Reconstructive Spectrum Analyzer with High‐Resolution and Large‐Bandwidth Using Physical‐Model and Data‐Driven Model Combined Neural Network
Yangyang Wan, Xinyu Fan, Bingxin Xu, Zuyuan He
Abstract
Abstract Most neural networks (NNs) used for reconstructive spectrum analyzers (RSAs) rely on data‐driven training strategies, which can be time‐consuming due to the need for a large training dataset with a limited amount of output channels. Here, a specially designed NN is proposed for a reconstructive wavemeter based on temporal speckle obtained from a whispering gallery mode (WGM) resonator. By combining a physical model and data‐driven model, it only takes 10 µs to obtain a reference speckle for the generation of a training dataset. The WGM resonator‐based wavemeter assisted by the NN uses only one photo‐detector to obtain a temporal speckle, achieving a spectral resolution of 3.2 fm. The number of output channels reaches 2300, which is the largest dynamic range achieved by an NN in RSA without the need for re‐training. It demonstrates that the proposed NN has capability to resolve unseen spectrum with multi‐tone wavelengths. Moreover, the proposed network exhibits better robustness in long‐time measurement compared to data‐driven model based networks. This opens up new possibilities for NN design methods in RSA, without the need for a large training dataset, by incorporating a physical model to achieve high‐resolution, high‐dynamic‐range, and fast‐speed spectrum measurement.