# 6G Communication: Envisioning the Key Issues and Challenges

Sabuzima Nayak and Ripon Patgiri
National Institute of Technology Silchar Sabuzima Nayak and Ripon Patgiri,
Department of Computer Science & Engineering, National Institute of Technology
Silchar, Cachar-788010, Assam, India, Email<EMAIL_ADDRESS>and
<EMAIL_ADDRESS>
###### Abstract

In 2030, we are going to evidence the 6G mobile communication technology,
which will enable the Internet of Everything. Yet 5G has to be experienced by
people worldwide and B5G has to be developed; the researchers have already
started planning, visioning, and gathering requirements of the 6G. Moreover,
many countries have already initiated the research on 6G. 6G promises
connecting every smart device to the Internet from smartphone to intelligent
vehicles. 6G will provide sophisticated and high QoS such as holographic
communication, augmented reality/virtual reality and many more. Also, it will
focus on Quality of Experience (QoE) to provide rich experiences from 6G
technology. Notably, it is very important to vision the issues and challenges
of 6G technology, otherwise, promises may not be delivered on time. The
requirements of 6G poses new challenges to the research community. To achieve
desired parameters of 6G, researchers are exploring various alternatives.
Hence, there are diverse research challenges to envision, from devices to
softwarization. Therefore, in this article, we discuss the future issues and
challenges to be faced by the 6G technology. We have discussed issues and
challenges from every aspect from hardware to the enabling technologies which
will be utilized by 6G.

###### Index Terms:

6G, Wireless Communication, Mobile Communication, Issues, Challenges, Internet
of Things, Internet of Everything.

##  I Introduction

Nowadays, 6G mobile communication technology is one of the most demanded
research fields. The 6G will revolutionize personal life, lifestyle, society,
business and communication systems. It is a crucial time to envision the
potential applications, techniques, use cases, and challenges of 6G
technology. Noteworthy, 6G will create enormous research possibilities and
enable many new technologies. Moreover, the 6G will be proven as a game
changer paradigm in diverse fields. Therefore, the visioning of the 6G
technology is necessary to revolutionize the modern world. Currently, many
countries are deploying the 5G technology in full swing. However, 5G and
Beyond 5G (B5G) will be unable to provide the complete requirements of the
Internet of Everything (IoE). Therefore, there is a high demand for 6G.
Researchers have already initiated solving the challenges of B5G mobile
communication. In the next five years, B5G will evolve and it is highly
expected that 6G will be fully able operate from 2030. The 6G will be the most
prominent research field for next 10-25 years. The 6G research work has
already begun in Finland in 2018 and the project name is “6genesis flagship
project” [1]. In 2019, USA, China, and South Korea have launched 6G project
[2]. Japan has also launched 6G project in 2020 [3]. Also, Nippon Telegraph
and Telephone Public Corporation (NTT) has released their white paper in
January 2020. Many countries have initiated the 6G project.

Figure 1: Envisioning the challenges of 6G

We are witnessing that there is a new mobile generation in every decade [4].
Therefore, it is expected that 6G will be deployed in 2030. The maximum data
rate of various mobile generation from 1G to 6G is exponential. Therefore, the
6G is itself a great challenge to achieve with all the desired parameters
stated in Table I. In a nutshell, the first research paper of 6G appears in
2018 [1]. From this, research is spiking up on 6G, and gathering various
requirements. Numerous discussions are performed regarding the requirements
[5, 6, 7]. To achieve the requirements of 6G, various visions have been
provided. Gui et al. discusses on various possible applications of mobile
broad bandwidth and low latency (MBBLL), massive broad bandwidth machine type
(mBBMT), massive low latency machine type (mLLMT) [8]. Zhang et al. focuses on
further-enhanced mobile broadband (FeMBB), extremely reliable and low-latency
communications (ERLLC), ultra-massive machine-type communications (umMTC),
long-distance and high-mobility communications (LDHMC) and extremely low-power
communications (ELPC) [7]. Dang et al. provides future possibilities of 6Gs
[2]. Moreover, already carious works have been initiated by the researchers
[9]. Thus, there are numerous research paper in visioning the 6G mobile
communication. On the contrary, it is very important to unearth the hardship
to be faced by the much awaited 6G technology. Because, the 5G technology is
yet to be deployed worldwide in full scale. Moreover, there are numerous
issues in 5G technology to overcome. Thus, it motivated us to investigate on
the issues and challenges of 6G technology.

The 6G will change the definition and perception of the modern lifestyle,
society, business and communication. It is expected that 6G will associate in
revolutionizing several fields which are to be visioned. However, there are
numerous problems in 6G, and thus, we exploit all issues and challenges of 6G
from every aspect in this article. Moreover, we survey various requirements of
6G technology. Thus, Section II analyzes and compares the requirements of 5G,
B5G and 6G. There are many requirements to analyze and acquired before
implementing the 6G. These requirements are the challenges of B5G and 6G in
the implementation process. Thus, this article explores all prime challenges
associated with 6G technology in Section III. Moreover, this article also
uncovers prominent issues of 6G technology in Section IV. Finally, the article
draws a suitable conclusion in Section V.

##  II 6G Requirements

TABLE I: Comparison among 5G, B5G and 6G requirements Requirements |  5G |  B5G |  6G
---|---|---|---
Application types [7, 10] |  $\bullet$ eMBB
$\bullet$ URLLC
$\bullet$ mMTC |  $\bullet$ Reliable eMBB
$\bullet$ URLLC
$\bullet$ mMTC
$\bullet$ Hybrid (URLLC + eMBB) |  $\bullet$ MBRLLC
$\bullet$ mURLLC
$\bullet$ HCS
$\bullet$ MPS
Device types [7, 10] |  $\bullet$ Smartphones
$\bullet$ Sensors
$\bullet$ Drones |  $\bullet$ Smartphones
$\bullet$ Sensors
$\bullet$ Drones
$\bullet$ XR equipment |  $\bullet$ Sensors and DLT devices
$\bullet$ CRAS
$\bullet$ XR and BCI equipment
$\bullet$ Smart implants
Operating frequency [7] |  3-300 MHz |  500 MHz |  1 THz
Spectral efficiency [7, 10] |  10$\times$ in bps/Hz/m2/Joules |  100$\times$ in bps/Hz/m2/Joules |  1000$\times$ in bps/Hz/m3/Joules
Data Rate [7, 10] |  1 Gbps |  100 Gbps |  1 Tbps
End-to-end delay [7, 10] |  5 ms |  1 ms |  $<$1 ms
Radio-only delay [7, 10] |  100 ns |  $<$100 ns |  $\leq$10 ns
Processing delay [7, 10] |  100 ns |  50 ns |  $\leq$ 10 ns
Mobility range [7] |  100 – $<$ 500 km/h |  500 km/h |  1000 km/h
Wavelength |  3 mm |  1 mm |  300 $\mu$m
Architecture |  Massive MIMO |  Massive MIMO |  Intelligent surface
Core network |  Internet of Things |  Internet of Things |  Internet of Everything
Satellite integration |  Partial |  Partial |  Full
Artificial Intelligence integration |  Partial |  Full |  Truly Artificial Intelligence Driven
XR integration |  Partial |  Full |  Full
Haptic communication integration |  Partial |  Full |  Full
Highlights |  Extremely high rate of Streaming |  Extremely high rate of Streaming |  Security, secrecy, & privacy

Table I depicts the requirements and the challenges of 6G. The 6G is expected
to deliver truly Artificial Intelligence (AI)-driven communications.
Therefore, “the 6G is about the 6th sense”, Peter Wetter, Nokia Bell Labs
[11]. Also, it is expected that prices will be $1000\times$ cheaper in 6G era
[12]. Zhang et al. [12] emphasizes price reduction that there were $10\times$
price reduction from 1G to 2G, and $1000\times$ in 3G and 4G. It will be
expected to continue in 5G and 6G era. Moreover, 6G will be backed by Extended
Radio (XR) and will replace NR-Lite of 5G. Also, it requires satellite support
with terrestrial communication. The prime requirements of 6G technology are 1
THz operating frequency and data rate of 1 Tbps. Also, the 6G is 3D structural
communication which defines space, time and frequency with requirements of
$100\times~{}in~{}bps/Hz/m^{3}/Joules$. It expects maximum wavelength of
$300~{}\mu m$ to achieve 1 THz.

