Photonics Research, Volume. 7, Issue 3, 368(2019)
Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks
Fig. 1. Schematic diagrams of the (a) THRC system, (b) FORC system, and (c) FIRC system.
Fig. 2. (a) Simulated transmission spectrum of the THRC system for Ag with loss (red solid line) and without loss (orange solid line), and theoretical transmission spectrum of the THRC system (blue dashed line); (b) group index and loss factor of the THRC system. The insets are simulated magnetic field distributions for the incident light at wavelengths of (A) 851 nm, (B) 893 nm, (C) 955 nm, (D) 1005 nm, and (E) 1048 nm.
Fig. 3. (a) Simulated transmission spectrum of the FORC system for
Fig. 4. (a) Simulated transmission spectrum of the FIRC system for
Fig. 5. (a) Diagram of the ANNs applied in the spectrum prediction; (b) fitness for different generations in the spectrum prediction; (c) training losses for different iterations in the spectrum prediction; FDTD simulated transmission spectra and ANN-predicted transmission spectra for the (d) THRC, (e) FORC, and (f) FIRC systems; (g) fitness for different generations in the parameter fitting. The inset reveals the training losses for different iterations in the parameter fitting.
Fig. 6. (a) Diagram of the ANNs applied in the inverse design and performance optimization problems; comparison results between the real structure parameters and ANN-predicted structure parameters for the (b) THRC, (c) FORC, and (d) FIRC systems. The insets in (b)–(d) are the FDTD-simulated transmission spectra corresponding to the real structures (red solid line) and ANN-predicted structure parameters (blue dashed line); (e) transmittance optimization for the THRC system; (f) bandwidth optimization for the FORC system; (g) transmittance optimization for the FIRC system.
Fig. 7. Prediction accuracies for different numbers of training instances in the (a) spectrum prediction and (b) inverse design.
Fig. 8. (a) The dispersion of Ag described by using the Drude model (red and blue solid lines) and experimental data (red and blue diamond-shaped markers), respectively; (b) transmission of THRC system when
Fig. 12. (a) Fitness of GA (blue) and PSO (black) for different generations in the inverse design; (b) comparison results between the ANN-predicted parameters, GA-optimized, and PSO-optimized structure parameters; (c) FDTD-simulated transmission spectra calculated for the ANN-predicted, GA-optimized, and PSO-optimized structure parameters.
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Tian Zhang, Jia Wang, Qi Liu, Jinzan Zhou, Jian Dai, Xu Han, Yue Zhou, Kun Xu, "Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks," Photonics Res. 7, 368 (2019)
Category: Plasmonics and Metamaterials
Received: Nov. 9, 2018
Accepted: Jan. 11, 2019
Published Online: Mar. 7, 2019
The Author Email: Kun Xu (xukun@bupt.edu.cn)