Dispersion-Oriented Inverse Design of Photonic-Crystal Fiber for Four-Wave Mixing Application

In this paper, we demonstrate the application of a deep learning neural network (DNN) in the dispersion-oriented inverse design of photonic-crystal fiber (PCF) for the fine-tuning of four-wave mixing (FWM). The empirical formula of PCF dispersion is applied instead of numerical simulation to generate a large dataset of phase-matching curves of various PCF designs, which significantly improves the accuracy of the DNN prediction. The accuracies of DNNs' predicted PCF structure parameters are all above 95%. The simulations of the DNN-predicted PCFs structure demonstrate that the FWM wavelength has an average numerical mean square error (MAE) of 1.92 nm from the design target. With the help of DNN, we designed and fabricated a specific PCF for wavelength conversion via FWM from 1064 nm to 770 nm for biomedical imaging applications. Pumped by a microchip laser at 1064 nm, the signal wavelength is measured at 770.2 nm.

Automated summary

In this paper, the application of a deep learning neural network (DNN) in the dispersion-oriented inverse design of photonic-crystal fiber (PCF) for four-wave mixing (FWM) fine-tuning is demonstrated. A large dataset of phase-matching curves of various PCF designs is generated using the empirical formula of PCF dispersion instead of numerical simulation, significantly improving the accuracy of the DNN prediction. The accuracies of DNNs' predicted PCF structure parameters are all above 95%. The simulations of the DNN-predicted PCFs structure show that the FWM wavelength has an average numerical mean square error (MAE) of 1.92 nm from the design target. With the assistance of DNN, a specific PCF for wavelength conversion from 1064 nm to 770 nm is designed and fabricated for biomedical imaging applications, with the signal wavelength measured at 770.2 nm.