Miniaturized spectrometer based on MLP neural networks and a frosted glass encoder

Computational spectrometers are explored to overcome the disadvantages of large size, narrow bandwidth and low spectral resolution suffered by conventional spectrometers. However, expensive spectral encoders and unstable algorithms impede widespread applications of the computational spectrometers. In this paper, we propose a neural network (NN)-based miniaturized spectrometer with a frosted glass as its spectral encoder. The frosted glass has the merits of easy fabrication, low loss, and high throughput. In order to evaluate the reconstruction ability, several frequently used algorithms such as the multilayer perceptron (MLP), convolutional neural network (CNN), residual convolutional neural network (ResCNN), and Tikhonov regularization are adopted to reconstruct different types of spectra in sequence. Experimental results show that the reconstruction performance of the MLP is better than other algorithms. By using the MLP network, the average mean squared error is 1.38 × 10