar system is an electromagnetic technology employed for the detection and
tracking of objects, specifically those in motion. This technology involves
transmitting electromagnetic waves and subsequently analyzing the signals that
are reflected or backscattered by the objects. Through this process, a radar
system can unveil various characteristics of the objects, including their
distance, speed, direction of movement, as well as material and physical
properties. This system typically employs essential components such as
antennas or transceivers, along with processing units and monitoring units.
These components collaborate with each other, to interpret the received
signals from the targets, thereby enabling the determination of their
respective locations, speeds, and other relevant features. At present, there
is limited literature available on nanoradars, with only a few notable works
being published [44, 49, 50, 51]. In addition to these limited works, there
have been various discussions on sensing, imaging, and diverse forms of nano
detections in the literature [52, 53, 54, 55, 56]. These discussions provide
valuable insights that can contribute to the development of a nanoradar
system. Integrating these discussions with the existing works on nanoradars
will help establish a more comprehensive understanding of the concept. In this
section, we explore potential methodologies for nanoradar systems. Generally
speaking, A Nano-scale Radar (NR) is a compact system designed to track
minuscule entities, such as molecules or ions, at the nanoscale level, with
the unique aspect of operating in optical frequencies, and to perform this
task, they must be capable to detect and analyze the back-reflected/back-
scattered waves from the targets efficiently. Unlike conventional radar
systems, which explore the transmission, reflection, and reception of signals
based on the Doppler Effect—a domain well-explored in literature—important
challenges can occur at the nanoscale. Specifically, due to the reliance on
the optical and quantum properties of targets for reception and detection,
such as scattering properties of the nanoparticle, a designed nanoradar might
exhibit reliable performance in detecting one specific type of molecule yet
struggle to discern another, while these optical properties can vary
noticeably from one material to another. In other words, an anticipated
potential NR system must be well-designed and optimized for only one
particular application, in one particular nano-channel. In order to study the
scattering properties of nanoparticles different methods, such as Mie-Theory
or Rayleigh-Gans-Debye-Theory are proposed [45, 46, 47, 48]. These theorems
investigate the backscattered light (or electromagnetic waves) from the nano-
objects, by taking into account their sizes (relative to the wavelength of
operation), shapes, and refractive indices. The Mie theory is a comprehensive
approach for calculating light scattering by particles of all sizes, involving
complex calculations based on Bessel functions and spherical harmonics [45].
It provides accurate results for various particle sizes and materials.
Mathematically speaking, let’s consider an abstract and simplified scenario,
where there is only one single spherical scatterer existing in the
environment, exposed by a dipole nano-radiator. In this case, we can find
closed-mathematical expressions for the incident and scattered electric fields
[45, 46], i.e.,

$\displaystyle
E_{i}(\textbf{r},\omega)=\sum_{\ell=0}^{\infty}\sum_{n=-\ell}^{\ell}(p_{\ell
n}N_{\ell n}^{(1)}(k\textbf{r})+q_{\ell n}M_{\ell n}^{(1)}(k\textbf{r})),$
(26) $\displaystyle
E_{s}(\textbf{r},\omega)=\sum_{\ell=0}^{\infty}\sum_{n=-\ell}^{\ell}(a_{\ell
n}N_{\ell n}^{(3)}(k\textbf{r})+b_{\ell n}M_{\ell n}^{(3)}(k\textbf{r})),$
(27)

where $N_{\ell n}^{(1)}$ and $M_{\ell n}^{1}$ are the vector spherical
harmonic basis

functions for electric (TM), and magnetic (TE) harmonics, respectively, and
$a_{\ell n}$, $b_{\ell n}$, $p_{\ell n}$, and $q_{\ell n}$ are the
coefficients, that must be calculated. Here, superscript $(1)$ means that the
coefficients are captured from spherical Bessel functions. If the nano-
particle is located at $\textbf{r}_{0}$, the scattering coefficients $a_{\ell
n}$ and $b_{\ell n}$ can be calculated, i.e.,

