Integrating photonics with silicon nanoelectronics for the next generation of systems on a chip

Atabaki, Amir H.; Moazeni, Sajjad; Pavanello, Fabio; Gevorgyan, Hayk; Notaros, Jelena; Alloatti, Luca; Wade, Mark T.; Sun, Chen; Kruger, Seth; Meng, Huaiyu; Qubaisi, Kenaish Al; Wang, Imbert; Zhang, Bohan; Khilo, Anatol; Baiocco, Christopher; Popović, Miloš A.; Stojanović, Vladimir; Ram, Rajeev J.

doi:10.1038/s41586-018-0028-z

Cited by 644 publications

(377 citation statements)

References 35 publications

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“…Second, SOI platform has a extremely wide transparency window from near-infrared to mid-infrared wavelength, which can implement the optical field with low insertion loss. In addition, the compatibility with well-established CMOS processes will combine photonic technologies and electronic technologies, enabling us to achieve desirable performance, scalability, functionality and low cost simultaneously for electronic-photonic systems [15].…”

Section: Introductionmentioning

confidence: 99%

Silicon-based multimode waveguide crossings

Chang

Zhang

2020

J. Phys. Photonics

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Mode multiplexing technique is a new promising option to increase the transmission capacity of on-chip optical interconnects. Multimode waveguide crossings are the key building blocks in high-density and large-scale mode division multiplexing silicon photonic integrated circuits. In this paper, we review the recent progresses on silicon-based multimode waveguide crossings. Firstly, a variety of multimode waveguide crossing schemes are demonstrated and introduced including conventional multimode interference coupler, Maxwell's fisheye lens and inverse-designed multimode interference coupler. Secondly, we also discuss some emerging applications of the inverse design algorithm in the multimode silicon devices to realize ultracompact footprint and multiple functionalities. Finally, we also give the outlook of the development prospects of on-chip multimode waveguide crossings.

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Section: Introductionmentioning

confidence: 99%

Silicon-based multimode waveguide crossings

Chang

Zhang

2020

J. Phys. Photonics

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“…The high capacity of information such as big data can be performed and implemented by the internet of things (IoT) . Moreover, the use of materials such as GaAsInP‐P, silicon, graphene, and chalcogenide for waveguides and devices can provide wider applications . Recently, the use of the stacked layers of silicon‐graphene‐gold has shown the very interesting results, where the conversion between electrical and optical signals can be made as following details.…”

Section: Introductionmentioning

confidence: 99%

“…[8][9][10][11][12] Moreover, the use of materials such as GaAsInP-P, silicon, graphene, and chalcogenide for waveguides and devices can provide wider applications. [13][14][15][16][17][18] Recently, the use of the stacked layers of silicon-graphene-gold has shown the very interesting results, 19,20 where the conversion between electrical and optical signals can be made as following details. The conversion from light to be electrical signals occurs when light from a laser source is fed into the silicon layer, from which the plasmonic waves are induced on the graphene surface and conducted by the gold layer.…”

Section: Introductionmentioning

confidence: 99%

Electro‐optic conversion circuit incorporating a fiber optic loop for light fidelity up‐down link use

Chaiwong

Bahadoran

Amiri

et al. 2018

Micro & Optical Tech Letters

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We propose the use of the electro‐optic circuit to integrate with the short distance fiber optic loop network, which can be used to form the light fidelity (LiFi) network platform within the building. The electro‐optic conversion circuit is formed by a plasmonic circuit. It is a passive device consisting of an embedded plasmonic island within the microring conjugate mirror (MCM), in which the applied input‐output information can be connected to the fiber optic loop. In this design, all sources of the information can be coupled to the plasmonic transducer and connected to the network. The transducer mobility and two different wavelengths of the input sources are demonstrated and all port signals plotted. The free spectral range and the full‐width at half maximum at the center wavelength of 1.30 μm are ~370.0 and ~0.62 nm, respectively. This is equal to the bandwidth of ~77 PetaHz, from which the output intensity power is ranged between 5 and 15 mW (μm)−2. The linear relationship between the driven mobility and the input power of ~0.75 (cm)2. (Vs)−1 W−1 is achieved at the drop port, with the gold layer length is 600 nm.

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“…Remarkably, the architecture itself enables mechanisms of abstraction and generalization, two features which are often considered key ingredients for artificial intelligence. The proposed architecture, based on single-photon evolution on a mesh of tunable beamsplitters, is simple, scalable, and a first integration in quantum optical experiments appears to be within the reach of near-term technology.OPEN ACCESS RECEIVED promising features as compared to electronic processors [27][28][29][30]. For instance, nanosecond-scale routing and reconfigurability have already been demonstrated [31][32][33], while encoding information in photons enables decision-making at the speed of light, only limited by the generation and detection rates.…”

mentioning

confidence: 99%

Photonic architecture for reinforcement learning

et al. 2020

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The last decade has seen an unprecedented growth in artificial intelligence and photonic technologies, both of which drive the limits of modern-day computing devices. In line with these recent developments, this work brings together the state of the art of both fields within the framework of reinforcement learning. We present the blueprint for a photonic implementation of an active learning machine incorporating contemporary algorithms such as SARSA, Q-learning, and projective simulation. We numerically investigate its performance within typical reinforcement learning environments, showing that realistic levels of experimental noise can be tolerated or even be beneficial for the learning process. Remarkably, the architecture itself enables mechanisms of abstraction and generalization, two features which are often considered key ingredients for artificial intelligence. The proposed architecture, based on single-photon evolution on a mesh of tunable beamsplitters, is simple, scalable, and a first integration in quantum optical experiments appears to be within the reach of near-term technology.OPEN ACCESS RECEIVED promising features as compared to electronic processors [27][28][29][30]. For instance, nanosecond-scale routing and reconfigurability have already been demonstrated [31][32][33], while encoding information in photons enables decision-making at the speed of light, only limited by the generation and detection rates. Moreover, the use of phase-change materials for in-memory information processing [34] promises to enhance the energy efficiency, since their properties can be modified without continuous external intervention [35,36]. Importantly, since the architecture uses single photons, decision-making is fueled by genuine quantum randomness. This feature marks a fundamental departure from pseudorandom number generation in conventional devices. (ii) The second contribution is the development of a specific variant of PS based on binary decision trees (tree-PS, or t-PS for short), which is closely connected to the standard PS and suitable for the implementation on a photonic circuit. Furthermore, we discuss how this variant enables key features of artificial intelligence, namely abstraction and generalization [37,38].The Article is structured as follows. In section 2 we summarize the theoretical framework of RL, exemplified by three common approaches: SARSA, Q-learning, and PS. In section 3 we describe the blueprint for a fully integrated, photonic RL agent. We then numerically investigate its performance within two standard RL tasks and under realistic experimental imperfections in section 4. Finally, in section 5 we discuss promising features of this architecture within the context of t-PS.

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Integrating photonics with silicon nanoelectronics for the next generation of systems on a chip

Abstract: and data-center networks 3, 6, 7

Cited by 644 publications

References 35 publications

Silicon-based multimode waveguide crossings

Silicon-based multimode waveguide crossings

Electro‐optic conversion circuit incorporating a fiber optic loop for light fidelity up‐down link use

Photonic architecture for reinforcement learning

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