Towards Neural-Network-based optical temperature sensing of Semiconductor Membrane External Cavity Laser

TL;DR

This paper introduces a machine learning approach using neural networks to non-invasively determine the temperature of semiconductor lasers from spectral data, enabling fast, reliable, and adaptable temperature sensing without additional sensors.

Contribution

The study presents a neural network-based method for optical temperature sensing of semiconductor lasers, utilizing spectral data and transfer learning for versatile, contactless temperature inference.

Findings

Achieved sub-percent temperature inference accuracy.

Used pretrained deep neural networks for fast predictions.

Reduced computational cost by adjusting network depth.

Abstract

A machine-learning non-contact method to determine the temperature of a laser gain medium via its laser emission with a trained few-layer neural net model is presented. The training of the feed-forward Neural Network (NN) enables the prediction of the device's properties solely from spectral data, here recorded by visible-/nearinfrared-light 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 labelled 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 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.