Predicting early failure of quantum cascade lasers during accelerated burn-in testing using machine learning.

Area of Science:

• Optoelectronics
• Materials Science
• Machine Learning

Background:

• Device lifetime is a critical factor in the total cost of ownership for quantum cascade lasers (QCLs).
• Previous research has focused on QCL lifetime post-burn-in, with limited attention to predicting early device failures occurring within hundreds of operational hours.
• Understanding and predicting premature failure is essential for improving reliability and reducing costs in QCL applications.

Purpose of the Study:

• To develop a predictive model for identifying quantum cascade lasers (QCLs) prone to premature failure.
• To utilize standard electrical and optical measurements from an accelerated burn-in process for failure prediction.
• To enhance the reliability and cost-effectiveness of QCLs through early failure detection.

Main Methods:

• An accelerated burn-in process was employed for quantum cascade lasers (QCLs).
• Standard electrical and optical device measurements were collected during the burn-in period.
• A support vector machine (SVM) algorithm was implemented to analyze the collected data and predict device failure.

Main Results:

• The support vector machine model successfully predicted premature failure in QCLs with high confidence.
• For failing devices, at least one measurement indicated premature failure up to 200 hours before actual device demise.
• The algorithm accurately classified all measurements for devices that successfully completed the burn-in process.

Conclusions:

• Standard electrical and optical measurements during accelerated burn-in can effectively predict premature QCL failure.
• This predictive capability can significantly reduce costs associated with device failure and improve operational planning.
• The findings pave the way for further analysis into the physical mechanisms underlying premature QCL failure.