$\displaystyle a_{\ell
n}=(-1)^{n}\frac{\textit{i}\alpha_{\ell}k^{3}}{\varepsilon}\frac{2\ell+1}{\ell(\ell+1)}N_{\ell(-n)}^{(3)}(k\textbf{r}_{0}).\textbf{p},$
(28) $\displaystyle b_{\ell
n}=(-1)^{n}\frac{\textit{i}\beta_{\ell}k^{3}}{\varepsilon}\frac{2\ell+1}{\ell(\ell+1)}M_{\ell(-n)}^{(3)}(k\textbf{r}_{0}).\textbf{p},$
(29)

where p, $\alpha_{\ell}$, $\beta_{\ell}$, $k$, and $\varepsilon$ are dipole
moment, Lorentz-Mie single sphere coefficients, wavenumber, and the dielectric
function, respectively. Here, superscript $(3)$ means that the coefficients
are captured from spherical Hankel functions. The computational cost, for
calculating these coefficients can be dramatically high, which makes finding
the scattering fields extremely difficult, specifically when a more extended
scenario is studied with assuming multiple nano-particles in the environment,
i.e.,

$\displaystyle a_{\ell
n}^{i}=\alpha_{\ell}^{i}\\{\frac{\textit{i}k^{3}}{\varepsilon}N_{\ell(-n)}^{(3)}(k\textbf{r}_{0}^{i}).\textbf{p}$
(30) $\displaystyle+\sum_{\begin{subarray}{c}j=1\\\ j\neq
i\end{subarray}}^{N_{s}}\sum_{\ell^{\prime}=1}^{\infty}\sum_{n^{\prime}=\ell^{\prime}}^{\ell^{\prime}}[a_{\ell^{\prime}n^{\prime}}^{j}A_{\ell
n\ell^{\prime}n^{\prime}}^{(3)}(k\textbf{R}^{ji})+a_{\ell^{\prime}n^{\prime}}^{j}B_{\ell
n\ell^{\prime}n^{\prime}}^{(3)}(k\textbf{R}^{ji})]\\},$ $\displaystyle b_{\ell
n}^{i}=\beta_{\ell}^{i}\\{\frac{\textit{i}k^{3}}{\varepsilon}M_{\ell(-n)}^{(3)}(k\textbf{r}_{0}^{i}).\textbf{p}$
(31) $\displaystyle+\sum_{\begin{subarray}{c}j=1\\\ j\neq
i\end{subarray}}^{N_{s}}\sum_{\ell^{\prime}=1}^{\infty}\sum_{n^{\prime}=\ell^{\prime}}^{\ell^{\prime}}[b_{\ell^{\prime}n^{\prime}}^{j}B_{\ell
n\ell^{\prime}n^{\prime}}^{(3)}(k\textbf{R}^{ji})+b_{\ell^{\prime}n^{\prime}}^{j}B_{\ell
n\ell^{\prime}n^{\prime}}^{(3)}(k\textbf{R}^{ji})]\\},$

where $A_{\ell n\ell^{\prime}\acute{n}}^{(3)}$ and $B_{\ell
n\ell^{\prime}n^{\prime}}^{(3)}$ are vector harmonic addition coefficients to
model the couple between $i^{\text{th}}$ and $j^{\text{th}}$ spherical nano-
particles, and $\textbf{R}^{ji}=\textbf{r}-\textbf{r}_{0}$. Note that in the
case of a plane-wave excitation, $m=\pm\ell$, which is not valid for dipole
excitation. In contrast to the Mie theory, the Rayleigh-Gans-Debye (RGD)
theory simplifies scattering calculations for small particles (radius $\ll$
wavelength) by assuming isotropic polarizability and straightforward
scattering patterns [47]. The RGD approximation is applicable under specific
conditions, i.e.,