###  II-A Network Architecture

Most importantly, 6G technology will enable the IoE. Every smart device will
be connected in the 6G network. Starting from small devices such as smartwatch
or smart phones to mobile healthcare, vehicular and so on. IoE and mMTC
services will connect all smart devices to one or more wireless-access
networks. These wireless-access networks have multiple access points (AP)/base
stations (BS) to provide services. To provide services to million smart
devices within a small geographical location, the APs/BSs are located
densely/ultradensely in 6G network. In such a network scenario, APs/BSs will
have overlapping coverage areas, i.e., a small geographical location will be
served by multiple APs/BSs. And, multipoint transmissions will have million
users served by multiple APs/BSs. In this case, management and efficiency are
required in frequency allocation, interference management and efficient
handoff. APs/BSs are connected through fast backhauling links. The 6G network
will be viewed as a distributed, cell-less massive multiple-input, multiple-
output (MIMO) system. In this network, every smart device will be served by
more than one APs/BSs using transmission multiplexing or transmission
coordination. The APs/BSs are connected to the Cloud for accessing Cloud
services. Cloud computing supports large smart applications such as autonomous
driving, virtual reality, smart city, industrial control and smart
manufacturing, and numerous health-monitoring services. The smart devices are
connected using peer-to-peer connectivity through single/multihop
communications. Moreover, terrestrial cellular systems will be integrated with
drone-assisted/aerial networks supported by mobile APs. In addition, these
networks will be based on content and application driven more than just data
transmission.

##  III Challenges

The challenges are much bigger than 6G services deliverable as depicted in
Figure 1 through an iceberg. It is expected that most of the issues of 5G will
be solved by B5G and the remaining issues by 6G. However, the requirements are
growing exponentially. Thus, challenges of 6G are much bigger than the 5G and
B5G.

###  III-A Terahertz Signal

6G will be using TeraHertz (THz) frequency band for transmission. However,
operating in THz is a grand challenge where the wavelength is 300 $\mu$m.

  * •

Generation: 6G transmission requires continuous THz signal. But, generation of
continuous THz signal is difficult because it has more strict requirements
regarding size and has more complexity in designing the antenna/transmitter.
Moreover, it is costly to generate of THz signal. Transmission aspect of 6G
has to be low in cost to support the low cost advantage claimed by 6G.

  * •

Energy Loss: Another issue with THz signal is that it is a short distance
communication signal (i.e., a few meters). Because, it attenuates to zero
after travelling a short distance in air. The energy loss of signal occurs due
to molecular absorption and spreading loss. The molecular absorption loss
occurs due to conversion from THz signal energy to the internal kinetic energy
of the molecules present in the air. And, the loss becomes severe with
increasing moisture in the air. The spreading loss occurs due to extension of
electromagnetic waves in the air and quadratically increases based on the
operating frequency and distance between two communication ends. This is a
major setback for 6G because such a signal is inappropriate for 6G
requirements. It is not economical to amplify a signal after every 1 meter.
However, research continues to make THz signal ideal for 6G.

###  III-B New Communication Alternatives

New communication alternatives are also explored to have a better option
instead of THz signals. Visible Light Communication (VLC) uses cheap light
emitting diodes (LED) to achieve higher frequency bands [13]. However, the VLC
has issues with coverage and noise interference from other sources of light.
Thus, VLC is used in a confined arena that does not have any interference from
other sources of light. Another option is molecular communications (MC) [13].
It uses biochemical signals for data transmission. Biochemical signals are
particles of the size of a few nanometers to a few micrometers. It propagates
in a gaseous or aqueous medium. Advantages of MC signals are biocompatible,
consumes less energy for production and transmission, and high data rates.
However, it creates challenges of security and effective interface between
chemical and electrical domains. Third option can be quantum communication
(QC) [13]. Quantum particles or photons are utilized to encode data in a
quantum state. It makes the data access and cloning by hackers difficult. The
advantages are high security, high data rates, and effective long distance
transmission. However, its at an early stage of development and have a long
way to be considered as an alternative of THz signal.

###  III-C Underwater Communication

6G is also aiming to provide underwater communication. However, the underwater
environment is becoming a different scenario compared to air or space.
Underwater environment is unpredictable and complex, such as high signal
attenuation, physical damage to equipment and complicated network deployment.
Radio signals are highly attenuated in salt water. Therefore, acoustic
communication is the only option for communication. Deployment of nodes is
expensive and difficult, hence, density of nodes have to be low. Node mobility
is difficult due to flow and density variation of the water. Underwater
sensors are expensive and these sensors are designed with extra protection for
the underwater environments. Moreover, it requires complex transceivers and a
large memory. The power supply has to be large because solar power cannot be
utilized. Fouling and corrosion increase the failure of underwater sensors.
The best choice is optical fiber, however, it is expensive. Another option is
quantum communication, however, it is at an early stage of research. To
achieve an efficient underwater communication 6G has to fight against the
challenges of underwater circumstances.

###  III-D Design of Transmitter and Antenna

The 6G demands highly efficient transmitter and receiver antenna. The required
radio frequency (RF) transceiver should have high integration. It is achieved
by implementing advanced silicon nodes with CMOS SOI, bulk CMOS and SiGe
BiCMOS and attentive co-design with off-chip, highly efficient antennas. To
meet the requirements of 6G, new balance between RF technologies,
communication and signal processing is essential [1]. Highly efficient RF will
help in achieving high data rates. In addition, the materials used to
construct the antennas also influence the data rate. The materials influence
the intrinsic and extrinsic composition-structure-property relationships. If
this relationship is poor, then the attenuation is more and leading to low
data rate. Currently, research on new materials are being conducted such as
nanomaterials, bio-based, foams, room-temperature fabricated materials, ultra-
low permittivity. These materials are self healing and electrically tuneable
[1].

In case of satellite assisted IoE, the IoT devices pose the most issues. In
satellite assisted IoE, first, data are collected from IoT devices which are
transmitted to satellite antennas at the ground connected to core networks
through wireless or wired channels. However, IoT devices have hardware
constraints. Therefore, data transmission to satellites is difficult. Suppose,
6G follows a direct transmission mode. It means that the IoT devices directly
send the data to satellites. In such a case, IoT devices have to provide high
power to the signal to reach the satellite, however, it is impractical due to
low power source of IoT devices. Another solution is hybrid transmission mode.
A data aggregator collects the IoT data and transmit to the satellite. 6G
requires mitigation/interference coordination techniques along with varieties
of MIMO multi-antenna transceivers for good transmission [1].

###  III-E End-to-end Delay and Reliability

The 6G data rate requirement is 1 Tbps which will enable many applications, to
name a few, virtual/augmented reality, smart healthcare, UAV, smart electric
car, and smart city. Such applications require high data rate with low
latency. Notably, 6G focuses on QoE. For instance, a car discusses with
passengers, which enable the teaching - learning process. Also, electric car
need to charge while moving without the intervention of the human. To provide
a high QoE, it demands low end-to-end delay. To achieve lower end-to-end
delay, smaller frame sizes or data packets have to be transmitted. Moreover,
6G will require flat network architectures. Reliable transmission requires
efficient Forward error correction (FEC) schemes. Efficient FEC schemes
requisite long transmission time or using parallel diversity channels in large
numbers [1]. Long transmission time is not possible because 6G has to maintain
a latency of $<$ 1 ms. This challenge need to be addressed in physical and
networking layers. Technology such as AI is also considered to reduce latency,
for example, deep learning based transmission prediction. It helps in
predicting the user requests and change in channel state to reduce the
transmission latency. The 5G is backed by ultra-reliable low latency
communications (URLLC) and it’s reliability is $99.999\%$. On the contrary, 6G
has to increase its reliability and requires extremely reliable low latency
communications (ERLLC) to support a reliability rate of $99.99999999\%$.
Therefore, 6G has to enhance URLLC and provide higher reliability rate than 5G
and B5G.

