Degradation Prediction of Semiconductor Lasers using Conditional Variational Autoencoder

TL;DR

This paper introduces a data-driven method using a conditional variational autoencoder to predict semiconductor laser degradation, outperforming traditional models and enabling early failure detection to reduce testing costs.

Contribution

The paper presents a novel unsupervised deep learning approach for laser degradation prediction that does not rely on physical models or domain knowledge.

Findings

Achieves 95.3% F1 score in degradation prediction

Outperforms baseline anomaly detection techniques

Enables early failure prediction to shorten aging tests

Abstract

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