The Role of Nanophotonics in Computing

By Owais AliReviewed by Lexie CornerUpdated on Feb 11 2025

As computational demands surpass the capabilities of traditional electronic systems, nanophotonics has emerged as a promising alternative for advancing computing architecture. By utilizing photons instead of electrons, nanophotonic systems offer higher processing speeds, lower power consumption, and improved scalability for next-generation applications.

Image Credit: Andrey Suslov/Shutterstock.com

Nanophotonics is an interdisciplinary field that explores light-matter interactions at the nanoscale, involving structures smaller than 100 nanometers. At this scale, light interacts with matter in unique ways, producing optical phenomena such as plasmons and photonic crystals, which are governed by quantum mechanics. These interactions facilitate precise light manipulation, driving innovations in energy-efficient computing, sensing, and optical switching technologies.¹

The integration of nanophotonic technologies into computing systems represents a significant advancement in computational capabilities. Nanophotonic systems utilize photons rather than electrons for data transmission and processing, achieving superior performance metrics compared to conventional electronic architectures.

Various studies have demonstrated that nanophotonic interconnects can achieve data transmission rates surpassing 100 gigabits per second while maintaining energy consumption below 50 femtojoules per bit, marking substantial improvements over traditional electronic systems.

Its applications extend across multiple domains, particularly in areas demanding intensive processing capabilities such as artificial intelligence and machine learning. The technology's capacity to manipulate light at the nanoscale allows computational systems with enhanced speed, reduced latency, and optimized energy efficiency.

These characteristics position nanophotonics as a key driver of future computational advancements, addressing power consumption challenges while meeting the increasing demands of modern computing applications.²

Cross-sectional view of a hybrid electronic-photonic processor chip. Image Credit: IBM Newsroom.

Early Applications in High-Performance Computing

Nanophotonics gained prominence in high-performance computing, particularly in data centers and supercomputing facilities. Initial deployments focused on replacing traditional copper interconnects with optical communication channels, resulting in reduced latency and improved bandwidth between computational units.

The transition from electronic to optical interconnects has progressed through several phases. Early systems utilized hybrid approaches, incorporating both electronic and optical components to maintain compatibility with existing infrastructure while introducing the benefits of optical communication.

Supercomputing facilities emerged as primary beneficiaries of nanophotonic technologies. The integration of optical interconnects in supercomputer architectures has supported the development of more compact and efficient systems.

Recent research indicates significant improvements in system performance, with some installations achieving inter-node communication speeds approaching 400 gigabits per second.^3,4

A multicore chip with silicon photonic components directing data traffic between the cores. Image Credits: IBM Newsroom.

Pioneering Technological Developments

IBM's Silicon Nanophotonics Breakthrough

IBM emerged as a pioneer in nanophotonic computing with its integrated photonic circuits, which control light signals via electric current. This was followed by key advancements, including a compact optical buffer (2006), a low-power silicon optical modulator (2007), a broadband optical switch (2008), and, in 2010, the successful integration of optical and electrical components on a silicon chip, allowing light-based communication.

In December 2012, the company achieved a significant milestone by integrating optical and electrical components on a single silicon chip using standard 90nm semiconductor techniques.

This innovation enabled high-speed light-based data transmission between supercomputers, data centers, and computer chips, using advanced components such as photodetectors, ultra-compact wavelength-division multiplexers, CMOS circuitry, and modulators.

This technology eliminates the need to assemble multiple components or retool factories by utilizing standard CMOS foundries. Additionally, on-chip wavelength-division multiplexing (WDM) devices support parallel optical data transmission within a single fiber, significantly reducing interconnect costs.⁵

Novel Nanophotonic Analog Processors

Traditional computing architectures struggle with energy efficiency and speed in solving complex tasks. However, a recent study published in Nature Communications Physics developed a nanophotonic analog processor that solves partial differential equations (PDEs) with over 90 % accuracy using epsilon-near-zero (ENZ) materials and wavelength stretching.