$\displaystyle|n-1|\ll 1,$ (32) $\displaystyle kd|n-1|\ll 1,$ (33)

where $d$ is the linear dimension of the particle, $n$ stands for the relative
complex refractive index of the particle, with respect to the surrounding
medium, and $k$ is the wavevector. Considering an arbitrary shaped particle
exposed to a plane wave radiation in z direction, the parallel and
perpendicular components of the scattered electric field can be calculated
using the scattering matrix [47, 48], i.e.,

$\displaystyle\begin{pmatrix}\Delta E_{\parallel scat}\\\ \Delta E_{\perp
scat}\end{pmatrix}=\frac{exp(\textit{i}k(r-z))}{-\textit{i}kr}\begin{pmatrix}S_{2}&0\\\
0&S_{1}\\\ \end{pmatrix}\begin{pmatrix}E_{\parallel inc}\\\ E_{\perp inc}\\\
\end{pmatrix},$ (34)

where $E_{\parallel scat}$, $E_{\perp scat}$, $E_{\parallel inc}$, and
$E_{\perp inc}$ are the parallel and perpendicular components of the
scattering, and incident fields, respectively. Also, the scattering matrix
elements needs to be calculated separately, i.e.,

$S_{1}=-\frac{\textit{i}k^{3}}{2\pi}(n-1)Vf(\theta,\phi),$
$S_{2}=-\frac{\textit{i}k^{3}}{2\pi}(n-1)Vf(\theta,\phi)cos(\theta),$

$V$ is the volume of the particle, and $f(\theta,\phi)$ is the form factor,
i.e.,

$\displaystyle f(\theta,\phi)=\frac{1}{V}\int_{V}e^{\textit{i}\delta}d\nu,$
(35)

where $\delta$ is the phase, and can be calculated for different cases, e.g.,
for a homogeneous sphere $\delta=2k\xi sin(\frac{\theta}{2})$, where the
variable $\xi$ is the distance from the origin to a plane of constant phase.
Note that, in the case of a heterogeneous particle including $j$ homogeneous
regions, (34) must be generalized by calculating $S_{1}$ and $S_{2}$ for each
region, and summing up the results to achieve the final terms of the
scattering matrix elements.
RGD theory is suitable for quick estimations but lacks accuracy for larger
particles, while Mie theory covers a broader range of particle sizes and
materials with more accurate predictions. Table (IV) provides a brief
comparison between these two. Due to the noticeable computational costs
associated with both Mie and Rayleigh-Gans-Debye approaches, it becomes
essential to use software tools such as Ansys Lumerical or COMSOL to fully
simulate the entire procedure physically, or MATLAB to explore the patterns of
back-scattered electromagnetic waves numerically.

(a) Scattering intensity by Mie theory (b) Scattering intensity by RGD theory

Figure 5: (a) The intensity of the back-reflected electromagnetic waves as a
function of the scattering angle, for a homogeneous particle having a radius
approximately equivalent to the incident light’s wavelength (around 500 nm and
428 nm, respectively). In this case, (28) can be used. (b) The intensity of
the scattered waves as a function of the scattering angle, for a homogeneous
spherical particle with a radius much smaller than the incident light’s
wavelength, i.e., r $\ll\lambda$ (around 50 nm, and 428 nm, respectively). In
these figures, RRI is the abbreviation of Relative Refractive Index, and
denotes the ratio of the refractive index of the nano-particle to the
surrounding medium.

