Yang Sun, Jiayang Wu, Mengxi Tan, Xingyuan Xu, Yang Li, Roberto Morandotti, Arnan Mitchell, and David J. Moss, "Applications of optical microcombs," Adv. Opt. Photon. 15, 86-175 (2023)
Optical microcombs represent a new paradigm for generating laser
frequency combs based on compact chip-scale devices, which have
underpinned many modern technological advances for both fundamental
science and industrial applications. Along with the surge in activity
related to optical microcombs in the past decade, their applications
have also experienced rapid progress: not only in traditional fields
such as frequency synthesis, signal processing, and optical
communications but also in new interdisciplinary fields spanning the
frontiers of light detection and ranging (LiDAR), astronomical
detection, neuromorphic computing, and quantum optics. This paper
reviews the applications of optical microcombs. First, an overview of
the devices and methods for generating optical microcombs is provided,
which are categorized into material platforms, device architectures,
soliton classes, and driving mechanisms. Second, the broad
applications of optical microcombs are systematically reviewed, which
are categorized into microwave photonics, optical communications,
precision measurements, neuromorphic computing, and quantum optics.
Finally, the current challenges and future perspectives are
discussed.
Data underlying the results presented in this paper are not publicly
available at this time but may be obtained from the authors upon
reasonable request.
Cited By
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Figure 2. (a) Chronology for different applications of optical microcombs,
including the first demonstrations of microwave frequency
synthesis [20], microwave
photonic filtering [29],
optical communications [41], microwave photonic signal processing [34], generation of multiphoton
quantum states [59],
dual-comb spectroscopy [60], ranging [46,47], single
neuron [54], astrocombs
[50,51], and convolutional neural networks [55,56]. (b) Number of publications on different
applications of optical microcombs in Science Citation Index
journals versus year since 2012. Data were taken from ISI Web of
Science.
Figure 3. Development roadmap of optical microcombs based on various
material platforms. Values of wavelength ranges are taken
from: Ref. [10] for
2007, Ref. [96] for
2008, Ref. [75] for
2009, Ref. [68] for
2010 (Hydex), Ref. [69] for 2010 (Si3N4), Ref.
[63] for 2011
(SiO2), Ref. [72] for 2011 (MgF2), Refs. [20,97] for 2012, Ref. [73] for 2013 (MgF2), Ref.
[71] for 2013
(SiO2), Ref. [98] for 2014 (AlN), Ref. [85] for 2014 (diamond), Ref. [65] for 2014
(MgF2), Ref. [79] for 2015 (Si), Ref. [74] for 2015 (MgF2), Ref.
[99] for 2015
(Si3N4), Ref. [86] for 2016 (AlGaAs), Ref. [82] for 2016
(Si3N4), Ref. [80] for 2016 (Si), Refs. [100,101] for 2017
(Si3N4), Ref. [102] for 2017 (SiO2), Refs.
[103,104] for 2018
(Si3N4), Ref. [105] for 2018 (Hydex), Refs. [94,95] for 2019 (LiNbO3), Ref.
[106] for 2019
(Hydex), Ref. [107] for 2019 (MgF2), Ref. [108] for 2020
(LiNbO3), Ref. [109] for 2020 (SiO2), Refs.
[110,111] for 2020
(Si3N4), Ref. [88] for 2020 (AlGaAs), Ref. [92] for 2020 (GaP), Ref.
[112] for 2021
(Hydex), Ref. [90]
for 2021 (SiC), Ref. [113] for 2021 (AlN), Ref. [114] for 2021
(Si3N4), and Ref. [91] for 2021
(Ta2O5).
Figure 5. Recent progress in high-volume manufacturing of optical
microcomb devices. (a) Microcomb generator based on
integrated silica ridge resonators with ultrahigh
Q factors. Reprinted by permission from
Nature Publishing Group: Yang et
al., Nat. Photonics 12, 297
(2018) [138]. (b)
Battery-operated microcomb generator. Reprinted by
permission from Nature Publishing Group: Stern
et al., Nature
562, 401 (2018) [139]. (c) Electrically pumped microcomb
generation by using a commercial InP laser diode.