###  III-F Energy

Every device in 6G, such as smart devices and APs, will implement
sophisticated signal processing mechanisms. Moreover, they have to process Big
data which require heavy processing and high energy. Therefore, energy is also
an issue in 6G. 6G will be using emerging technology such as Edge and AI in
its network nodes that also require high energy. Thus, 6G has to solve the
issue of harvesting, charging and conservation of energy. Another factor is
energy cooperation among 6G network nodes. In addition, data transmission also
demands more energy as discussed in the transmission mode. Therefore, to
reduce the power consumption, new waveform and modulation is required to have
low peak to average power ratios (PAPR) [4]. Besides, higher energy is
consumed to compute complex algorithms to provide high level security. On the
contrary, embedding AI with 6G will reduce the energy consumption.

###  III-G Capacity

6G is a key enabler of IoE that connects billions of smart devices and smart
wearables. Moreover, 6G promises many high QoS applications, e.g,
virtual/augmented reality. The IoE devices will produce huge traffic. But, the
aim of $<$1 ms latency is achievable in case of low traffic. Moreover,
communication network congestion will degrade QoS. Therefore, capacity is a
big challenge for 6G. 6G capacity is enhanced mainly by four ways. First,
increase the spectrum bandwidth. Second, enhance the spectrum efficiency in
the air, i.e., the bits per second per Hertz. It is achieved by implementing
good channel coding or new modulation technique. Third, spectrum reuse.
Another solution is increasing the node density. Mobile nodes are deployed to
hotspot network areas to reduce the load of the nodes. Nevertheless, the
repositioning of mobile node affects many factors such as handover control and
network topology. Manually handling the repositioning is difficult, hence, AI
is deployed.

###  III-H Global Coverage

6G will rely on the low-earth orbit (LEO) satellite having a height of 500 to
2000 km from the orbit for providing global coverage. LEO will aim for less
path loss, lower transmission delay and much more. However, LEO have issues
such as doppler variation, doppler shift, long transmission delay and more
path loss [4]. LEO satellites travel very fast compared to the rotation of
Earth. This leads to doppler variation and doppler shift in the network
communication and cause random access, synchronization, signal detection, and
signal measurement issues. Other issues are long transmission delay and more
path loss in LEO compared to terrestrial transmission.

###  III-I Density

Density is the number of network nodes per square kilometer. 6G will have more
density to have a smooth and high QoS. However, deploying a number of network
nodes will increase the communication cost. Communication cost refers to the
congestion control, scheduling, synchronization, failure detection and many
more. The economic cost will also be high because 6G include both non-
terrestrial and terrestrial nodes. The non-terrestrial nodes are very costly
compared to terrestrial nodes. Moreover, more nodes require more maintenance.

###  III-J Cost

One of the goals of 6G is providing the services at a cheaper cost. The 6G
network nodes are both non-terrestrial and terrestrial. Terrestrial nodes are
comparatively low in cost, however, their maintenance will incur money. The
non-terrestrial nodes, such as satellites, drones and other mobile nodes, are
very expensive. Deploying satellites to space is a costly affair. Their repair
and maintenance is another issue in terms of cost. In addition, underwater
communication also requires expensive infrastructure. Moreover, the 6G
requires high quality devices to maintain the highest QoS. Sophisticated and
high quality devices are costly. The smart devices are costly and may not be
affordable to everyone. The challenge is to reduce to cost to make affordable
to everyone.

###  III-K Heterogeneity

The 6G will connect a variety of smart devices. Moreover, the communication
network will be different at different coverage. It is impossible to provide
just a single global coverage to connect the whole world. The communication
network will be divided into sub-networks. And, these sub-networks will not be
homogeneous. In addition, 6G will integrate both non-terrestrial and
terrestrial communication networks, whereas both are very heterogeneous by
nature. Their different heterogeneous features have to be considered to
efficiently integrate them together. Heterogeneity is also present in the
protocol that these communication networks will follow. Thus, 6G is taking a
big challenge of integrating various heterogeneous components together.

###  III-L Security, Secrecy and Privacy

Dang et. al [2] propose that the security, secrecy and privacy are the key
features of 6G. It is expected that 6G will be able to deliver the highest
level of security. THz communication will make the 6G eavesdropping and
jamming proof. Similarly, quantum computing will feature the unbreakable
security to the 6G, for instance, quantum cryptography for secrecy. The
privacy is the key concerns of individuals, particularly, healthcare requires
the highest degree of privacy protection. Blockchain is the most prominent
technology to achieve a higher degree of privacy, secrecy and security.
Deploying federated AI also increases security. Another important point to
note is 6G promises to provide physical layer security. It will be achieved by
integrating AI to 6G. Various research works are going on to explore this
option [9]. Another vulnerable target of 6G is the power system of the network
devices. For DoS attacks, attackers are adopting sleep deprivation or battery
draining. To achieve sleep deprivation attacks the attacker transmits many
requests to Edge devices or other network nodes pretending as a legitimate
user. The nodes will respond to the requests and exhausts the power. In
battery draining attack, using any means the attacker tries to exhaust the
power. For example, executing power consuming subroutines in the 6G network
nodes. Thus, such simple trick will be able to cut off the network nodes and
will impair the communication.

###  III-M Heavy Computation

6G will combine communication with computation. The services of 6G are heavy
computation. 6G networks are ultra large scale, complex and multidimensional.
It is also dynamic due to network topology, consumer demands, traffic load and
radio resource. Thus, automatic network configuration is required for wireless
connection. Similarly, the 6G network requires intelligent mobility
management. It helps in positioning the mobile node during a disaster or node
failure. However, all these tasks require heavy computation. To solve that 6G
will rely on new technologies such as edge computing, federated AI, etc.
However, implementing these technologies also has many sets of issues. Issues
are present both in individual technology and integration with 6G.

###  III-N Five Senses Communication

The 6G is a sixth sense communication system. Therefore, it is expected that
6G will be able to support five senses of communication system. The five
senses are sight, hearing, smell, touch and taste. 6G requires a very high
data rate with extremely low latency. Moreover, the sensors must able to
reproduce the five senses from remote locations to provide real experience to
the users, for example, holographic communication. Therefore, 6G is focused on
QoE.

###  III-O Industrial Revolution

We expect that Industry 5.0 will evolve and revolutionized soon. There is a
high chance of shifting of Industry 4.0 to Industry 5.0 in 2030. Industry 4.0
is “Digitalization”, and Industry 5.0 is “Personalization and
Intelligentization” which requires 5G, B5G and 6G. Industry 5.0 will allow the
customer to customize the services. 6G will enable Industrial Internet of
Everything (IIoE), and further, it will revolutionize the industries a step
ahead and motivate to introduce “Intelligentization”. Moreover, we foresee
that 6G and B6G will be a great enabler of Industry 5.0 which is
“Intelligentization” of industries.

###  III-P Paradigm Shift

6G will enable paradigm shift and we will evidence the shift from 2030 onward.
Furthermore, 6G will enable the conversion from smart devices to intelligent
devices, for instance, intelligent vehicle, intelligent mobile, intelligent
wearables, intelligent healthcare and many more. It will be possible with the
combination of AI and 6G communication technology. However, integration of
intelligence and the conversion from smart to intelligent devices involve many
issues. Therefore, new sensors, new devices and new methods have to be devised
to achieve this challenge.

##  IV Issues

Apparently, the 5G technology has just begun to deploy. Also, there is no user
experiences on B5G and the B5G has to experience many things in real scenario
than a lab. Moreover, B5G mobile communication is yet to be developed
completely. Therefore, there are many issues while envisioning 6G technology.

