Degradation Prediction of Semiconductor Lasers Using Conditional Variational Autoencoder

Semiconductor lasers have been rapidly evolving to meet the demands of next-generation optical networks. This imposes much more stringent requirements on the laser reliability, which are dominated by degradation mechanisms (e.g., sudden degradation) limiting the semiconductor laser lifetime. Physics...

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Vydáno v:Journal of lightwave technology Ročník 40; číslo 18; s. 6213 - 6221
Hlavní autoři: Abdelli, Khouloud, Grieser, Helmut, Neumeyr, Christian, Hohenleitner, Robert, Pachnicke, Stephan
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 15.09.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Anomalies
Data models
Decoding
Degradation
Degradation prediction
Laser modes
Lasers
Logic gates
machine learning
Mathematical models
Network reliability
Optical communication
Reliability
semiconductor laser reliability
Semiconductor lasers
Tunable lasers
variational autoencoder
Vertical cavity surface emission lasers
Vertical cavity surface emitting lasers
ISSN:0733-8724, 1558-2213
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Shrnutí:Semiconductor lasers have been rapidly evolving to meet the demands of next-generation optical networks. This imposes much more stringent requirements on the laser reliability, which are dominated by degradation mechanisms (e.g., sudden degradation) limiting the semiconductor laser lifetime. Physics-based approaches are often used to characterize the degradation behavior analytically, yet explicit domain knowledge and accurate mathematical models are required. Building such models can be very challenging due to a lack of a full understanding of the complex physical processes inducing the degradation under various operating conditions. To overcome the aforementioned limitations, we propose a new data-driven approach, extracting useful insights from the operational monitored data to predict the degradation trend without requiring any specific knowledge or using any physical model. The proposed approach is based on an unsupervised technique, a conditional variational autoencoder, and validated using vertical-cavity surface-emitting laser (VCSEL) and tunable edge emitting laser reliability data. The experimental results confirm that our model (i) achieves a good degradation prediction and generalization performance by yielding an F1 score of 95.3%, (ii) outperforms several baseline ML based anomaly detection techniques, and (iii) helps to shorten the aging tests by early predicting the failed devices before the end of the test and thereby saving costs.
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ISSN:0733-8724
1558-2213
DOI:10.1109/JLT.2022.3188831