Mie Sensing with Neural Networks: Recognition of Nano‐Object Parameters, the Invisibility Point, and Restricted Models. Issue 2 (18th November 2021)

• Record Type: Journal Article
• Authors: Movsesyan, Artur; Besteiro, Lucas V.; Wang, Zhiming; Govorov, Alexander O.
• Is Part Of: Advanced theory and simulations. Volume 5:Issue 2(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-11-18
• Subjects: artificial neural networks -- Mie theory -- nano‐objects recognition -- plasmons -- sensing; Science -- Simulation methods -- Periodicals; Science -- Methodology -- Periodicals; Engineering -- Simulation methods -- Periodicals; Engineering -- Methodology -- Periodicals; 507.21
• DOI: 10.1002/adts.202100369
• Languages: English
• ISSNs: 2513-0390

Abstract

Abstract: In this work, artificial neural networks (ANNs) are used to recognize nano‐objects solely from the absorbance spectrum of a macroscopic sample. For this, ANNs with two recognition schemes are constructed. The first one is designed to recognize ensembles of dielectric scatterers. The second ANN model recognizes the dimensions of gold nanospheres in a mixture and the refractive index of a matrix. A challenge in the first scheme arises at and near the invisibility point, i.e., when the refractive index of nanoparticles is close to that of the medium. Of course, particle recognition in this regime faces fundamental physical limitations. However, such recognition near the invisibility point is possible, and this study reveals its unique properties. Interestingly, the recognition process for the refractive index in the vicinity of the invisibility point shows very small errors. In contrast, the errors for recognition of the radius grow strongly near this point. Another regime with limited recognition occurs when the extinction spectra are not unique and can correspond to different realizations of nanoparticle mixtures. The recognition schemes proposed and investigated herein can find their applications in sensing. Abstract : Artificial neural networks can demonstrate excellent capabilities to predict the physical parameters to design nanophotonic systems with the required properties. In this work, trained neural networks are used to accurately recognize the physical parameters of dielectric and metal nanospheres from their optical response. The fundamental challenges and limitations in machine‐learning‐based Mie sensing are revealed.