####  IV-C2 Simulations and Analysis

In this section, MATLAB has been used to find the scattering coefficients, and
investigate the scattering patterns, and the results are depicted in Figure 5.
In this context, consider an optical nano dot functioning as a transmitting
antenna, emitting at a frequency of $700$ THz (wavelength = 428 nm), within a
nano-channel filled with Air ($\varepsilon=1$). The objective is to detect a
single homogeneous spherical object under two distinct scenarios. In one
scenario, the nanoparticle’s radius is set at $50$ nm, i.e., the Mie theory
must be applied, while in the other, is adjusted to $500$ nm, i.e., RGD theory
is employed. The key difference between these two cases is the ratio of the
target’s size to the operational wavelength. In the first scenario, size $\ll$
wavelength is met, allowing for the utilization of the Rayleigh-Gans-Debye
approximation. Conversely, in the second scenario, where this condition does
not hold, the more sophisticated Mie’s algorithm must be employed to
accurately model the scattering phenomenon. As shown in Figure 5, due to their
minuscule dimensions, nano-particles exhibit limited capability in reflecting
light with high intensities. Consequently, the received signals (echo-signals)
are weak, specially within certain scattering angle intervals, such as between
50 to 150 degrees, which makes the process of detecting and analyzing the
back-reflected signals, practically complex for the processing unit of the
nanoantenna. One way to overcome this issue, is to increase the refractive
index of the particle, as its inherent physical aspect. This index signifies
the ability of the nano-particle to repel incident waves. Notably, an increase
in the refractive index often leads to enhanced scattering properties of the
particle, which makes it more feasible for the radar to correctly detect the
nano-particle. In radar systems, received signals often include both echoes
and undesired signals, known as noise. A simple method to mitigate noise
effects is through threshold detection. This involves selecting an appropriate
threshold value based on the signal’s strength. This threshold helps
distinguish between received signals that could be representive of a target’s
presence and those that are mere noise. According to Figure 5, only specific
refractive indices within certain scattering ranges exhibit a noticeable
distinction between potential system noise and a valid echo signal. However,
it is crucial to note that the effectiveness of the processing unit holds
significant importance. This unit is responsible for carrying out subsequent
analysis of received echo signals, which includes tasks like determining a
reliable threshold value, detecting the presence of a target, and even
extracting its electrical properties, such as permittivity. As an instance, in
Figure 6, determining an appropriate threshold value aligned with the
processing unit’s capabilities provides us with the range of detectable
scattering angles, denoted as $2\delta$.

Figure 6: A hypothetical configuration of the Nano-scale Radar System containing a DNA molecule as the scatterer (target), and a bow-tie structure as the transmitting antenna is depicted, showcasing the detectable angle $\delta$. Due to the symmetry, the total feasible range of reflection angles for radar detection spans $2\delta$. TABLE IV: Comparison Between RGD and Mie Theorems [88, 89, 45, 46, 47, 48] Aspect | Rayleigh-Gans-Debye Theory | Mie Theory
---|---|---
Particle Size | Suitable for $a\ll\lambda$ | Applicable to a wide range of particle sizes
Scattering Regime | Rayleigh and some Mie scattering | Mie scattering (covers broader scattering range)
Polarizability Model | Isotropic | Complex polarizability based on size, shape, and material properties
Scattering Amplitude | Simple expression $f(\theta)\propto\cos^{2}(\theta)$ | More complex expressions based on Bessel functions
Computational Cost | Simplified calculations | High computation costs in terms of time and resources
Accuracy | Accurate enough on terms (32) | More accurate on different particles
Usage Scenarios | Suitable for quick estimations and simple scattering cases | Preferred for detailed and accurate scattering analyses

This setup suggests an initial step for a NR system, to detect nano-targets.
Yet, radar systems encompass tasks beyond mere detection, including estimating
the range between the system and the target. While Mie theory addresses
electromagnetic wave-particle interaction, it doesn’t directly account for
calculating the distance between emitter and target, i.e., solely relying on
the Mie theory principles, cannot help us through a complete standardization
of nanoradars. Hence, to unveil deeper insights into target detection,
accounting for the coexistence of factors like particle-emitter distance,
necessitates the application of more sophisticated electromagnetic theories
and computational techniques, such as Finite-Difference Time-Domain (FDTD)
approach. In other words, to analyze and calculate the waves that are
reflected back from the target, we can apply more sophisticated methods and
use the Mie or RGD theories to find the wave properties near the nano-
particles. For instance, to study the scattering pattern of a nano-object, we
can use the FDTD method to simulate the waves at the target location,
considering the case of an inhomogeneous and dispersive medium. Then, we can
apply the Mie theory to evaluate the backscattered waves. Although NRs are
still a young field and lack standardized infrastructure, characterization and
fabrication methods, certain potential components of these multipurpose
systems can be outlined as follows:

