Predicting premature failure of quantum cascade lasers with different quantum designs using active machine learning

Mid-infrared quantum cascade lasers are crucial for a variety of applications, including fundamental research, industry, and homeland security. While laser lifetimes can exceed 100,000 h, there is an initial period of premature failure (e.g., less than 400 h of operation) that increases the cost of ownership. Here, machine learning algorithms based on support vector machines are developed to predict the failure of quantum cascade lasers with different quantum designs and device properties (facet coating, resonator length, and laser ridge width). The algorithm identifies up to 700% more devices that fail prematurely in the first 40 h of testing compared to conventional approaches. Additionally, training the support vector machine on devices from a single quantum design is sufficient for predicting the performance of lasers from a second, significantly different quantum design. This research not only helps ensure reliable operation of the delivered devices with high confidence in the estimations but also offers a valuable framework for further understanding the causes of premature QCL failures.