Multimode waveguide crossing with ultralow loss and low imbalance

Wu, Beibei; Yu, Yu; Zhang, Chi

doi:10.1364/oe.392445

“…The single-mode waveguide crossings are indispensable building and connecting blocks for complex photonic circuits in the system-on-chip. By utilizing fully etched and shallowly etched waveguides and linear tapered waveguides in the MMI coupler region, the waveguide crossing attains ultra-low loss and imbalance [54,55]. The transfer characteristic of the waveguide crossing designed for the proposed multi-degree switch, illustrated in Figure 2, shows that the transmission loss of this structure is low, only fluctuating from −0.1dB to −0.2dB in 20nm wavelength bandwidth.…”

Section: Components Of a Photonic Chipmentioning

confidence: 99%

Self-Controlling Photonic-on-Chip Networks With Deep Reinforcement Learning

Do

¹

,

Truong

²

,

Nguyễn

³

et al. 2021

Preprint

0

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We present a novel photonic chip design for high bandwidth four-degree optical switches that support high-dimensional switching mechanisms with low insertion loss and low crosstalk in a low power consumption level and a short switching time. Such four-degree photonic chips can be used to build an integrated full-grid Photonic-on-Chip Network (PCN). With four distinct input/output directions, the proposed photonic chips are superior compared to the current bidirectional photonic switches, where a conventionally sizable PCN can only be constructed as a linear chain of bidirectional chips. Our four-directional photonic chips are more flexible and scalable for the design of modern optical switches, enabling the construction of multi-dimensional photonic chip networks that are widely applied for intra-chip communication networks and photonic data centers. More noticeably, our photonic networks can be self-controlling with our proposed Multi-Sample Discovery model, a deep reinforcement learning model based on Proximal Policy Optimization. On a PCN, we can optimize many criteria such as transmission loss, power consumption, and routing time, while preserving performance and scaling up the network with dynamic changes. Experiments on simulated data demonstrate the effectiveness and scalability of the proposed architectural design and optimization algorithm. Perceivable insights make the constructed architecture become the self-controlling photonic-on-chip networks.

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“…The single-mode waveguide crossings are indispensable building and connecting blocks for complex photonic circuits in the system-on-chip. By utilizing fully etched and shallowly etched waveguides and linear tapered waveguides in the MMI coupler region, the waveguide crossing attains ultra-low loss and imbalance 73 , 74 . The transfer characteristic of the waveguide crossing designed for the proposed multi-degree switch, illustrated in Fig.…”

Section: Multi-degree Optical Switchesmentioning

confidence: 99%

Self-controlling photonic-on-chip networks with deep reinforcement learning

Do

¹

,

Truong

²

,

Nguyen

³

et al. 2021

Sci Rep

3

0

View full text Add to dashboard Cite

We present a novel photonic chip design for high bandwidth four-degree optical switches that support high-dimensional switching mechanisms with low insertion loss and low crosstalk in a low power consumption level and a short switching time. Such four-degree photonic chips can be used to build an integrated full-grid Photonic-on-Chip Network (PCN). With four distinct input/output directions, the proposed photonic chips are superior compared to the current bidirectional photonic switches, where a conventionally sizable PCN can only be constructed as a linear chain of bidirectional chips. Our four-directional photonic chips are more flexible and scalable for the design of modern optical switches, enabling the construction of multi-dimensional photonic chip networks that are widely applied for intra-chip communication networks and photonic data centers. More noticeably, our photonic networks can be self-controlling with our proposed Multi-Sample Discovery model, a deep reinforcement learning model based on Proximal Policy Optimization. On a PCN, we can optimize many criteria such as transmission loss, power consumption, and routing time, while preserving performance and scaling up the network with dynamic changes. Experiments on simulated data demonstrate the effectiveness and scalability of the proposed architectural design and optimization algorithm. Perceivable insights make the constructed architecture become the self-controlling photonic-on-chip networks.

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“…The waveguide crossing is inevitable in optical networks to obtain a complex and advanced architecture. , Many efforts have been involved into the multimode waveguide crossings in the past years, mainly based on Y-junctions, MMI couplers, , inverse-designed nanostructures, , and Maxwell’s fisheye lens ,, (MFE). For the crossings based on Y-junction and MMI coupler, the loss and supporting mode channels are the issues to be addressed.…”

Section: Realization Of Multimode Devicesmentioning

confidence: 99%

Key Multimode Silicon Photonic Devices Inspired by Geometrical Optics

Sun

¹

,

Ding

²

,

Li

³

et al. 2020

Self Cite

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On-chip optical interconnect has been widely accepted as a promising technology to realize future large-scale multiprocessors. Mode-division multiplexing (MDM) provides a new degree of freedom for optical interconnects to dramatically increase the link capacity. For present on-chip multimode devices, although large amounts of computation and optimization are adopted to support more modes, mode-independent manipulation is still hard to be achieved due to severe mode dispersion. Here, we propose a promising solution to standardize the design of fundamental multimode building blocks, inspired by the geometrical-optics concept, adopting a waveguide width larger than the working wavelength. The proposed solution can tackle a group of modes at the same time with very simple processes, avoiding a demultiplexing procedure and ensuring compact footprint. Compared to the conventional schemes, it is scalable to larger mode channels without increasing the complexity and whole footprint. As a proof of concept, we demonstrate a set of multimode building blocks, including crossing, bend, coupler, and switches, with low loss and power consumption. Our work promotes the multimode photonics research and makes the MDM technique more practical.

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Multimode waveguide crossing with ultralow loss and low imbalance

Cited by 27 publications

References 20 publications

Self-Controlling Photonic-on-Chip Networks With Deep Reinforcement Learning

Self-Controlling Photonic-on-Chip Networks With Deep Reinforcement Learning

Self-controlling photonic-on-chip networks with deep reinforcement learning

Key Multimode Silicon Photonic Devices Inspired by Geometrical Optics

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