####  IV-C3 Nanoantennas

Nanoantennas are foundational components within potential NR systems.
Engineered to engage with electromagnetic waves and resonate at distinct
frequencies, they facilitate the transmission and reception of signals. In
other words, they are responsible for controlling wave transmissions and
reflections by emitters and targets, respectively. In a potential bistatic NR
(BNR) configuration, two separate nanoantennas would be employed—one for
transmitting signals and another for receiving them. On the other hand, in a
Monostatic NR (MNR) setup, compact transceivers can be utilized to manage both
the transmission and reception of radar signals simultaneously [66, 67].
Previous sections have discussed numerous structures that have been proposed
for potential use in nanoradar systems. Depending on the specific applications
and requirements, any of these structures can be used in a nanoradar system.
The choice would depend on factors such as desired performance, operating
conditions, and target detection capabilities.

####  IV-C4 Photo-detectors (PDs)

One convenient method for capturing back-scattered waves from targets within
nanochannels involves the use of photo-detectors. These devices possess the
ability to sense and detect electromagnetic waves, particularly in the form of
light, by generating an electric current when exposed to it. The resulting
electric current in the external circuitry of the photo-detector, in response
to optical radiations, can be monitored and analyzed. This analysis allows for
a better understanding of the intrinsic features of the back-reflected light,
serving as indicators of both the physical and electrical properties of nano-
objects, such as their sizes, speeds, locations, and permittivities [87].
Photo-detectors are considered as the main components of the photo-receiver
inside optical channels, e.g., a nanoradar system, with different performance
metrics, such as the quantum efficiency, bandwidth, and compatibility, which
are expected to be optimized as a primary step of incorporating the PD into a
nanoradar configuration. There are different proposed structures for photo-
detectors depending on the application, e.g., resonant cavity enhanced photo-
detectors (RCE-PD) [78], carbon nanotube and nanowire-based photo-detectors
[79, 80], low-temperature grown gallium arsenide (LT-GaAs) high-speed photo-
detectors [81], plasmonic photo-detectors [82], CMOS-integrated waveguide
photo-detectors [83], photomultiplier tubes (PMTs) [84], image intensifiers
(I2) [85], and organic narrowband photodetectors [86]. The possibility of
using each of these structures, along with their operational frequency bands
must be proven for NR. For instance, we can calculate the photo-generated
current of a RCE-PD structure as a result of an incident power as proposed in
[78], i.e.,

$\displaystyle
I_{ph}(t)=\frac{q}{x_{a}+w_{n}+w_{p}}[v_{n}N_{ph}(t)+v_{p}P_{ph}(t)],$ (36)

where $q$, $x_{a}$, $N_{ph}(t)$, $P_{ph}(t)$, $v_{n}$, $v_{p}$, $R_{tot}$, and
$C_{d}$ are the total charge, active region width, total photo-generated
electron concentration, total photo-generated hole concentration, electrons
saturation velocity, hole saturation velocity, total resistor of the
structure, and the capacitor of the depletion region, respectively. The total
photo-generated electron and hole contentrations in (36) are functions of
time, dependent on the intrinsic physical parameters of the photo-detector,
the incident power intensity $P_{i}$, the propagation frequency $\nu$, and the
Planck’s constant $\textstyle{h}$ [78], i.e.,

