Simulative analysis of scalable and low-latent photonic switch for data center applications
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Harpreet Kaur
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Rajinder Singh Kaler
Abstract
Data center requirements for entertaining many users in a minimum amount of time with maximum speed are expensive and power-hungry. To overcome this problem, we present a fast, scalable, all-photonic 3-stage interferometric-loop-based interconnecting switch for data centers. A short response time of 1.33 ns is achieved by utilizing semiconductor optical amplifier (SOA)-based non-linearities, which is superlative to previously reported techniques. The high values of (Optical signal-to-noise ratio) OSNR of 20.93 dB, extinction ratio (ER) of 19.9 dB, and Q-factor of 24.5 show excellent working of implemented configuration. The structure is also evaluated for an optimized bias current of 0.15 A to achieve improved error-rate of 10−17. To check the stage-wise power consumption, continuous monitoring and evaluation of power per stage is done. The measurement shows performance enhancement when the high input power imposes its effect on it. The parametric comparison is performed using received power, Q-factor, and ER for 2 × 2, 4 × 4, and 8 × 8 switching interconnects. The consistency in extinction ratio shows that configuration can handle high powers and also maintain good quality of service simultaneously. The design ensures high switching accuracy and signal quality through SOA-based amplification, non-blocking architecture, and integrated 3R-regeneration for longer, high-speed transmissions. The parameter reliability with progressing stage proves that proposed structure is a good solution to scalability and latency challenges for data centers.
Acknowledgments
The authors are greatly thankful to the optical research lab provided by the Thapar Institute of Engineering and Technology for carrying out our research.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors have equal contributions to this manuscript.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors declare that they have no known conflicts of interest associated with this publication.
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Research funding: The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.
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Data availability: Data sharing does not apply to this article as no datasets were generated or analyzed during the current study.
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