###  IV-A Lack of Technology

Fitzek and Peeling [14] argues that there is a lack of real technology to
implement the 6G requirements. The 6G has many promises to deliver, but
hindered by underdeveloped real technology which could drastically advance 6G
technology from 5G. The requirements increase exponentially from 5G to 6G
mobile communications. 6G has no underpinning technology, and therefore, it
poses doubt on the envisioning in this stage [14]. The 5G technology has just
begun. Thus, deficient in wider user experiences of 5G poses many issues.
Obviously, networking can be effectively utilized using softwarization. Huge
modifications are required on Software-Defined Radio, Software-Defined Network
and Network Function Virtualization of 5G technology to achieve 6G [14]. Also,
the AI has to be embedded in 5G technology to advance 6G. The 6G is truly AI-
Driven communication technology. Therefore, the AI has to be deployed in every
layer of 5G. Noteworthy, 5G will partially support AI to communicate.

###  IV-B Over Expectation from 5G

Another key issue is the exponential difference of 6G with its predecessor.
The 5G is an enhanced version of its predecessor and not exponential growth of
requirements. However, 4G is modified immensely to introduce 5G as
uninterrupted mobile communication. We acknowledge Fitzek et al., the issues
of 5G have to be solved before developing 6G technology. Currently, the 5G is
deployed as a campus solution in a local area [14]. Moreover, the 5G does not
support high mobility. As a consequence, the 6G mobile communication
technology will face a huge problem in providing the mobility support 1000
km/h. Also, this issue can delay the deployment of 6G to 2032 or beyond.
Moreover, current state-of-the-art technology is unable to provide network
coverage to the passengers in an aircraft due to various issues [2]. But, high
mobility is the key issue. This issue can be addressed by satellite
communication, however, it is very costly. Therefore, another alternative is
required to solve the issue. It is expected that 6G will solve the issue.
However, there is no clear roadmap to provide low cost connectivity to the
passengers with 6G parameters.

###  IV-C Interoperability of protocols

6G network will integrate both non-terrestrial and terrestrial communication
networks. The TCP/IP protocols used in terrestrial communication networks are
unsuitable for non-terrestrial communication networks, i.e., satellite
communication. Because, efficiency of TCP/IP protocols is lower due to long
transmission delay, ultra wide bandwidth and higher bit error rate. Therefore,
modification in TCP/IP protocol is required to support both non-terrestrial
and terrestrial communication networks with efficiency. Moreover, different
protocols are implemented in both types of communication networks. In that
case, interoperability of protocols will be an issue [4].

###  IV-D Artificial Intelligence

6G will be truly AI-driven mobile wireless communication, i.e., communication
system will be intelligent enough to route the data. Moreover, federated AI
will help in knowledge sharing among the intelligent devices. Furthermore,
quantum machine learning will enhance the performance of 6G by intelligent
data analysis. However, most of the techniques are in the initial phase of
research. All AI algorithms have high computation. The high computation task
has a long execution time and consumes more power. Whereas, 6G is unable to
provide such relaxation. Another issue is the dynamic nature of the network.
Once inferences are obtained by AI algorithms, it is used to predict future
incoming data. However, communication networks are very dynamic, therefore,
the inferences will become obsolete quickly. Training the AI module after a
short interval will be very costly for 6G. Therefore, 6G will face huge
challenge to deploy AI in the communication. Furthermore, physical layer will
be assisted by AI. However, implementing AI in physical layer is difficult due
to the complexity of the physical layer and bounded learning capacity of AI
algorithms.

###  IV-E Big Data

The 6G will enable IoE and produce massive amounts of small sized data. These
Big data are stored, process and managed through Cloud Computing. The Data on
Internet of Everything (DIoE) will pose new challenges to handle these data.
Till date, there is no such technology that can store, process and manage
Exabytes of small data. These small data will form data silo, and thus, Big
Data is to be redefined and Big Data 2.0 will be introduced. However, Big data
2.0 require a supercomputer to compute and analyze data [15].

###  IV-F Edge Technology

Edge technology was introduced because Cloud is far from the devices that
generate data. However, Edge technology is also an emerging research area
containing many issues. Edge nodes have small power sources and memory. Edge
analytics mostly rely on AI. However, due to the incapability to execute heavy
computation, the AI algorithm is executed in the Cloud and the inferences are
transmitted to Edge nodes. But, the dependence of the Edge on the Cloud will
reduce the performance of 6G. Moreover, the issues of AI will influence the
performance of Edge, and then it will adversely influence the performance of
6G. As discussed in the Big Data section, the massive data will be stored in
Edge nodes. 6G is planning to have a high density. However, with limited
storage, Edge nodes will be incapable of storing information and have to
transmit the data to the Cloud within a very short time interval. Thus,
communication cost will be incurred upon data transmission to the Cloud.

##  V Conclusion

6G is an important communication technology that will enable many new
technologies, for instance, holographic communication. Therefore, we have
surveyed the desired parameters of 6G technology. Also, we have compared the
5G, B5G and 6G mobile communication technology to depict the differences and
needs of the 6G technology. However, the differences are significant.
Therefore, there are huge challenges of 6G to achieve the desired parameters
and deliver the promises of 6G. All issues and challenges of 6G have been
uncovered from every aspect. Finally, we conclude that 6G will revolutionize
many areas, and will also be proven as game changing technology in diverse
fields.

## References

  * [1] M. Katz, M. Matinmikko-Blue, and M. Latva-Aho, “6genesis flagship program: Building the bridges towards 6g-enabled wireless smart society and ecosystem,” in _2018 IEEE 10th Latin-American Conference on Communications (LATINCOM)_. Guadalajara, Mexico: IEEE, Nov 2018, pp. 1–9.
  * [2] S. Dang, O. Amin, B. Shihada, and M.-S. Alouini, “What should 6g be?” _Nature Electronics_ , vol. 3, no. 1, pp. 2520–1131, 2020.
  * [3] N. DOCOMO, “White paper 5g evolution and 6g,” Accessed on 1 March 2020 from https://www.nttdocomo.co.jp/english/binary/pdf/corporate/technology/whitepaper_6g/DOCOMO_6G_White_PaperEN_20200124.pdf, 2020\.
  * [4] S. Chen, Y. Liang, S. Sun, S. Kang, W. Cheng, and M. Peng, “Vision, requirements, and technology trend of 6g: How to tackle the challenges of system coverage, capacity, user data-rate and movement speed,” _IEEE Wireless Communications_ , pp. 1–11, 2020.
  * [5] B. Zong, C. Fan, X. Wang, X. Duan, B. Wang, and J. Wang, “6g technologies: Key drivers, core requirements, system architectures, and enabling technologies,” _IEEE Vehicular Technology Magazine_ , vol. 14, no. 3, pp. 18–27, Sep. 2019.
  * [6] K. David and H. Berndt, “6g vision and requirements: Is there any need for beyond 5g?” _IEEE Vehicular Technology Magazine_ , vol. 13, no. 3, pp. 72–80, Sep. 2018.
  * [7] Z. Zhang, Y. Xiao, Z. Ma, M. Xiao, Z. Ding, X. Lei, G. K. Karagiannidis, and P. Fan, “6g wireless networks: Vision, requirements, architecture, and key technologies,” _IEEE Vehicular Technology Magazine_ , vol. 14, no. 3, pp. 28–41, Sep. 2019.
  * [8] G. Gui, M. Liu, F. Tang, N. Kato, and F. Adachi, “6g: Opening new horizons for integration of comfort, security and intelligence,” _IEEE Wireless Communications_ , pp. 1–7, 2020.
  * [9] Y. Al-Eryani and E. Hossain, “The d-oma method for massive multiple access in 6g: Performance, security, and challenges,” _IEEE Vehicular Technology Magazine_ , vol. 14, no. 3, pp. 92–99, 2019.
  * [10] W. Saad, M. Bennis, and M. Chen, “A vision of 6g wireless systems: Applications, trends, technologies, and open research problems,” _IEEE Network_ , pp. 1–9, 2019.
  * [11] H. Viswanathan and P. Mogensen, “Communications in the 6g era,” _IEEE Access_ , pp. 1–1, 2020.
  * [12] S. Zhang, C. Xiang, and S. Xu, “6g: Connecting everything by 1000 times price reduction,” _IEEE Open Journal of Vehicular Technology_ , pp. 1–1, 2020.
  * [13] T. Huang, W. Yang, J. Wu, J. Ma, X. Zhang, and D. Zhang, “A survey on green 6g network: Architecture and technologies,” _IEEE Access_ , vol. 7, pp. 175 758–175 768, 2019.
  * [14] F. H. P. Fitzek and P. Seeling, “Why we should not talk about 6g,” _arXiv preprint arXiv: 2003.02079_ , 2020.
  * [15] S. Nayak, R. Patgiri, and T. D. Singh, “Big computing: Where are we heading?” _EAI Endorsed Transactions on Scalable Information Systems_ , 4 2020.