$\displaystyle
N_{ph}(t)=\frac{P_{i}}{\textstyle{h}\nu}\\{(\mu_{f}^{*}+\mu_{b}^{*})[1-e^{-\alpha_{eff}x_{a}}].[u(t)-u(t-\frac{w_{n}}{v_{n}})]+$
$\displaystyle[\mu_{f}^{*}(1-e^{-\alpha_{eff}x_{a}+\alpha_{eff}v_{n}t}-\alpha_{eff}w_{n})+\mu_{b}^{*}(-e^{-\alpha_{eff}x_{a}}+$
$\displaystyle
e^{\alpha_{eff}v_{n}t-\alpha_{eff}w_{n}})][u(t-\frac{w_{n}}{v_{n}})-u(t-\frac{w_{n}+x_{a}}{v_{n}})]\\},$
$\displaystyle
P_{ph}(t)=\frac{P_{i}}{\textstyle{h}\nu}\\{(\mu_{f}^{*}+\mu_{b}^{*})[1-e^{-\alpha_{eff}x_{a}}].[u(t)-u(t-\frac{w_{p}}{v_{p}})]+$
$\displaystyle[\mu_{f}^{*}(1-e^{-\alpha_{eff}x_{a}+\alpha_{eff}v_{p}t}-\alpha_{eff}w_{p})+\mu_{b}^{*}(-e^{-\alpha_{eff}x_{a}}+$
$\displaystyle
e^{\alpha_{eff}v_{p}t-\alpha_{eff}w_{p}})][u(t-\frac{w_{p}}{v_{p}})-u(t-\frac{w_{p}+x_{a}}{v_{p}})]\\},$

in which $\alpha_{eff}$ is the ionization factor,
$\mu_{f}^{*}=\mu_{f}/(1-exp(-\alpha_{eff}x_{a}))$, and
$\mu_{b}^{*}=\mu_{b}/(1-exp(-\alpha_{eff}x_{a}))$, where $\mu_{f}$, and
$\mu_{b}$ are the forward quantum efficiency (the ratio between the forward
optical power to the total incident power), and the backward quantum
efficiency (the ratio between the backward optical power to the total incident
power), respectively.

It’s important to highlight that in the next phase, we can analyze the impact
of nano-targets on $I_{ph}(t)$ by employing Deep Learning (DL) models, thereby
completing the nanoradar detection process [74]. Further investigation into
feasible photo-detectors is essential, if opted for being employed in a NR
system. This choice is dependent on multiple factors, including the channel’s
length, the background material of the channel (water/air/etc.), the optimal
number of PDs for effective detection, and their strategic placement within
the channel.

####  IV-C5 Signal Processing and Monitoring

The processor would assume the responsibility of analyzing radar echo signals,
extracting pertinent data, and formulating decisions predicated on the
received signals. This unit could be seamlessly integrated within the radar
system or function autonomously, establishing connectivity to the radar
through suitable interfaces. Additionally, comprehensive oversight and
regulation of all signals within the NR system are imperative across various
tiers. The radar system’s operation, encompassing parameter configuration,
configuration adjustments, and overall performance monitoring, necessitates
meticulous supervision, facilitated through software interfaces or internal
components and controllers. By leveraging Machine Learning (ML) and Deep
Learning (DL) techniques, the complex output information contained within
nanoscale signals in the NR system, can be efficiently processed and
interpreted. This enables researchers and practitioners to gain deeper
insights into the data, enabling more precise monitoring and visualization of
targets of interest [74]. Moreover, ML and DL models contribute to the task of
detecting and classifying targets, providing enhanced capabilities in accurate
identification and categorization. Therefore, the utilization of ML and DL
models in analyzing nanoscale signals offers a practical solution that
empowers sophisticated data processing, enabling comprehensive understanding
and improved decision-making based on the captured information [74, 75, 76,
77]. The processing unit might be empowered by different components and
modules. Some of these are as follows:

  1. (a)

Nano-resonators
Nanoscale resonators, specifically Metamaterial-based (MtM) structures, have
the capability to manipulate electromagnetic waves at unique resonant
frequencies, determined by their size, shape, and material characteristics.
These resonators can serve as highly sensitive sensors, detecting optical
forces exerted by nano-objects like quantum dots or individual molecules.
Additionally, they offer potential applications in nano-communication systems
as precise frequency references for accurate timekeeping [68, 69]. As a
result, these structures are designed to enhance signal processing
capabilities and improve sensitivity, making them viable components for a
potential Nanoscale Radar (NR) system.

