Towards Neural-Network-Based Optical Temperature Sensing of Semiconductor Membrane External Cavity Laser

Abstract

A machine-learning (ML) non-contact method to determine the temperature of a laser gain medium via its laser emission with a trained few-layer neural-network (NN) model is presented. The training of the feed-forward NN enables the prediction of the device's properties solely from spectral data, here recorded by visible-/nearinfrared-light (VIS/NIR) compact micro-spectrometers for both a diode pump laser and optically-pumped gain membrane of a semiconductor disk laser. Fiber spectrometers are used for the acquisition of large quantities of labeled intensity data, which can afterwards be used for the prediction process. Such pretrained deep NNs enable a fast, reliable and easy way to infer the temperature of a laser system such as our Membrane External Cavity Laser, at a later monitoring stage without the need of additional optical diagnostics or read-out temperature sensors. With the miniature mobile spectrometer and the remote detection ability, the temperature inference capability can be adapted for various laser diodes using transfer learning methods with pretrained models. Here, mean-square-error (mse) values for the temperature inference corresponding to sub-percent accuracy of our sensor scheme are reached, while computational cost can be saved by reducing the network depth at the here displayed cost of accuracy, as appropriate for different application scenarios.