|  Sabuzima Nayak is pursuing PhD at the department of Computer Science &
Engineering, National Institute of Technology Silchar. She has published
several papers in conferences and journals. She is authoring a Book. Her
research interests are Bloom Filter, Communication, Big Data and
Bioinformatics.
---|---
|  Ripon Patgiri is currently working as an Assistant Professor, National
Institute of Technology Silchar. He has received his PhD degree from National
Institute of Technology Silchar and M.Tech. Degree from Indian Institute of
Technology Guwhati. He has published several papers in reputed journals,
conferences and books. His research Interests are Bloom Filter, Communication
and Networking, Big Data and Bioinformatics. He is a senior member of IEEE. He
has chaired and organized several international conferences. URL:
http://cs.nits.ac.in/rp/
---|---


††thanks: These authors contributed equally††thanks: These authors contributed
equally††thanks<EMAIL_ADDRESS><EMAIL_ADDRESS>
# Dual-Resonance Enhanced Quantum Light-Matter Interactions In
Deterministically Coupled Quantum-Dot-Micopillars

Shunfa Liu  State Key Laboratory of Optoelectronic Materials and Technologies,
School of Physics, School of Electronics and Information Technology, Sun Yat-
sen University, Guangzhou 510275, China  Yuming Wei  State Key Laboratory of
Optoelectronic Materials and Technologies, School of Physics, School of
Electronics and Information Technology, Sun Yat-sen University, Guangzhou
510275, China  Xueshi Li  State Key Laboratory of Optoelectronic Materials and
Technologies, School of Physics, School of Electronics and Information
Technology, Sun Yat-sen University, Guangzhou 510275, China  Ying Yu  State
Key Laboratory of Optoelectronic Materials and Technologies, School of
Physics, School of Electronics and Information Technology, Sun Yat-sen
University, Guangzhou 510275, China  Jin Liu  State Key Laboratory of
Optoelectronic Materials and Technologies, School of Physics, School of
Electronics and Information Technology, Sun Yat-sen University, Guangzhou
510275, China  Siyuan Yu  State Key Laboratory of Optoelectronic Materials and
Technologies, School of Physics, School of Electronics and Information
Technology, Sun Yat-sen University, Guangzhou 510275, China  Photonics Group,
Merchant Venturers School of Engineering, University of Bristol, Bristol BS8
1UB, United Kingdom  Xuehua Wang  State Key Laboratory of Optoelectronic
Materials and Technologies, School of Physics, School of Electronics and
Information Technology, Sun Yat-sen University, Guangzhou 510275, China

###### Abstract

Optical microcavities have widely been employed to enhance either the optical
excitation or the photon emission processes for boosting light matter
interactions at nanoscale. When both the excitation and emission processes are
simultaneously facilitated by the optical resonances provided by the
microcavities, as referred to the dual-resonance condition in this article,
the performances of many nanophotonic devices approach to the optima. In this
work, we present versatile accessing of dual-resonance conditions in
deterministically coupled quantum-dot(QD)-micopillars, which enables emission
from exciton (X) - charged exciton (CX) transition with improved single-photon
purity. In addition, the rarely observed up-converted single-photon emission
process is achieved under dual-resonance condition. We further exploit the
vectorial nature of the high-order cavity modes to significantly improve the
excitation efficiency under the dual-resonance condition. The dual-resonance
enhanced light-matter interactions in the quantum regime provides a viable
path for developing integrated quantum photonic devices based on cavity
quantum electrodynamics (QED) effect e.g., highly-efficient quantum light
sources and quantum logical gates.

Last decade has witnessed significant advances in nanophotonics by harnessing
the enhanced light-matter interaction in optical microcavities Vahala (2003).
E.g., cavity enhanced scattering and excitation enable the realization of
biosensing with sensitivity down to single-molecule level Vollmer and Arnold
(2008); Baaske et al. (2014); Toropov et al. (2021) and highly efficient
optical harmonic generations Liu et al. (2019a, 2018a). On the emission part,
microcavities can modify the photonic environments of the nanoscale quantum
emitters, resulting in faster radiative emission rate and better far-field
radiation directionality Gérard et al. (1998); Liu et al. (2018b, 2019b); Wang
et al. (2019a). However, most of the nanophotonic devices based on high
quality (Q) dielectric microcavities, to date, only involves the single
resonance condition either for boosting the excitations or improving the
photon emissions. Ideally, it is possible and highly desirable to
simultaneously enhance both the excitation and the emission processes under
the multiple-resonance condition, which is however technologically challenging
especially for the dielectric microcavities with high-Q factors. Only until
very recently, dual and even triply resonances conditions have been achieved
in photonic crystal cavities, micro-rings and microspheres, which leads to the
unprecedented device performances including Raman laser Takahashi et al.
(2013); Yu et al. (2020), frequency conversion Xue et al. (2017); Lu et al.
(2019), surface nonlinear optics Zhang et al. (2019) and on-chip optical
parametric oscillation with a record low threshold Marty et al. (2021). For
single semiconductor QDs, most studies are focused on the enhancements of the
emission process to pursue optimal single-photon sources Somaschi et al.
(2016); Wang et al. (2019b); Tomm et al. (2021). While the cavity enhanced
P-shell excitation Nomura et al. (2006), wetting layer excitation Kaniber et
al. (2009) and phonon assisted excitationMadsen et al. (2014) have been
observed by utilizing the high-order cavity modes of photonic nanocavities,
the dual-resonance enhanced excitation-emission process has not been reported
yet. In this work, we present versatile accessing of the dual-resonance
conditions in deterministically coupled QD-micopillars operating in the cavity
QED regime. By carefully engineering the fundamental mode and the high-order
mode of the micropillars, we have realized both up-converted and down-
converted single-photon emission under the dual-resonance condition. In
particular, the intra-dot transitions between the X and the CX in the down-
conversion process effectively suppress the carrier recapturing process by the
defects states in semiconductor and therefore improve the single-photon purity
of the emission. We further show that the excitation efficiency under dual-
resonance condition can be greatly improved by utilizing the vectorial
excitation beams with the same polarization states as the high-order cavity
modesFang and Wang (2017); Le Kien et al. (2017).

Figure 1: QD-micropillar system. (a) Schematic of the energy diagram of InAs
QD embedded in GaAs matrix. The quantum confinement of carriers in the QD
results in discrete energy levels. (b) Different excitation schemes for
triggering the single-photon emissions from exciton states in QDs used in the
work. All the excitation methods can be improved by the optical resonances
provided from microcavities. The black and red lines represent the optical
excitation and photon emission processes respectively while the blue lines
denote the non-radiative process. (c) Representative spectrum of a single QD
under the low-power above-band excitation. (d) Schematic of the coupled QD-
micropillar system. (e) The calculated radiation spectrum and the mode
profiles of the micropillar cavity with a diameter of 2.5 $\mu$m.