Reprinted from [140] under a CC-BY license. (d) Heterogeneously
integrated microcomb generators on a silicon substrate.
Reprinted from [135] under a CC-BY license. (e) A microcomb generator
based on a high-power, low-noise laser fully integrated
with a Si3N4 MRR. Reprinted from
[136] under
a CC-BY license.
Figure 8. (a) Allan deviations (at an integration time of 1 s) and (b)
SSB phase noise (at 1-kHz, 10-kHz, and 10-MHz offset
frequencies) versus synthesized frequencies for frequency
synthesizers based on different microcombs as well as LFCs
generated by solid-state lasers (SSLs) and mode-locked fiber
lasers (MLFLs). Values of Allan deviations and SSB phase noise
are taken from Ref. [24] for the 16.82-GHz, 6.73-GHz, and 7.05-GHz signals
synthesized based on microcombs generated by a
Si3N4 MRR, Ref. [27] for the 100-GHz signal synthesized by
microcombs generated by a Si3N4 MRR, and
Ref. [28] for the
300-GHz signal synthesized by microcombs generated by a
Si3N4 MRR. Values of Allan deviations
are taken from Ref. [24] for the 4.7-GHz signal synthesized based on
microcombs generated by a MgF2
whispering-gallery-mode (WGM) resonator, Ref. [16] for the 0.01-GHz signal
synthesized based on LFCs generated by a solid-state laser,
Ref. [206] for the
10-GHz signal synthesized based on LFCs generated by two SSLs,
Ref. [207] for the
10-GHz signal synthesized based on LFCs generated by two
MLFLs, Ref. [26] for
the 10-GHz and 20-GHz signals synthesized based on microcombs
generated by two Si3N4 MRRs, Ref. [22] for the 14-GHz signal
synthesized based on microcombs generated by a MgF2
WGM resonator, Ref. [144] for the 22-GHz signal synthesized by microcombs
generated by a SiO2 wedge resonator, values are
taken from Thorlabs, Inc. for the
0.1-GHz [208] and the 0.25-GHz [209] signals synthesized by commercial LFCs
generated by mode-locked fiber lasers. Values of SSB phase
noise are taken from Ref. [210] for the 11.55-GHz signal synthesized based on
LFCs generated by two MLFLs, Ref. [23] for the 14-GHz signal synthesized based
on microcombs generated by a MgF2 WGM resonator,
Ref. [211] for 18-GHz,
24-GHz, 30-GHz, and 36-GHz signal synthesized based on
microcombs generated by a MgF2 WGM resonator, Ref.
[25] for 20-GHz signal
synthesized based on microcombs generated by a Hydex MRR, Ref.
[20] for 22-GHz signal
synthesized based on microcombs generated by a SiO2
microdisk resonator.
Figure 9. Recent progress in microcomb-based frequency synthesizers. (a)
Microwave frequency synthesizers in the X and K bands based on
soliton microcombs generated by Si3N4
MRRs. Reprinted by permission from Nature Publishing Group:
Liu et al., Nat. Photonics
14, 486 (2020) [26]. (b) 100-GHz microwave frequency synthesizer based
on soliton microcombs generated by a
Si3N4 MRR [27]. (c) Microwave frequency synthesizer
using a transfer oscillator approach to divide the repetition
rate of a soliton microcomb [23]. (d) Microwave frequency synthesizer realized by
injecting a soliton microcomb into a GSL [24]. (b), (c), and (d)
Reprinted under a
CC-BY license. (e) Optical frequency synthesizer
with f–2f
stabilization based on two soliton microcombs generated by a
Si3N4 MRR and a SiO2
microdisk. Reprinted by permission from Nature Publishing
Group: Spencer et al.,
Nature 557, 81 (2018) [21].
