Bridging the gap: Exploring the synergy between nanophotonics and artificial intelligence

(Nanowerk Spotlight) The study of light-matter interactions has a rich history that can be traced back to the early days of science, even before the nature of light was fully understood. This fascinating journey has led to increasingly smaller dimensions of materials, from optics to photonics, and finally to nanophotonics.
Nanophotonics investigates light-matter interactions at the nanoscale, where materials are often structured into subwavelength building blocks that exhibit extraordinary optical properties beyond those of bulk materials.
Over the past two decades, nanophotonics has garnered immense interest, becoming a vibrant research field with both fundamental and application-driven studies. Subfields of nanophotonics include plasmonics, metamaterials and metasurfaces, photonic crystals, photonic integrated circuits, and other resonant nanostructures that perform photonic functions.
These devices operate on various mechanisms, offering unprecedented opportunities to control light at the nanoscale, unveiling new physics, and achieving remarkable applications not possible with conventional techniques.
Meanwhile, artificial intelligence (AI), a seemingly unrelated subject to nanophotonics, is currently among the most promising technologies with the potential to revolutionize various aspects of our world.
The history of AI spans almost 80 years, with early research on neural networks in the 1940s marking its inception. The popularity of AI has now infiltrated numerous research fields, including physics, chemistry, materials science, and biomedicine.
With the success of computer programs like AlphaGo, which defeated top professional Go players, and the remarkable achievements of AlphaFold in predicting protein structures with groundbreaking accuracy, there is widespread belief that we are entering a new era where AI, particularly through neural networks, can compete with human intelligence in specific tasks, known as weak AI. One prominent example of neural networks is large language models (LLMs) such as ChatGPT, which have recently captured the imagination of broader audiences, providing human-like interactions in conversational AI and enhancing applications in various industries.
In science and engineering, especially fields related to Big Data, AI is expected to play a significant role in materials discovery, drug development, and more, showcasing its far-reaching impact across diverse domains.

The Intersection of AI and Nanophotonics

The integration of AI with nanophotonics is an intriguing prospect. While not a panacea for all challenges, AI can potentially assist in designing nanophotonic devices. Conventional inverse design relies on trial-and-error processes, which are labor-intensive and time-consuming. In contrast, machine learning, a subset of AI, provides data-driven methods that leverage large training sets to improve the optimization of designs for specific tasks. The question of whether and how AI can benefit inverse design remains open, but the potential benefits warrant further research efforts.
The synergy between AI and nanophotonics goes beyond passive assistance. The rapid growth of machine learning has revealed the inefficiency of general-purpose processors for implementing neural networks, leading to the development of application-specific hardware. Nanophotonic circuits, which can process coherent light signals, offer advantages in speed and power efficiency over electronic architectures.
Recent advances have demonstrated that specially designed nanophotonic circuits can perform machine learning tasks like inference, making the relationship between AI and nanophotonics reciprocal and interactive.

Bridging the Knowledge Gap

As AI and nanophotonics have distinct backgrounds, there is often a knowledge gap for those interested in this interdisciplinary area. A new book ("Nanophotonics and Machine Learning – Concepts, Fundamentals, and Applications"), the first of its kind, aims to introduce the fundamentals of both nanophotonics and machine learning, specifically deep learning, and to help readers understand how these fields can enhance each other.

The first two chapters cover the essentials of nanophotonics, while the third chapter shifts focus to machine learning fundamentals. Chapters 4-6 present selected examples illustrating the practical applications of these concepts, including deep learning in nanophotonics for inverse design and diverse uses, as well as machine learning on nanophotonic platforms.

To maintain a compact volume and balance between fundamentals and applications, as well as between nanophotonics and machine learning, this book omits some introductory content and areas of nanophotonics, which are widely available elsewhere. Some aspects of nanophotonics, such as photonic crystals and circuits, are not included either, as they have been covered in classic textbooks and monographs and may be considered on the periphery of nanophotonics if defined by the subwavelength dimensions of the building blocks and their separations.

With these considerations in mind, the authors, Kan Yao and Yuebing Zheng from the Walker Department of Mechanical Engineering, Texas Materials Institute, The University of Texas at Austin, hope that readers of all levels –(under)graduate students, professionals, and researchers new to or working in either field – will find this book accessible and valuable.
Michael Berger By – Michael is author of three books by the Royal Society of Chemistry:
Nano-Society: Pushing the Boundaries of Technology,
Nanotechnology: The Future is Tiny, and
Nanoengineering: The Skills and Tools Making Technology Invisible
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