Editorial Feature

Ultrafast Photonics for Next-Gen High-Speed Optical Networks

Communication technologies have grown rapidly in the recent past with innovations that are imagined one day the next day they come to life. Ultrafast photonics is one such field that is growing very quickly, with each advancement improving the speed and efficiency of optical communication networks. This article provides an overview of ultrafast photonics for next-gen high-speed optical networks and recent developments in this field.

Ultrafast Photonics, Ultrafast Photonics for Optical Networks

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What is Ultrafast Photonics?

Ultrafast photonics is a field of science that explores the behavior of light pulses on extremely short timescales, typically in less than a picosecond 10-12 range, allowing information manipulation and transmission at very high speeds, making them ideal for the future of high-speed optical networks.

Ultrafast photonics relies heavily on advanced fiber optic technologies that can transmit ultrafast pulses without significant distortion or dispersion of the high-speed optical signal. Usually, these ultrafast signal pulses are generated by Mode-locked lasers, which produce a stream of extremely short pulses by synchronizing the phases of different longitudinal modes of the laser cavity, enabling the transmission of vast amounts of data in a short time span.

Applications in High-Speed Optical Networks

High-speed optical networks have a wide range of applications in data centers, 5G capabilities, and the Internet. Data centers, driven by cloud computing and big data applications, demand faster interconnectivity. Ultrafast photonics enhance the communication infrastructure within these data centers, enabling rapid data transfer between servers and storage units and ensuring defectless operation of data-intensive applications.

Similarly, ultrafast photonics can further amplify 5G capabilities by providing high-speed, reliable optical links for backhaul and fronthaul connections. Researchers aim to increase the capacity and reduce the latency of long-haul optical communication links by using the speed and efficiency of ultrafast pulses, contributing to a more responsive internet infrastructure.

Recent Developments

Chromoproteins for Optical Networks

In a 2023 study, researchers explored the potential of chromoproteins for ultrafast all-optical switching in high-speed optical networks by focusing on photoactive yellow protein (PYP) and comparing its performance with bacteriorhodopsin (BR). The researchers demonstrated the feasibility of < 200-fs all-optical switching by conducting femtosecond transient grating experiments using hydrated thin films of PYP. Unlike BR, PYP is a water-soluble protein with a simpler incorporation into passive structures, offering advantages for practical applications.

The study revealed that PYP films, when doped with glycerol or polyacrylamide, showed sub-picosecond switching times, opening up possibilities for ultrafast optical modulation and routing in high-speed optical networks. The findings suggest that chromoproteins like PYP could be promising candidates for developing ultrafast, cost-effective, and efficient components in optical communication systems.

Ultrafast Photonics with VCSEL-Neurons

Researchers, in a 2019 study, focused on artificial laser neurons, particularly Vertical-Cavity Surface Emitting Lasers (VCSEL-Neurons), demonstrating controllable excitation of spiking signals at sub-nanosecond speeds, surpassing biological neuron speeds by over seven orders of magnitude. The study explores various techniques, including optical and electronic excitation, to activate and inhibit sub-nanosecond spiking signals in VCSEL-Neurons.

The researchers demonstrated the communication of spiking patterns between interconnected VCSEL-Neurons, paving the way for future high-speed optical neuromorphic networks. The findings of the research reveal the capability of VCSEL-Neurons to perform diverse neuro-inspired spike processing tasks, with experimental demonstrations of photonic spiking memory modules and the ultrafast emulation of neuronal circuits in the retina, all accomplished with off-the-shelf VCSELs compatible with current optical network technologies.

This breakthrough holds promise for advancing brain-inspired computing and artificial intelligence.

Ultrafast Optical Processing with ROSS-NNs

In a 2022 study, researchers introduced a novel approach, Recurrent Optical Spectrum Slicing Neural Networks (ROSS-NNs), for neuromorphic computing in high-speed optical networks. This hardware concept utilizes optical filters in a loop, where each filter processes a specific spectral slice of the incoming optical signal.

