Deep Learning Approaches for Nanoplasmonics: Expanding Horizons

Abstract

Deep learning, a subfield of machine learning, has revolutionized various domains by providing powerful tools to process and analyze complex data. In the realm of nanoplasmonics, deep learning techniques have emerged as promising approaches to tackle the challenges associated with large-scale
data analysis, modeling, and optimization of plasmonic systems. This article presents an in-depth exploration of deep learning approaches applied to nanoplasmonics research, focusing on the advances, opportunities, and future directions in this rapidly evolving field. It discusses the utilization
of deep neural networks, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), for tasks such as nanoparticle synthesis, plasmonic resonance prediction, and optical property characterization. It showcases the potential of deep learning in nanoplasmonics by highlighting various applications, including enhanced sensing and biosensing, nanophotonic device design, and optimization of plasmonic structures for specific functionalities. It delves into the integration of deep learning with experimental techniques, such as
hyperspectral imaging and near-field scanning optical microscopy (NSOM), to extract valuable insights from high-dimensional data and enable real-time analysis. It discusses the challenges and limitations associated with deep learning in nanoplasmonics, such as the need for large annotated
datasets, interpretability of deep learning models, and computational complexity. It also sheds light on ongoing research efforts to address these challenges and presents potential future directions for leveraging deep learning in nanoplasmonics.