We use single InAs QDs embedded in a GaAs matrix as quantum emittersLodahl et
al. (2015); Buckley et al. (2012), as schematically shown in Fig. 1(a). Due to
the quantum confinement of carriers at nanoscale, the QD exhibit atomic-like
discrete energy levels, such as S-shell and P-shell. Carriers can be excited
by using a laser with the energy higher than the bandgap of GaAs, referred as
to above-band excitation (denoted by the thick black arrow). The created
carriers in the GaAs material then relax to the lowest excited states of the
QD via electron-phonon scattering before the radiative recombination process
of single-photon emissions. The longitudinal optical or acoustic phonons in
the solid-state provide additional degree of freedom over the atomic systems
to excite the QDs via both down- and up- conversion processesEnglund et al.
(2010); Quilter et al. (2015); Reindl et al. (2019); Pooley et al. (2012), as
shown in Fig. 1(b). More interestingly, the transition between exciton states
with different charge configurations (referred as to intra-dot excitation in
this work) can also be utilized to trigger the radative process as recently
demonstrated in two-dimensional semiconductor Jones et al. (2016). A
representative emission spectrum of a singe InAs QD under the low-power above-
band excitation is presented in Fig. 1(c), exhibiting a broad band GaAs band
edge emission, a wetting layer emission and sharp X and CX lines. To build a
coupled QD-micropillar system, the single InAs QD is embedded in the centre of
a semiconductor planar cavity consisting of a $\lambda$-thick GaAs spacer
sandwiched by GaAs/Al0.9Ga0.1As distributed Bragg reflectors (DBR) with 18(26)
top (bottom) pairs grown via molecular beam epitaxy. Micropillars are then
fabricated from the planar cavity in order to reduce the cavity mode volume
for further enhancing photon-exciton interaction, as schematically shown in
Fig. 1(d). Micropillar supports a series of cavity modes with sharp resonances
over a broad bandwidth Gérard et al. (1996); Reitzenstein and Forchel (2010).
In Fig. 1(e), the mode family of a 2.5 $\,\mu\text{m}$ micropillar with the
planar cavity resonance at 920 nm is calculated by the finite difference time
domain (FDTD) simulation with the insets representing the intensity profiles
of a few representative modes. Among all the cavity modes, the fundamental
mode HE11 exhibits the highest Q-factor, lowest mode volume and near Gaussian
far-field pattern, which is widely used for building high-performance single-
photon sources via the cavity QED effect Somaschi et al. (2016); Ding et al.
(2016); He et al. (2016); Su et al. (2018).

Figure 2: Identification of the cavity modes of the micropillar. (a) PL
spectra for the QD-micropillar excited by a laser with varied wavelengths. The
CX emission is resonant and therefore enhanced by the HE11 mode. Appreciable
CX emission is observed when excitation laser is resonant with one of the
high-order cavity modes (red spectrum). In the off-resonance condition, the CX
emission intensity is neglecgible. (black spectrum) Inset, SEM image of the
fabricated micropillar. (b). PLE spectrum (red) for the CX state resonant with
the HE11 mode and the PL spectrum (blue) of the QD-cavity system under high-
power above-band excitation condition. The high-power PL is shifted slightly
in y direction for clarity. Figure 3: Dual-resonance enhanced X-CX transition
for the highly pure single-photon emission. (a) Temperature dependent PL
mapping of a QD coupled to a 2.5 $\mu$m diameter micropillar cavity, the white
line is guide for the eyes. (b) Log scaled line cut of PL mapping in (a) from
a QD obtained under above-band (780 nm) excitation at temperature of 45 K. At
this temperature, the CX is tuned into resonance of the HE11 mode and X is
resonant with TE01 mode. (c) and (d) Hanbury Brown and Twiss (HBT)
measurements of single-photon purity for dual-resonance enhanced the intra-dot
excitation and the above-band excitation. Strong suppression of the carrier
recapturing process is observed via intra-dot excitation, leading to a
background-free $g^{(2)}(0)$ value as low as 0.01.

The deterministically coupled QD-micropillars are fabricated by using the
fluorescence imaging technique He et al. (2016); Liu et al. (2017a, b) which
ensures that the single QDs are both spectrally and spatially matched with the
fundamental cavity mode (HE11) of the micropillar. We first identify the mode
family of the micropillar by scanning the excitation laser through all the
high-order cavity modes and monitoring the emission from the CX state in
resonance with the fundamental cavity mode (HE11). The power of excitation
laser is kept as a constant level of 0.5 mW before the objective lens to avoid
saturating the QD. As along as the excitation laser is tuned to one of the
high-order cavity modes, bright CX emission is observed (red spectra). On the
contrary, the CX emission is barely detectable when the excitation laser is
detuned from any of the high-order cavity modes, as shown by the black
spectrum in Fig. 2(a). The identifications of the high-order cavity modes
under the dual-resonance condition is further quantified via photoluminescence
excitation (PLE) spectrum (red points) in Fig. 2(b) in which the emission
intensity of the CX state is plotted as a function of the wavelength of the
excitation laser. The high-order cavity modes are confirmed by another
independent experiment in which cavity modes are mapped from the PL spectrum
(blue curve) under high-power above-band excitation. In such a scenario, the
single QD serves as a broadband internal light sources to efficiently probe
all the cavity modes Smolka et al. (2011).

Figure 4: Dual resonances enhanced up-converted excitation. (a) High-power
above-band PL spectrum of a pillar with diameter of 2 $\mu$m (blue) and low-
power PL spectra of a QD excited under dual resonances (red) and single
resonance conditions (black). (b) Normalized high-power PL spectrum of HE11
mode (blue) and PLE spectrum of the QD acquired by sweeping the excitation
laser through HE11 mode and detecting the QD emission intensity (red), the
power of excitation laser is kept constant for PLE measurement.

We further show, for the first time, that the intra-dot transition process
between the exciton states in S-shell of a QD can be facilitated under the
dual-resonance condition, as recently demonstrated in two-dimensional
semiconductor Jones et al. (2016). Such a process enables the high-purity
single-photon emission due to the absence of carrier recapturing process by
the defect state in the semiconductor. Due to the Coulomb interactions of the
confined carriers, there is a slightly energy shift between the X and CX
Regelman et al. (2002); Ediger et al. (2007). The intra-dot transition is
implemented by tuning the excitation laser to match the energy of X state
(913.4 nm) and monitoring the emission from the CX state (917.02 nm). The
resonance condition can be reached simultaneously for both X and CX by tuning
the temperature of the sample, as shown by the temperature dependent spectra
in Fig. 3(a). At 45 K, the X and CX states are simultaneously resonant to the
TE01 and HE11 modes respectively, as presented in Fig. 3(b). The photon
statistics of the CX emission is examined by the Hanbury-Brown-Twiss (HBT)
interferometer and the coincidence histogram of the second-order correlation
function is presented in Fig. 3(c). Under the dual-resonance enhanced intra-
dot excitation, the coincidence event at the zero delay is almost vanishing
with a near-zero background across the whole histogram, indicating the
generation of single-photon emission with high-purity from the CX state. For
comparison, the photon statistics of the CX emission under the above-band
excitation condition (pumping at 780 nm) is presented in Fig. 3(d), which
shows significant background due to the carrier recapturing process by the
defect states in the semiconductor Dalgarno et al. (2008); Aichele et al.
(2004); Yang et al. (2020).

Figure 5: Power dependence of QD emission excited by vectorial beams. The CX
is resonant with HE11 mode, and excited via the azimuthally polarized TE01
mode (blue) (a) and the radially polarized TM01 mode (blue) (b) to match the
polarization states of the high-order cavity modes. For comparison, a linear
polarized Gaussian beam excitation (red) is implemented.