Figure 10. Influence of tap number on the performance of microcomb-based
microwave photonic filters. (a) Low-pass filters with the same
cut-off frequency of 1 GHz but different tap numbers
ranging from 10 to 80. (b) Band-pass filters with the same
center frequency of 10 GHz but different tap numbers
ranging from 10 to 80. In (a) and (b), (i), (ii), and (iii)
show the simulated transmission spectra, the filtering
resolutions (i.e., 3-dB bandwidth) versus tap numbers, and the
MSSRs versus tap numbers, respectively.
Figure 12. Influence of tap number on the performance of microcomb-based
microwave photonic signal processors. (a) First-order
differentiators with different tap numbers ranging from 10 to
80. (b) 0.5-order differentiators with different tap numbers
ranging from 10 to 80. In (a) and (b), (i), (ii), (iii), and
(iv) show the simulated amplitude responses, phase responses,
the output waveforms for Gaussian input signal, and the
root-mean-square errors (RMSEs) between the output waveforms
and the ideal results versus tap numbers, respectively.
Figure 15. Coherent optical communications based on optical microcombs.
(a) ∼55.0 Tbit/s coherent communication over
75 km based on two interleaved soliton microcombs
generated by Si3N4 MRRs. Reprinted by
permission from Nature Publishing Group: Marin-Palomo
et al., Nature
546, 274 (2017) [42]. (b) ∼4.4 Tbit/s coherent communication
over 80 km based on a mode-locked dark-pulse microcomb
generated by a normal-dispersion Si3N4
MRR. Reprinted from [43] under a
CC-BY license. (c) ∼44.2 Tbit/s coherent
communication over 76.6 km based on a soliton crystal
microcomb generated by a Hydex MRR. Reprinted from [44] under a
CC-BY license. (d) ∼1.68 Tbit/s coherent
communication over 50 km based on coherence-cloned
soliton microcombs generated by Si3N4
MRRs. Reprinted from [252] under a
CC-BY license.
Figure 18. Ranging based on optical microcombs. (a)
Ultrafast ranging of moving targets based on soliton
microcombs generated by two Si3N4 MRRs.
(b) Distance measurement based on counterpropagating soliton
microcombs generated by a single silica wedge resonator. (c)
Spectrally resolved laser ranging with nanometric precision
based on a soliton microcomb generated by a tapered
Si3N4 MRR. (d) Massively parallel
ranging using a soliton microcomb generated by a
Si3N4 MRR pumped by chirped laser. (a)
From [46]. Reprinted
with permission from AAAS. Trocha et
al., Science 359, 887–891
(2018). (b) From [47].
Reprinted with permission from AAAS. Suh and Vahala, Science
359, 884–887 (2018). (c) Figure 1 reprinted with
permission from Bao et al.,
Phys. Rev. Lett. 126, 023903 (2021) [266]. Copyright 2021 by the
American Physical Society. (d) Reprinted by permission from
Nature Publishing Group: Riemensberger et
al., Nature 581, 164 (2020)
48]. Copyright
2020.
Figure 19. Astrocombs based on optical microcombs. (a) A spectrograph
calibrator based on a soliton microcomb generated by a
SiO2 microdisk resonator. (b) A microphotonic
astrocomb based on soliton microcomb generated by a
Si3N4 MRR. (a) Reprinted by permission
from Nature Publishing Group: Suh et al.,
Nat. Photonics 13, 25–30 (2019) [51]. Copyright 2019. (b)
Reprinted with permission from Nature Publishing Group: Obrzud
et al., Nat. Photonics
13, 31–35 (2019) [50]. Copyright 2019.
Figure 22. Reconfigurable single neuron based on optical microcombs. (a)
Experimental setup. (b) Experimental results for
classification of handwritten digits and cancer cells.
Reprinted with permission from Feldmann et
al., Laser Photon. Rev. 14, 2000070
(2020) [54].