The method extends the optical signal transmission reach to over 60 km at baud rates exceeding 100 Gbaud, surpassing state-of-the-art digital equalizers fourfold. ROSS-NN significantly reduces complexity, requiring less than 100 multiplications/bit in the digital domain, resulting in a tenfold reduction in power consumption. This approach is useful for efficient photonic hardware accelerators in optical communication and high-speed imaging applications, providing coherent processing at ultrahigh speeds and minimizing power consumption.

Challenges

As technology evolves at a rapid pace, the compatibility of new technology with old infrastructure is becoming a significant challenge. For instance, integrating ultrafast photonics into existing optical networks poses a considerable challenge since many of these networks are not designed for such signal transmission.

Therefore, the gradual adoption of ultrafast photonics technologies without disrupting existing networks is essential, but it hinders the fast adoption of such technologies. Moreover, these next-gen high-speed optical networks also require new materials that can withstand such extremely paced signal transmission with minimal distortion.

Conclusion

Ultrafast photonics is the base for the next generation of high-speed optical networks, providing rapid data transfer rates and faster communication. Although challenges lie in integrating these technologies with existing infrastructure and developing materials that handle ultrafast signals without distortion, with ongoing research and advancements in materials science, laser technology, and optical communication protocols, the potential of ultrafast photonics in communication technology is very bright.

More from AZoOptics: What Are Biophotonic Sensors for Medical Diagnostics?

References and Further Reading

Andriolli, N., Giorgetti, A., Castoldi, P., Cecchetti, G., Cerutti, I., Sambo, N., ... & Paolucci, F. (2022). Optical networks management and control: A review and recent challenges. Optical Switching and Networking. https://doi.org/10.1016/j.osn.2021.100652

Kamiya, T., & Tsuchiya, M. (2005). Progress in ultrafast photonics. Japanese journal of applied physics. https://doi.org/10.1143/JJAP.44.5875

Khan, Taha. (2024, February 05). What to Know About Integrated Photonics in Data Centers. AZoOptics. Retrieved on February 19, 2024 from https://www.azooptics.com/Article.aspx?ArticleID=2540

Kim, J., & Kaertner, F. X. (2010). Attosecond‐precision ultrafast photonics. Laser & Photonics Reviews. https://doi.org/10.1002/lpor.200910003

Krekic, S., Mero, M., Dér, A., & Heiner, Z. (2023). Ultrafast all-optical switching using doped chromoprotein films. The Journal of Physical Chemistry. https://doi.org/10.1021/acs.jpcc.2c06232

Robertson, J., Wade, E., Kopp, Y., Bueno, J., & Hurtado, A. (2019). Toward neuromorphic photonic networks of ultrafast spiking laser neurons. IEEE Journal of Selected Topics in Quantum Electronics. https://doi.org/10.1109/JSTQE.2019.2931215

Saleh, A. A., & Simmons, J. M. (2012). All-optical networking—evolution, benefits, challenges, and future vision. Proceedings of the IEEE. https://doi.org/10.1109/JPROC.2011.2182589

Serafino, G., Porzi, C., Falconi, F., Pinna, S., Puleri, M., D’Errico, A., ... & Ghelfi, P. (2018). Photonics-assisted beamforming for 5G communications. IEEE Photonics Technology Letters. https://doi.org/10.1109/LPT.2018.2874468

Sozos, K., Bogris, A., Bienstman, P., Sarantoglou, G., Deligiannidis, S., & Mesaritakis, C. (2022). High-speed photonic neuromorphic computing using recurrent optical spectrum slicing neural networks. Communications Engineering. https://doi.org/10.1038/s44172-022-00024-5

Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.

Taha Khan

Written by

Taha Khan

Taha graduated from HITEC University Taxila with a Bachelors in Mechanical Engineering. During his studies, he worked on several research projects related to Mechanics of Materials, Machine Design, Heat and Mass Transfer, and Robotics. After graduating, Taha worked as a Research Executive for 2 years at an IT company (Immentia). He has also worked as a freelance content creator at Lancerhop. In the meantime, Taha did his NEBOSH IGC certification and expanded his career opportunities.  

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