  2. (b)

Nano Integrated Circuits (NICs)
Nanointegrated Circuits (NICs) possess the potential to play a critical role
in the processing of radar signals, encompassing essential functions like
modulation, sensing and detection (including single-photon detectors), and
filtering. The design of these circuits must align with the specific
requirements and functionalities of the NR system. Effective operation at the
nanoscale and high frequencies, spanning the terahertz and optical ranges, is
a key consideration. NICs can be fabricated using innovative materials and
structures such as nanowires, graphene, and carbon nanotubes (CNTs) [70, 71].

  3. (c)

Nano-actuators and Sensors
Nanoscale actuators play a crucial role in detecting external stimuli and
enabling the NR system to adjust its parameters in response to the surrounding
conditions. These actuators possess the unique advantage of precise
manipulation at the nanoscale, making them well-suited for a broad range of
applications that require accurate and miniaturized motion. The literature
presents various types of nanoscale actuators or nanoelectromechanical systems
(NEMS). For instance, Piezoelectric Actuators utilize the piezoelectric effect
to enable precise positioning and control applications. Electromagnetic
Actuators employ magnetic fields to generate forces, while Thermal Actuators
rely on the expansion and contraction properties of materials. Additionally,
Shape Memory Alloy Actuators utilize shape memory alloys, capable of
reversible changes in shape or length when subjected to temperature
variations. These diverse types of actuators offer promising avenues for
achieving precise and adaptable motion at the nanoscale [72, 73].

Although all of the mentioned building blocks of the NR system have actual
fabrications, the compatibility of these components remains a critical
challenge. Significant efforts are required to develop the field of nanoradars
towards standardization. The standardization process aims to ensure
consistency, and efficiency across different nanoradar systems. To achieve
this goal, researchers and engineers need to focus on explaining the
underlying principles of nanoradars and clarifying the essential building
blocks required for their implementation. This includes studying the
interactions between nanoradar components such as transceiver antennas,
nanointegrated circuits (NICs), nano actuators and sensors, and signal
processing and monitoring units. In parallel with understanding the
fundamental aspects, the development of standardized fabrication methods is a
must. Researchers must explore various techniques and processes that enable
the efficient and reliable integration of nanoradar components.
Standardization will enable the development of more robust and reliable
nanoradar systems that are versatile tools which find efficient applications
across various fields, including biomedical imaging, disease monitoring, drug-
delivery supervision, in-body or external monitoring, and more.

##  V Conclusion

In summary, the successful operation of many cutting-edge technologies relies
heavily on well-established infrastructures. Optical antennas, particularly
those operating in optical ranges including the terahertz gap, are critical
components that require thorough investigation and development to meet the
demands of state-of-the-art applications such as 6G, IoT, ISAC, nanoscale
radar systems, and medical applications. The increasing need for Tbps data
transfer rates in optical and terahertz communication channels calls for the
development of optical nanoantennas with high gains and wide bandwidths. This
article aims to investigate the fundamentals and emerging technologies in this
field, referencing significant publications and offering researchers a
comprehensive perspective on the various aspects and challenges connected to
the design and construction of nanoantennas for diverse applications.
Additionally, for the first time, this paper introduces the standardization of
Nano-scale Radar (NR) systems by presenting a foundational building block and
discussing potential analysis techniques like Mie and RGD theories to explore
the scattering behavior of nanoparticles. A practical nanoradar must
proficiently detect the presence of nanoparticles and extract their physical
or electrical properties within the nanochannel. Illuminating nano-objects is
achieved through nanoantennas, and capturing back-scattered waves necessitates
a device like a transceiver or a photo-detector. The photo-detector monitors
the output current generated by incident waves, aiding in the understanding of
various aspects and properties of the incoming light. Finally, the data
processing unit, empowered by machine learning or deep learning models,
analyzes and extracts these properties. In conclusion, further research is
expected in the near future, focusing on feasible configurations within the
aforementioned components, ensuring physical compatibility between different
parts, and refining data processing techniques for nanoradars.

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