Figure 23. CNNs based on optical microcombs. (a) Schematic of a
convolution computation accelerator. Parallel operations are
achieved by using multiple wavelengths derived from a soliton
microcomb generated by a Si3N4 MRR. (b)
Operation principle of an optical CNN based on
time–wavelength interleaving using a soliton crystal
microcomb generated by a Hydex MRR. (a) Reprinted with
permission from Nature Publishing Group: Feldmann et
al., Nature 589, 52–58 (2021) [55]. Copyright 2021. (b)
Reprinted with permission from Nature Publishing Group: Xu
et al, Nature 589, 44–51 (2021)
[56]. Copyright
2021.
Figure 26. Comparison of the state-of-the-art material platforms for optical
microcomb generation. (a) Materials’ bandgaps and cut-off
wavelengths versus their Kerr nonlinear coefficients
(n2). (b) Microresonators’
quality (Q) factors versus the materials’
n2. (c) Materials’ bandgaps and
cut-off wavelengths versus microresonators’ FSRs in the
microwave frequency band (0.3‒300 GHz). Values of
n2, bandgap and cut-off wavelength are
taken from Ref. [83] for
MgF2 and CaF2, Ref. [91] for Ta2O5, Ref. [92] for SiO2, diamond,
Hydex, AlN, Si3N4, SiC, Si, GaP, and AlGaAs,
and Ref. [95] for
LiNbO3. Values of Q factors are taken
from Refs. [65,73,74] for MgF2, Refs. [21,63,102,109,331] for SiO2, Refs.
[75,76] for CaF2, Ref. [85] for
diamond,Refs. [93,95,108] for
LiNbO3, Refs. [68,112] for Hydex,
Ref. [98] for AlN, Refs.
[81,82,99,114,139] for
Si3N4, Ref. [91] for Ta2O5, Ref. [90] for SiC, Refs. [79,332] for Si, Ref. [92] for GaP, and Refs. [86–88] for AlGaAs. Values of FSRs
are taken from Refs. [65,73,74] for MgF2, Refs.
[21,63,102]
for SiO2, Refs. [75,76] for
CaF2, Ref. [85]
for diamond, Refs. [93,95,108] for LiNbO3,
Refs. [30,68,333] for Hydex, Ref. [98] for AlN, Refs. [26,69,81,82,99,114,139] for
Si3N4, Ref. [90] for SiC, Refs. [79] for Si, Ref. [92] for GaP, and Ref. [87] for AlGaAs.
This value is taken from Fig. 4(a) in Ref. [225].
This value is taken from Figs. 2 and 3 in Ref. [225].
The demonstrated operation bandwidth was limited by the
oscilloscope.
Table 6.
Performance Comparison of Microcomb-Based Optical Communication
Systems
Operations per second, i.e., floating-point operations per
second.
Scalability and reconfigurability: Level 1, the synaptic
weights can hardly be reconfigured; Level 2, the synaptic
weights can be reconfigured, but the network structure
(i.e., the number of layers and neurons in each layer) can
hardly be reconfigured; Level 3, the synaptic weights and
network structure can be reconfigured.
Continuous-wave light source is used in the architecture as
the input data signal, and high-speed updating of the
input data is not demonstrated to achieve a high computing
speed.
Convolution operations per second here.
Table 9.
Comparison of Squeezed Light Generation Based on Optical
Microcombs
This value is taken from Fig. 4(a) in Ref. [225].
This value is taken from Figs. 2 and 3 in Ref. [225].
The demonstrated operation bandwidth was limited by the
oscilloscope.
Table 6.
Performance Comparison of Microcomb-Based Optical Communication
Systems
Operations per second, i.e., floating-point operations per
second.
Scalability and reconfigurability: Level 1, the synaptic
weights can hardly be reconfigured; Level 2, the synaptic
weights can be reconfigured, but the network structure
(i.e., the number of layers and neurons in each layer) can
hardly be reconfigured; Level 3, the synaptic weights and
network structure can be reconfigured.
Continuous-wave light source is used in the architecture as
the input data signal, and high-speed updating of the
input data is not demonstrated to achieve a high computing
speed.
Convolution operations per second here.
Table 9.
Comparison of Squeezed Light Generation Based on Optical
Microcombs