Comparing to the down-conversion process, the up-conversion process is more
challenging since it extracts energy out of the system. In the up-conversion
process, one low-energy photon in the excitation laser absorb an acoustic
phonon and results in the emission of a single photon from the exciton state.
Such processes have been recently employed to cool the mechanical motions of a
micro-resonator to its quantum ground state Chan et al. (2011); Verhagen et
al. (2012) or bulk temperature of semiconductors Zhang et al. (2013, 2016); Ha
et al. (2016), showing great potential in exploring fundamental quantum
physics and exploiting novel optical refrigeration method for nanophotonic
devices. As opposed to the dual-resonance enhanced down-conversion process,
the up-conversion process utilizes the fundamental mode to boost the optical
excitation and the high-order cavity modes to enhance the photon emission. As
shown in Fig. 4(a), the PL spectra under detuning conditions are presented.
Under the high-power above-band excitation condition (blue spectrum), the
cavity modes of HE11, TE01, HE21 and TM01 are clearly identified. The
excitation laser is then scanned across the HE11 mode to excite the X state
resonant with the HE21 mode. Under the dual-resonance condition (red
spectrum), bright X state emission is observed. Such emission is nearly
vanishing once the laser is either slightly red or blue-detuned from the HE11
mode (Black Spectra). The PLE spectrum of the HE11 mode matches excellently
with the cavity resonance observed in the high-power PL spectrum under above-
band excitation condition as shown in Fig. 4(b), indicating that the up-
conversion process is enhanced by the cavity. Further developments along this
direction may result in the realizations of reduced electron-phonon
interactions in the system and even the development of optical refrigeration
for single QDs.

Finally, we show that the efficiency of the down-conversion process under the
dual resonances condition can be further improved by engineering the
polarization state of the excitation beam Koshelev et al. (2020). Instead of
the linearly-polarized HE11 mode, the high-order cavity modes exhibit
vectorial polarizations, e.g., the TE01 mode is azimuthally polarized while
TM01 mode is radially polarized. Fig. 5(a) shows the single-photon emission
intensity of single photons from the CX state as a function of excitation
laser power at 913.5 nm (resonant with the TE01 mode) with different states of
polarization. While the emission intensity at the saturation power is the
same, the excitation power required to saturate the QD is reduced from 50
$\mu$W to 3 $\mu$W by switching the excitation laser from a linearly polarized
Gaussian beam to a radially polarized vortex beam. Similar behavior can be
observed for the excitation via the TM01 mode, as shown in Fig. 5(b), in which
the saturation powers is reduced by a factor of 7 by using the azimuthally
polarized excitation beam.

To conclude, we show versatile accessing of dual-resonance conditions for
enhancing the light matter interactions in QD-micropillar devices operating in
the cavity QED regime. The cavity mode family are independently identified in
both the PLE measurement and the high-power PL spectrum under the above-band
excitation condition. By exploiting the intra-dot excitation under the dual-
resonance condition, the singe-photon purity of emitted photons is greatly
improved compared to the above-band excitation condition due to the
suppression of the carrier recapture process by the defects in the
semiconductor. The up-converted emission is further demonstrated by using
excitation via fundamental cavity mode and emission at the high-order cavity
resonance. Such a process could be used to engineer electron- phonon
interactions and optical refrigeration of single QDs. By engineering the
polarization state of the excitation laser beam, the excitation efficiency can
be further boosted. The QD-micropillar system under dual-resonance condition
may serve as an ideal platform in solid state for investigating light matter
interaction in the quantum regime and developing integrated quantum photonic
devices with high-performances.

Acknowledgements The authors wish to thank Lin Liu and Li-Dan Zhou for
technical assistance in microfabrication. This research was supported by
National Key R&D Program of China (2018YFA0306100, 2018YFA0306302), the
National Natural Science Foundation of China (11874437, 61935009), Guangzhou
Science and Technology Project (201805010004), the Natural Science Foundation
of Guangdong (2018B030311027), the national super-computer center in
Guangzhou, the National Natural Science Foundation of China (12074442,
91836303), and the Local Innovative and Research Teams Project of Guangdong
Pearl River Talents Program (2017BT01X121).

