Spectrally Programmable Optical Frequency Comb Generation

Abstract

Optical frequency combs (OFCs) are critical components in several fields, such as optical communications, microwave photonics, ranging, atomic clocks, and photonic neural networks, which meet diverse application requirements through precise spectral manipulations. Conventional OFC spectral manipulation methods use Fourier transform pulse shapers for postprocessing. However, they face constraints in terms of spectral resolution and operational bandwidth. In this study, a deep-learning-assisted approach is introduced to enable efficient spectral shaping of OFCs through nonlinear broadening, achieving broader spectral shaping by controlling a narrower-bandwidth seed comb. By training a convolutional neural network, we model complex nonlinear interactions within a highly nonlinear fiber to predict and control the spectral output of a broadened comb. Experimental validation confirms the system’s ability to generate, with high precision, diverse spectral shapes such as Gaussian, parabolic, Cauchy, Laplace, and Gaussian mixture models within seconds, with a relative error of 10^–1–10^–2. Our study presents a flexible, rapid, and efficient method for the spectral shaping of OFCs using deep learning, marking a substantial advancement in the programmability and utility of OFC technologies.