Semiconductor Laser
Semiconductor lasers are crucial components in numerous applications, and current research focuses on improving their reliability and lifespan. This involves developing advanced predictive maintenance frameworks using machine learning techniques, such as recurrent neural networks, convolutional autoencoders, and variational autoencoders, to analyze operational data and predict degradation or failure. These data-driven approaches aim to optimize manufacturing processes, reduce costly aging tests, and ultimately enhance the performance and longevity of semiconductor lasers in optical communication and other technologies. The resulting improvements in reliability and predictive capabilities have significant implications for the manufacturing and deployment of these essential devices.
Papers
Degradation Prediction of Semiconductor Lasers using Conditional Variational Autoencoder
Khouloud Abdelli, Helmut Griesser, Christian Neumeyr, Robert Hohenleitner, Stephan Pachnicke
A Machine Learning-based Framework for Predictive Maintenance of Semiconductor Laser for Optical Communication
Khouloud Abdelli, Helmut Griesser, Stephan Pachnicke