Deep learning in the context of nano-photonics is mostly discussed in terms of its potential
for inverse design of photonic devices or nano-structures. Many of the recent works on …

Optical superoscillations are rapid, subwavelength spatial variations of the intensity and
phase of light, occurring in complex electromagnetic fields formed by the interference of …

Deep neural networks (DNNs) are empirically derived systems that have transformed
traditional research methods, and are driving scientific discovery. Artificial electromagnetic …

With the growing interest in the field of artificial materials, more advanced and sophisticated
functionalities are required from phononic crystals and acoustic metamaterials. This implies …

This tutorial–review on applications of artificial neural networks in photonics targets a broad
audience, ranging from optical research and engineering communities to computer science …

Photonic brain-inspired platforms are emerging as novel analog computing devices,
enabling fast and energy-efficient operations for machine learning. These artificial neural …

The overall goal of photonics research is to understand and control light in new and richer
ways to facilitate new and richer applications. Many major developments to this end have …

Surface-enhanced Raman spectroscopy (SERS) has gained recognition as a potential
vibrational spectroscopy technique due to its distinctive fingerprint characteristics, non …

Deep learning is having a tremendous impact in many areas of computer science and
engineering. Motivated by this success, deep neural networks are attracting increasing …

Over the past decades, photonics has transformed many areas in both fundamental
research and practical applications. In particular, we can manipulate light in a desired and …