Open Access Open Access  Restricted Access Subscription or Fee Access

Deep Learning Approaches for Nanoplasmonics: Expanding Horizons

Vansh Gupta


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.

Full Text:



Zhao Y, Dong B, Benkstein KD, Chen L, Steffens KL, Semancik S. Deep Learning Image Analysis of Nanoplasmonic Sensors: Toward Medical Breath Monitoring. ACS Applied Materials & Interfaces. 2022 Nov 23;14(49):54411-22.

Cheng N, Fu J, Chen D, Chen S, Wang H. An antibody-free liver cancer screening approach based on nanoplasmonics biosensing chips via spectrum-based deep learning. NanoImpact. 2021 Jan 1;21:100296.

Masson JF, Biggins JS, Ringe E. Machine learning for nanoplasmonics. Nature Nanotechnology. 2023 Feb;18(2):111-23.

John‐Herpin A, Kavungal D, von Mücke L, Altug H. Infrared metasurface augmented by deep learning for monitoring dynamics between all major classes of biomolecules. Advanced Materials. 2021 Apr;33(14):2006054.

Kazemzadeh M, Martinez-Calderon M, Paek SY, Lowe M, Aguergaray C, Xu W, Chamley LW, Broderick NG, Hisey CL. Classification of Preeclamptic Placental Extracellular Vesicles Using Femtosecond Laser Fabricated Nanoplasmonic Sensors. ACS sensors. 2022 Jun 6;7(6):1698-711.

Pan Z, Yu W, Yi X, Khan A, Yuan F, Zheng Y. Recent progress on generative adversarial networks (GANs): A survey. IEEE access. 2019 Mar 14;7:36322-33.

Saxena D, Cao J. Generative adversarial networks (GANs) challenges, solutions, and future directions. ACM Computing Surveys (CSUR). 2021 May 8;54(3):1-42.

Alqahtani H, Kavakli-Thorne M, Kumar G. Applications of generative adversarial networks (gans): An updated review. Archives of Computational Methods in Engineering. 2021 Mar;28:525-52.

Kattenborn T, Leitloff J, Schiefer F, Hinz S. Review on Convolutional Neural Networks (CNN) in vegetation remote sensing. ISPRS journal of photogrammetry and remote sensing. 2021 Mar 1;173:24-49.

Lu J, Tan L, Jiang H. Review on convolutional neural network (CNN) applied to plant leaf disease classification. Agriculture. 2021 Jul 27;11(8):707.

Montavon G, Samek W, Müller KR. Methods for interpreting and understanding deep neural networks. Digital signal processing. 2018 Feb 1;73:1-5.

Samek W, Binder A, Montavon G, Lapuschkin S, Müller KR. Evaluating the visualization of what a deep neural network has learned. IEEE transactions on neural networks and learning systems. 2016 Aug 25;28(11):2660-73.

Miikkulainen R, Liang J, Meyerson E, Rawal A, Fink D, Francon O, Raju B, Shahrzad H, Navruzyan A, Duffy N, Hodjat B. Evolving deep neural networks. InArtificial intelligence in the age of neural networks and brain computing 2019 Jan 1 (pp. 293-312). Academic Press.

Samek W, Montavon G, Lapuschkin S, Anders CJ, Müller KR. Explaining deep neural networks and beyond: A review of methods and applications. Proceedings of the IEEE. 2021 Mar 4;109(3):247-78.



  • There are currently no refbacks.