## References

  * Vahala (2003) K. J. Vahala, Nature 424, 839 (2003).
  * Vollmer and Arnold (2008) F. Vollmer and S. Arnold, Nature Methods 5, 591 (2008), ISSN 15487091.
  * Baaske et al. (2014) M. D. Baaske, M. R. Foreman, and F. Vollmer, Nature Nanotechnology 9, 933 (2014), ISSN 17483395.
  * Toropov et al. (2021) N. Toropov, G. Cabello, M. P. Serrano, R. R. Gutha, M. Rafti, and F. Vollmer, Light: Science and Applications 10 (2021), ISSN 20477538\.
  * Liu et al. (2019a) Z. Liu, Y. Xu, Y. Lin, J. Xiang, T. Feng, Q. Cao, J. Li, S. Lan, and J. Liu, Physical Review Letters 123, 1 (2019a), ISSN 10797114\.
  * Liu et al. (2018a) H. Liu, C. Guo, G. Vampa, J. L. Zhang, T. Sarmiento, M. Xiao, P. H. Bucksbaum, J. Vučković, S. Fan, and D. A. Reis, Nature Physics 14, 1006 (2018a), ISSN 17452481\.
  * Gérard et al. (1998) J. M. Gérard, B. Sermage, B. Gayral, B. Legrand, E. Costard, and V. Thierry-Mieg, Physical Review Letters 81, 1110 (1998), ISSN 10797114.
  * Liu et al. (2018b) F. Liu, A. J. Brash, J. O’Hara, L. M. Martins, C. L. Phillips, R. J. Coles, B. Royall, E. Clarke, C. Bentham, N. Prtljaga, et al., Nature Nanotechnology 13, 835 (2018b), ISSN 17483395.
  * Liu et al. (2019b) J. Liu, R. Su, Y. Wei, B. Yao, S. F. C. da Silva, Y. Yu, J. Iles-Smith, K. Srinivasan, A. Rastelli, J. Li, et al., Nature Nanotechnology 14, 586 (2019b), ISSN 17483395\.
  * Wang et al. (2019a) H. Wang, H. Hu, T. H. Chung, J. Qin, X. Yang, J. P. Li, R. Z. Liu, H. S. Zhong, Y. M. He, X. Ding, et al., Physical Review Letters 122, 1 (2019a), ISSN 10797114\.
  * Takahashi et al. (2013) Y. Takahashi, Y. Inui, M. Chihara, T. Asano, R. Terawaki, and S. Noda, Nature 498, 470 (2013), ISSN 00280836.
  * Yu et al. (2020) M. Yu, Y. Okawachi, R. Cheng, C. Wang, M. Zhang, A. L. Gaeta, and M. Lončar, Light: Science and Applications 9, 9 (2020), ISSN 20477538\.
  * Xue et al. (2017) X. Xue, F. Leo, Y. Xuan, J. A. Jaramillo-Villegas, P. H. Wang, D. E. Leaird, M. Erkintalo, M. Qi, and A. M. Weiner, Light: Science and Applications 6, e16253 (2017), ISSN 20477538.
  * Lu et al. (2019) X. Lu, G. Moille, Q. Li, D. A. Westly, A. Singh, A. Rao, S. P. Yu, T. C. Briles, S. B. Papp, and K. Srinivasan, Nature Photonics 13, 593 (2019), ISSN 17494893.
  * Zhang et al. (2019) X. Zhang, Q. T. Cao, Z. Wang, Y. xi Liu, C. W. Qiu, L. Yang, Q. Gong, and Y. F. Xiao, Nature Photonics 13, 21 (2019), ISSN 17494893.
  * Marty et al. (2021) G. Marty, S. Combrié, F. Raineri, and A. De Rossi, Nature Photonics 15, 53 (2021), ISSN 17494893.
  * Somaschi et al. (2016) N. Somaschi, V. Giesz, L. De Santis, J. C. Loredo, M. P. Almeida, G. Hornecker, S. L. Portalupi, T. Grange, C. Antón, J. Demory, et al., Nature Photonics 10, 340 (2016), ISSN 17494893.
  * Wang et al. (2019b) H. Wang, Y.-M. He, T.-H. Chung, H. Hu, Y. Yu, S. Chen, X. Ding, M.-C. Chen, J. Qin, X. Yang, et al., Nature Photonics 13, 770 (2019b).
  * Tomm et al. (2021) N. Tomm, A. Javadi, N. O. Antoniadis, D. Najer, M. C. Löbl, A. R. Korsch, R. Schott, S. R. Valentin, A. D. Wieck, A. Ludwig, et al., Nature Nanotechnology 16, 399 (2021).
  * Nomura et al. (2006) M. Nomura, S. Iwamoto, T. Yang, S. Ishida, and Y. Arakawa, Applied Physics Letters 89, 1 (2006), ISSN 00036951.
  * Kaniber et al. (2009) M. Kaniber, A. Neumann, A. Laucht, M. F. Huck, M. Bichler, M. C. Amann, and J. J. Finley, New Journal of Physics 11 (2009), ISSN 13672630.
  * Madsen et al. (2014) K. H. Madsen, S. Ates, J. Liu, A. Javadi, S. M. Albrecht, I. Yeo, S. Stobbe, and P. Lodahl, Physical Review B - Condensed Matter and Materials Physics 90, 1 (2014), ISSN 1550235X.
  * Fang and Wang (2017) L. Fang and J. Wang, Physical Review A 95, 1 (2017), ISSN 24699934.
  * Le Kien et al. (2017) F. Le Kien, T. Busch, V. G. Truong, and S. Nic Chormaic, Physical Review A 96 (2017), ISSN 24699934\.
  * Lodahl et al. (2015) P. Lodahl, S. Mahmoodian, and S. Stobbe, Reviews of Modern Physics 87, 347 (2015), ISSN 15390756.
  * Buckley et al. (2012) S. Buckley, K. Rivoire, and J. Vučković, Reports on Progress in Physics 75 (2012), ISSN 00344885\.
  * Englund et al. (2010) D. Englund, A. Majumdar, A. Faraon, M. Toishi, N. Stoltz, P. Petroff, and J. Vučković, Physical Review Letters 104, 1 (2010), ISSN 00319007.
  * Quilter et al. (2015) J. H. Quilter, A. J. Brash, F. Liu, M. Glässl, A. M. Barth, V. M. Axt, A. J. Ramsay, M. S. Skolnick, and A. M. Fox, Physical Review Letters 114, 1 (2015), ISSN 10797114.
  * Reindl et al. (2019) M. Reindl, J. H. Weber, D. Huber, C. Schimpf, S. F. Covre Da Silva, S. L. Portalupi, R. Trotta, P. Michler, and A. Rastelli, Physical Review B 100, 155420 (2019), ISSN 24699969\.
  * Pooley et al. (2012) M. A. Pooley, D. J. Ellis, R. B. Patel, A. J. Bennett, K. H. Chan, I. Farrer, D. A. Ritchie, and A. J. Shields, Applied Physics Letters 100 (2012), ISSN 00036951\.
  * Jones et al. (2016) A. M. Jones, H. Yu, J. R. Schaibley, J. Yan, D. G. Mandrus, T. Taniguchi, K. Watanabe, H. Dery, W. Yao, and X. Xu, Nature Physics 12, 323 (2016), ISSN 17452481.
  * Gérard et al. (1996) J. M. Gérard, D. Barrier, J. Y. Marzin, R. Kuszelewicz, L. Manin, E. Costard, V. Thierry-Mieg, and T. Rivera, Applied Physics Letters 69, 449 (1996), ISSN 00036951.
  * Reitzenstein and Forchel (2010) S. Reitzenstein and A. Forchel, Journal of Physics D: Applied Physics 43 (2010), ISSN 00223727\.
  * Ding et al. (2016) X. Ding, Y. He, Z. C. Duan, N. Gregersen, M.-C. Chen, S. Unsleber, S. Maier, C. Schneider, M. Kamp, S. Höfling, et al., Phys. Rev. Lett 116, 020401 (2016), ISSN 17381460.
  * He et al. (2016) Y.-M. He, J. Liu, S. Maier, M. Emmerling, S. Gerhardt, M. Davanco, K. Srinivasan, C. Schneider, and S. Höfling, Optica 4 (2016), ISSN 2334-2536.
  * Su et al. (2018) R. Su, S. Liu, Y. Wei, Y. Yu, J. Liu, S. Yu, and X. Wang, Science Bulletin 63, 739 (2018), ISSN 20959281.
  * Liu et al. (2017a) S. Liu, Y. Wei, R. Su, R. Su, B. Ma, Z. Chen, H. Ni, Z. Niu, Y. Yu, Y. Wei, et al., Scientific Reports 7, 1 (2017a), ISSN 20452322\.
  * Liu et al. (2017b) J. Liu, M. I. Davanço, L. Sapienza, K. Konthasinghe, J. V. De Miranda Cardoso, J. D. Song, A. Badolato, and K. Srinivasan, Review of Scientific Instruments 88, 1 (2017b), ISSN 10897623, eprint 1612.07412.
  * Smolka et al. (2011) S. Smolka, H. Thyrrestrup, L. Sapienza, T. B. Lehmann, K. R. Rix, L. S. Froufe-Pérez, P. D. García, and P. Lodahl, New Journal of Physics 13 (2011), ISSN 13672630.
  * Regelman et al. (2002) D. V. Regelman, D. Gershoni, E. Ehrenfreund, W. V. Schoenfeld, and P. M. Petroff, Physica E: Low-Dimensional Systems and Nanostructures 13, 114 (2002), ISSN 13869477.
  * Ediger et al. (2007) M. Ediger, G. Bester, A. Badolato, P. M. Petroff, K. Karrai, A. Zunger, and R. J. Warburton, Nature Physics 3, 774 (2007), ISSN 17452481.
  * Dalgarno et al. (2008) P. A. Dalgarno, J. McFarlane, D. Brunner, R. W. Lambert, B. D. Gerardot, R. J. Warburton, K. Karrai, A. Badolato, and P. M. Petroff, Applied Physics Letters 92, 1 (2008), ISSN 00036951.
  * Aichele et al. (2004) T. Aichele, V. Zwiller, and O. Benson, New Journal of Physics 6, 1 (2004), ISSN 13672630.
  * Yang et al. (2020) J. Yang, C. Nawrath, R. Keil, R. Joos, X. Zhang, B. Höfer, Y. Chen, M. Zopf, M. Jetter, S. Luca Portalupi, et al., Optics Express 28, 19457 (2020), ISSN 1094-4087.
  * Chan et al. (2011) J. Chan, T. P. Alegre, A. H. Safavi-Naeini, J. T. Hill, A. Krause, S. Gröblacher, M. Aspelmeyer, and O. Painter, Nature 478, 89 (2011), ISSN 00280836.
  * Verhagen et al. (2012) E. Verhagen, S. Deléglise, S. Weis, A. Schliesser, and T. J. Kippenberg, Nature 482, 63 (2012), ISSN 00280836.
  * Zhang et al. (2013) J. Zhang, D. Li, R. Chen, and Q. Xiong, Nature 493, 504 (2013), ISSN 00280836.
  * Zhang et al. (2016) J. Zhang, Q. Zhang, X. Wang, L. C. Kwek, and Q. Xiong, Nature Photonics 10, 600 (2016), ISSN 17494893.
  * Ha et al. (2016) S. T. Ha, C. Shen, J. Zhang, and Q. Xiong, Nature Photonics 10, 115 (2016), ISSN 17494893.
  * Koshelev et al. (2020) K. Koshelev, S. Kruk, E. Melik-Gaykazyan, J. H. Choi, A. Bogdanov, H. G. Park, and Y. Kivshar, Science 367, 288 (2020), ISSN 10959203.