Temperature Control Performance Improvement of High-Power Laser Diode with Assistance of Machine Learning

For a laser diode (LD) with high output power, it is difficult to precisely and quickly control its temperature because of the large thermal power involved. In this paper, a machine learning-based temperature controller for high-power LDs is reported. It is implemented by developing a back-propagation neural network (BPNN) with an adaptive dynamic adjustment strategy (ADAS) temperature controller which integrates a constant-current-source circuit into the conventional proportional-integral-derivative (PID) temperature-controlling circuit. Compared to the conventional PID controller, the speed of temperature control had been shortened from 1300 s to 350 s, the long-term temperature fluctuation was decreased from ±0.148% to ±0.082%, and the step response time could be decreased from 960 s to 210 s.