The processor is programmable through carrier injection, operates within the optical telecommunication band, and processes inputs at the speed of light. This metatronic circuit approach enables high-speed, low-energy, chip-scale analog computing, overcoming the limitations of traditional electronic architectures and providing a reconfigurable platform for ultrafast computation.⁶

MIT's Programmable Nanophotonic Processor

MIT's programmable nanophotonic processor represents a significant advancement in optical computing. The system utilizes arrays of Mach-Zehnder interferometers to perform matrix operations optically, allowing rapid processing of neural network computations.

The researchers demonstrated that these processors could perform matrix multiplications at speeds exceeding 100 trillion operations per second, making them particularly suitable for deep learning applications.⁷

Material Innovations Driving Nanophotonic Computing

Advanced materials such as cadmium sulfide nanowires and metamaterials have improved efficiency, processing speeds, and energy dissipation, permitting the integration of photonic components with semiconductor technologies for scalable, chip-scale processors.

These advancements have supported the development of efficient and compact nanophotonic devices, capable of performing complex computational tasks at high speeds while maintaining low energy consumption.

Functional Nanowires

In a study published in Science Advances, researchers developed a photonic computing processor using hybridized-active-dielectric nanowires made of phase-change material Ge₂Sb₂Te₅ (GST) and silicon.

When illuminated by light pulses, these nanowires exhibit a reversible phase change from a resistive to a conductive state, with the polarization of the light tuning the material's absorption.

This approach facilitates parallel computing across multiple nanowires, increasing information storage and processing density by leveraging light’s speed and large bandwidth, surpassing the limitations of traditional electronic systems.⁸

Lithium Niobate

Lithium niobate has become a key material in nanophotonic waveguide technologies, enabling advanced signal processing due to its unique optical and electrical properties.

Recently, Caltech researchers utilized this material to create ultrafast systems, achieving 16 femtojoules per activation and activation times of 75 femtoseconds, marking a substantial improvement in computational efficiency over current electronic systems.

These capabilities open up opportunities for integrated photonic circuits that operate at unprecedented speeds and energy efficiency, with potential applications in quantum information processing, precision sensing, and advanced computing platforms.⁹

Consumer Electronics and AI Integration

Smartphone Technology Advancements

The impact of nanophotonics extends into consumer electronics, driving advancements in compact, high-performance imaging systems and energy-efficient display technologies.

Researchers at the University of Washington developed nanophotonic cameras using a hybrid optical system that combines traditional lenses with nanoscale structures called "nanopillars," facilitating sub-wavelength light manipulation for professional-quality imaging. These systems eliminate bulky camera protrusions using artificial neural networks while enhancing depth sensing and low-light performance.¹⁰

In displays, companies like Nanosys and Anders Electronics have introduced advanced nanophotonic materials for OLED screens, delivering richer colors, deeper blacks, and increased brightness while reducing battery consumption, offering significant improvements for smartphones and wearable devices.¹¹

AI-Driven Applications

The current era of artificial intelligence demands unprecedented computational speed and energy efficiency to handle complex algorithms and vast data processing needs. Nanophotonic technologies offer transformative solutions through ultrafast data processing and energy-efficient operations.

Oregon State University and Lawrence Berkeley National Laboratory researchers have developed luminescent nanocrystals with intrinsic optical bistability, allowing rapid transitions between light and dark states. These nanocrystals provide low-power switching mechanisms, addressing critical energy challenges in AI hardware by reducing energy consumption while increasing processing speeds.

This breakthrough has the potential to enhance machine learning processors, optoelectronic systems, and advanced computational platforms, enabling more efficient and powerful AI technologies.¹²

Energy Efficient Computing

Nanophotonic computing technologies align with the global push for sustainable and energy-efficient solutions. These systems use light for data transmission and processing, minimizing resistive losses inherent in traditional electronic circuits, thus presenting a more sustainable computing paradigm.

The University of California, Davis researchers demonstrated that an all-optical nanophotonic computing platform reduces power consumption by approximately 1,000 times while enhancing computational speed. This approach resolves impedance issues in electronic circuits, allowing the development of all-optical input and output systems that reshape computational energy efficiency strategies.^13,14

Quantum Computing Prospects

The integration of nanophotonics with quantum computing represents perhaps the most exciting frontier in computational technology.

