Solver-informed neural networks for spectrum reconstruction of colloidal quantum dot spectrometers

Recently, the miniature spectrometer based on the optical filter array has received much attention due to its versatility. Among many open challenges, designing efficient and stable algorithms to recover the input spectrum from the raw measurements is the key to success. Of many existing spectrum reconstruction algorithms, regularization-based algorithms have emerged as practical approaches to the spectrum reconstruction problem, but the reconstruction is still challenging due to ill-posedness of the problem. To alleviate this issue, we propose a novel reconstruction method based on a solver-informed neural network (NN). This approach consists of two components: (1) an existing spectrum reconstruction solver to extract the spectral feature from the raw measurements (2) a multilayer perceptron to build a map from the input feature to the spectrum. We investigate the reconstruction performance of the proposed method on a synthetic dataset and a real dataset collected by the colloidal quantum dot (CQD) spectrometer. The results demonstrate the reconstruction accuracy and robustness of the solver-informed NN. In conclusion, the proposed reconstruction method shows excellent potential for spectral recovery of filter-based miniature spectrometers.