Exploring the synergy between nanophotonics and artificial intelligence

(Nanowerk Highlights) 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 astonishing journey has led to increasingly smaller dimensions of materials, from optics to photonics, and finally to nanophotonics.

Nanophotonics investigates matter-light interactions at the nanoscale, where materials are often assembled into building blocks at wavelengths that exhibit extraordinary optical properties beyond bulk materials.

Over the last two decades, nanophotonics has garnered enormous interest, becoming a dynamic research field with both fundamental and application-based studies. The subfield of nanophotonics includes plasmonics, metamaterials and metasurfaces, photonic crystals, photonic integrated circuits, and other resonant nanostructures that perform photonic functions.

These devices operate on multiple mechanisms, offering unprecedented opportunities to control light at the nanoscale, uncovering new physics and achieving extraordinary applications not possible with conventional techniques.

Meanwhile, artificial intelligence (AI), a subject seemingly unrelated to nanophotonics, is currently one of the most promising technologies with the potential to revolutionize many aspects of our world.

The history of AI spans nearly 80 years, with early research on neural networks in the 1940s marking its beginnings. The popularity of AI has now infiltrated various research fields, including physics, chemistry, materials science and biomedicine.

With the success of computer programs such as AlphaGo, which beat top professional Go players, and AlphaFold’s astounding achievement at predicting protein structures with incredible accuracy, there is widespread belief that we are entering a new era in which AI, particularly via neural networks, can compete with human intelligence in certain tasks, known as weak AI. One prominent example of a neural network is large language modeling (LLM) such as ChatGPT, which has recently captured the imagination of a wider audience, providing human-like interactions in conversational AI and enhancing applications across a wide range of 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, demonstrating its far-reaching impact across multiple domains.

The intersection of AI and Nanophotonics

AI integration with nanophotonics is an exciting prospect. While not a panacea for all challenges, AI can potentially assist in designing nanophotonic devices. Conventional inverse design relies on a trial and error process, which is labor intensive and time consuming. In contrast, machine learning, a subset of AI, provides data-driven methods that leverage large training sets to improve design optimization for specific tasks. The question of whether and how AI might benefit reverse 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 shown that specially designed nanophotonic circuits can perform machine learning tasks such as inference, making the relationship between AI and nanophotonics reciprocal and interactive.

Bridging the Knowledge Gap

Because AI and nanophotonics have different backgrounds, there are often knowledge gaps for those interested in this interdisciplinary field. A new book (“Nanophotonics and Machine Learning – Concepts, Fundamentals, and Applications“), the first of its kind, aims to introduce the fundamentals of nanophotonics and machine learning, particularly deep learning, and to help readers understand how these fields can complement each other.


The first two chapters cover nanophotonics fundamentals, while the third chapter shifts focus to machine learning fundamentals. Chapters 4-6 present selected examples illustrating practical applications of this concept, including deep learning in nanophotonics for reverse design and multiple uses, and machine learning on nanophotonic platforms.

In order to maintain a compact volume and balance between fundamentals and applications, and between nanophotonics and machine learning, this book omits some of the introductory content and areas of nanophotonics, which are widely available elsewhere. Some aspects of nanophotonics, such as crystals and photonic circuits, are also not included, as they have been covered in classic textbooks and monographs and can be considered on the fringes of nanophotonics if defined by the wavelength dimensions of the building blocks and their separation.

With this in mind, the authors, Kan Yao and Yubing Zheng from the Walker Department of Mechanical Engineering, Texas Materials Institute, The University of Texas at Austin, hopes that readers of all levels – graduate students (under), professionals, and researchers new to or working in either field – will find this book accessible. and valuable.