Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks

Abstract

In this article, we propose a novel approach to achieve spectrum prediction, parameter fitting, inverse design and performance optimization for the plasmonic waveguide coupled with cavities structure (PWCCS) based on artificial neural networks (ANNs). The Fano resonance and plasmon induced transparency effect originated from the PWCCS have been selected as illustrations to verify the effectiveness of ANNs. We use the genetic algorithm to design the network architecture and select the hyper-parameters for ANNs. Once ANNs are trained by using a small sampling of the data generated by Monte Carlo method, the transmission spectrums predicted by the ANNs are quite approximate to the simulated results. The physical mechanisms behind the phenomena are discussed theoretically, and the uncertain parameters in the theoretical models are fitted by utilizing the trained ANNs. More importantly, our results demonstrate that this model-driven method not only realizes the inverse design of the PWCCS with high precision but also optimizes some critical performance metrics for transmission spectrum. Compared with previous works, we construct a novel model-driven analysis method for the PWCCS which are expected to have significant applications in the device design, performance optimization, variability analysis, defect detection, theoretical modeling, optical interconnects and so on.

Designing integrated photonic devices using artificial neural networks

“…Two recent theoretical papers by Zhang et al [7], and Peurifoy et al [8] go beyond simply optimizing over a large parameter space, and used ANNs to calculate complicated spectra using a smaller number of intuitive, smoothly varying input parameters. Even though in both cases each wavelength point in the calculated spectra required its own ANN output neuron, the usefulness of using ANNs to model systems with intuitive input parameters was demonstrated.…”

Accelerating optics design optimizations with deep learning

“…2 The current resurgence of neural computing in the form of deep learning (DL) 3 has raised the intriguing possibility of artificial intelligencebased methods that could potentially overcome the "curse of dimensionality." The application of DL has shown early promise in the design of optical thin-films, 4 nanostructures, [5][6][7][8] metasurfaces, [9][10][11] and integrated photonics. 12,13 Optics design differs from pattern recognition problems (a space where DL has achieved remarkable success) in many ways: (1) performance is often very sensitive to variations in design parameters; (2) large datasets are difficult to generate although labeling of data is automatic; (3) performance requirements are often stringent and, hence, uncertainties in the model are not acceptable; and (4) a given response can be realized through multiple designs, while a single design has a unique response (nonuniqueness).…”

Probabilistic Representation and Inverse Design of Metamaterials Based on a Deep Generative Model with Semi‐Supervised Learning Strategy

“…Neural networks have been used to solve the design, optimization, and prediction problems of electromagnetism in some early works, but the model capability and performance were limited, largely due to the simple model structure and the lack of data. More recent works sought to deal with the inverse design by DL under various scenarios, including plasmonic waveguides, optical power splitters, plasmonic metamaterials, chiral metamaterials, and nanophotonic particles . Despite different design targets and network architectures, the common idea behind these works is to model the relationship between the design parameters and optical response as a bidirectional mapping, which is only able to deal with a few design parameters in a small range of applications.…”