On-demand transfer of trapped photons on a chip

Konoike, Ryotaro; Nakagawa, Haruyuki; Nakadai, Masahiro; Asano, Tanemasa; Tanaka, Yoshinori; Noda, Susumu

doi:10.1126/sciadv.1501690

Cited by 46 publications

(27 citation statements)

References 19 publications

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“…There have been various efforts to increase the Q factors and/or Q/V of 2D-PC slab nanocavities both in theory and experiments [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. The developed PC nanocavities have been used for various applications including ultracompact channel add/drop devices [1], nano-lasers [17], laser arrays for sensing [18], strongly coupled light-matter systems in solids [19,20], ultra-low-power consumption optical bi-stable systems [21], ultracompact and low-threshold all-Si Raman lasers [22], and photonic buffer memories [23][24][25]. However, further improvement is desirable for the realization of more advanced applications.…”

Section: Introductionmentioning

confidence: 99%

Optimization of photonic crystal nanocavities based on deep learning

2018

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An approach to optimizing the Q factors of two-dimensional photonic crystal (2D-PC) nanocavities based on deep learning is proposed and demonstrated. We prepare a dataset consisting of 1000 nanocavities generated by randomly displacing the positions of many air holes of a base nanocavity and their Q factors calculated by a first-principle method. We train a four-layer neural network including a convolutional layer to recognize the relationship between the air holes' displacements and the Q factors using the prepared dataset. After the training, the neural network becomes able to estimate the Q factors from the air holes' displacements with an error of 13% in standard deviation. Crucially, the trained neural network can estimate the gradient of the Q factor with respect to the air holes' displacements very quickly based on back-propagation. A nanocavity structure with an extremely high Q factor of 1.58  10 9 is successfully obtained by optimizing the positions of 50 air holes over ~10 6 iterations, having taken advantage of the very fast evaluation of the gradient in high-dimensional parameter space. The obtained Q factor is more than one order of magnitude higher than that of the base cavity and more than twice that of the highest Q factors reported so far for cavities with similar modal volumes. This approach can optimize 2D-PC structures over a parameter space of a size unfeasibly large for previous optimization methods based solely on direct calculations. We believe this approach is also useful for improving other optical characteristics. Fig. 8. Optimized structures and their Q factors, electric field distributions (Ey), and modal volumes (Vcav).

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Section: Introductionmentioning

confidence: 99%

Optimization of photonic crystal nanocavities based on deep learning

2018

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“…[15][16][17] Therefore, the present demonstration of ultrahigh-Q nanocavities for both O band and C band implies that most Si PC devices for the O/C bands can be implemented with the dual thickness substrate while maintaining the performance. [8][9][10][11][18][19][20][21][22][23]…”

Section: Doi: 101002/lpor201800258mentioning

confidence: 99%

Ultrahigh‐Q Photonic Nanocavity Devices on a Dual Thickness SOI Substrate Operating at Both 1.31‐ and 1.55‐µm Telecommunication Wavelength Bands

Kuwabara

Noda

Takahashi

2019

Laser & Photonics Reviews

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A feasible method for integrating several silicon (Si) photonic devices with operating wavelengths separated by several hundred nanometers on a single chip will greatly help increasing capacities of small optical communication modules. This work demonstrates the integration of two photonic crystal nanocavity devices that exhibit ultrahigh quality factors (Q) and operate at the 1.31‐ and 1.55‐µm bands. A dual thickness Si‐on‐insulator substrate forms the base of the device. The two nanocavity patterns are defined by electron beam lithography on the thick and thin substrate regions and are transferred to the top Si layer by performing plasma etching only once. All dimensions of the fabricated 1.31‐µm nanocavity are ≈15.5% smaller (1–1.31/1.55) than those of the 1.55‐µm nanocavity; that is, they can be treated with the same photonic band diagram. Both nanocavities exhibit an ultrahigh Q > 2.0 × 106 and enable fabrication of nanocavity‐based Raman lasers for the 1.31/1.55‐µm bands with sub‐microwatt threshold.

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“…However, owing to the relatively small third‐order optical nonlinearity of conventional materials, the reported threshold operating control‐light intensity reached several GW/cm 2 even though nonlinearity enhancement associated with photonic crystal microcavities (having a feature device size of several microns) and two‐photon absorption have been adopted . This switching technique has important applications in the fields of cascaded and complicated logic processing circuits and quantum solid chips …”

Section: Introductionmentioning

confidence: 99%

“…[4][5][6][7] This switching technique has important applications in the fields of cascaded and complicated logic processing circuits and quantum solid chips. [18,19] This paper contains a report of ultrafast on-chip remotely-triggered nanoscale all-optical switching with high switching efficiency and low power, directly in integrated photonic circuits. It consists of a bus signal waveguide side-coupled silicon photonic crystal nanocavity B, which is indirectly-coupled with a distant silicon photonic crystal nanocavity A via two long silicon photonic crystal waveguides, covered with monolayer graphene, multicomponent nanocomposite, and an uppermost control waveguide.…”

Section: Introductionmentioning

confidence: 99%

Ultrafast on‐Chip Remotely‐Triggered All‐Optical Switching Based on Epsilon‐Near‐Zero Nanocomposites

Chai

Wang

et al. 2017

Laser & Photonics Reviews

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On‐chip‐triggered all‐optical switching is a key component of ultrahigh‐speed and ultrawide‐band information processing chips. This switching technique, the operating states of which are triggered by a remote control light, paves the way for the realization of cascaded and complicated logic processing circuits and quantum solid chips. Here, a strategy is reported to realize on‐chip remotely‐triggered, ultralow‐power, ultrafast, and nanoscale all‐optical switching with high switching efficiency in integrated photonic circuits. It is based on control‐light induced dynamic modulation of the coupling properties of two remotely‐coupled silicon photonic crystal nanocavities, and extremely large optical nonlinearity enhancement associated with epsilon‐near‐zero multi‐component nanocomposite achieved through dispersion engineering. Compared with previous reports of on‐chip direct‐triggered all‐optical switching, the threshold control intensity, 560 kW/cm2, is reduced by four orders of magnitude, while maintaining ultrafast switching time of 15 ps. This not only provides a strategy to construct photonic materials with ultrafast and large third‐order nonlinearity, but also offers an on‐chip platform for the fundamental study of nonlinear optics.

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On-demand transfer of trapped photons on a chip

Abstract: Researchers experimentally demonstrate on-demand transfer of trapped photons on a photonic crystal chip for the first time.

Cited by 46 publications

References 19 publications

Optimization of photonic crystal nanocavities based on deep learning

Optimization of photonic crystal nanocavities based on deep learning

Ultrahigh‐Q Photonic Nanocavity Devices on a Dual Thickness SOI Substrate Operating at Both 1.31‐ and 1.55‐µm Telecommunication Wavelength Bands

Ultrafast on‐Chip Remotely‐Triggered All‐Optical Switching Based on Epsilon‐Near‐Zero Nanocomposites

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