Abstract
We demonstrate a method of Photonic Crystal Fiber (PCF) inverse design for nonlinear wavelength conversion based on Four-Wave Mixing (FWM), where Deep learning Neural Networks (DNN) is applied to predict PCF structure parameters. By applying empirical formula of PCF dispersion instead of numerical simulation, a large dataset of phase-matching curves is generated of various PCF designs. The average running time of DNN prediction is 0.2s. With the help of DNN, we design and fabricate a PCF for wavelength conversion via FWM from 1064 nm to 770 nm. Pumped by a microchip laser at 1064 nm, signal wavelength is detected by optical spectrum analyzer at 770.2nm
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