Companies like Quandela and QuiX are at the forefront of this innovation. Quandela is developing plug-and-play quantum dot-based single-photon emitters, and QuiX is creating reconfigurable photonic processors using silicon nitride (SiN) waveguide platforms. These advancements hold significant potential for applications in quantum information processing, quantum chemistry, and machine learning.

However, scaling these systems presents challenges in maintaining quantum coherence and minimizing losses in optical circuits, prompting research into more efficient single-photon sources and enhanced waveguide technologies.^3,13,14

Nanophotonics is transforming computational technologies by manipulating light at the nanoscale, overcoming traditional limitations and enabling unprecedented speeds, energy efficiency, and advanced capabilities across diverse applications.

To explore the latest advancements in nanophotonics, check out these resources:

• What to Know About Chiral Nano-Photonics
• Laser Innovations for Nanophotonics Applications
• An Introduction to Nanophotonic Sensors
• What are the Applications of Plasmonics in Photonics?

References and Further Reading

1. Aamir Iqbal, M., Ashraf, N., Shahid, W., Awais, M., Khan Durrani, A., Shahzad, K., & Ikram, M. (2022). Nanophotonics: Fundamentals, Challenges, Future Prospects and Applied Applications. Nonlinear Optics – Nonlinear Nanophotonics and Novel Materials for Nonlinear Optics. doi: 10.5772/intechopen.98601
2. Photonics. (2025). Nanophotonics. [Online] Photonics. Available at: https://www.photonics.com/EDU/nanophotonics/d5636
3. González, A., Raspa, A. (2025). Nanophotonics Are at the Heart of Advancements in Quantum Computing, 5G, and More. [Online] Photnics. Available at: https://www.photonics.com/Articles/Nanophotonics_Are_at_the_Heart_of_Advancements_in/a67180
4. Lamon, S., Zhang, Q., Gu, M. (2021). Nanophotonics-enabled optical data storage in the age of machine learning. APL Photonics. https://doi.org/10.1063/5.0065634
5. Hecht, J. (2013). Photonic Frontiers: Silicon Photonics: Silicon photonics evolve to meet real-world requirements. [Online] Laserfocusworld. Available at: https://www.laserfocusworld.com/lasers-sources/article/16556940/photonic-frontiers-silicon-photonics-silicon-photonics-evolve-to-meet-real-world-requirements
6. Miscuglio, M., et al. (2021). Approximate analog computing with metatronic circuits. Communications Physics. https://doi.org/10.1038/s42005-021-00683-4
7. Shen, Y., et al. (2017). Deep learning with coherent nanophotonic circuits. Nature Photonics. https://doi.org/10.1038/nphoton.2017.93
8. Lee, JS., Farmakidis, N., Wright, CD., Bhaskaran, H. (2022). Polarization-selective reconfigurability in hybridized-active-dielectric nanowires. Science Advances. https://doi.org/abn9459
9. Guo, Q., et al. (2023). Ultrafast mode-locked laser in nanophotonic lithium niobate. Science. https://doi.org/adj5438
10.  Gillam, W. (2023). Reimagining optics for smartphone cameras and other devices. [Online] Electrical & Computer Engineering. Available at: https://www.ece.uw.edu/spotlight/reimagining-optics/
11. Roy, G. (2024). Building a Better Smartphone With Advanced Nanophotonics. [Online] Securities.io. Available at: https://www.securities.io/building-a-better-smartphone-with-advanced-nanophotonics/
12. Skripka, A., et al. (2025). Intrinsic optical bistability of photon avalanching nanocrystals. Nature Photonics. https://doi.org/10.1038/s41566-024-01577-x
13. Thompson, B., Gibney, E. (2024). How light-based computers could cut AI's energy needs. [Online] Nature. Available at: https://doi.org/10.1038/d41586-024-02517-z
14. University of California. (2018). Energy-Efficient All-Optical Nanophotonic Computing. [Online] University of California. Available at: https://techtransfer.universityofcalifornia.edu/NCD/28912.html

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