How to test the long-term stability of the laser in three months

The book provides a practical solution for testing laser stability over a three-month period, including testing procedures, equipment selection, and data analysis.They help engineers to design scientific testing protocols, and they conduct tests on the aging of lasers and other core components, as well as environmental simulations, to ensure product reliability.

Why do we need a three-month testing cycle?

The long-term stability of a laser directly affects the performance and lifespan of the equipment.The three-month test period allows the researchers to simulate the continuous workloads that the parts will experience in actual use, and also captures the cumulative effects of environmental factors such as temperature and humidity.If the time is too short, potential problems may be missed, but if it is too long, then research and development is slowed down. Three months is a "golden balance point.

The preparations before testing.

A clearly defined objective and indicator.

First, you need to decide what you are testing for: output power fluctuations? Wavelength offset? Heat dissipation performance? It is suggested that you take the key parameters in the national standard (such as GB / T 15306) as a baseline, then add or remove indicators based on the characteristics of your own products.

Build a simulated workplace.

Don't test in the laboratory 'greenhouse'! In the real world there may be temperature fluctuations and voltage fluctuations.We recommend using a programmable temperature chamber to simulate a temperature cycle between -20 and 60 ℃, and a stabilized power source to create a ± 10 % voltage disturbance.Remember to add a cooling plate to the laser--this is closer to the real operating conditions.

The key points in the execution of the test.

Collecting data requires "hard work.

In the first two weeks, record the data three times a day, then reduce to once a day.The focus is on the performance of the machine during the initial start-up phase, when the performance of the machine is most likely to change, and the middle phase, when the machine is most stable.There is a small trick to this: Use a power meter with cloud storage. Then you can check the real-time curve on your smartphone, which is much easier than manually copying the data.

The idea was to put the system under stress.

Every 10 days, they perform a stress test. They suddenly cut the power, then turn it back on, and run the computer at full load for 12 hours.These stress tests expose problems of inadequate redundancy.Last year we used this method to discover that the performance of a certain model of heat sink was declining because of a 0.5-mm difference in thickness.

Data analysis and optimization recommendations.

Don't just look at the average.

More important than the amplitude and frequency of the fluctuations is the trend.For instance, one laser would lose 2 % of its power every afternoon. It was later discovered that this was caused by changes in humidity due to the air conditioning being automatically switched on and off at certain times. Such periodic fluctuations are impossible to detect by looking at average values.

They are compared with batches from other years to see the differences.

If you test several samples at the same time, you need to pay special attention to the dispersion.Last year six samples were tested, and three of them showed the same level of deterioration after 60 days. It was discovered that the three were all from the same batch of sealant, which did not meet the standard for heat resistance.If such a problem is discovered early on, it can prevent a large-scale recall.

The practical value of test reports.

Finally, when the data were being processed, he suggested that two reports be produced: a technical report for the R & D department, with a focus on the actual changes in parameters; and a simplified report for management, using a line graph to show the extent to which reliability had been improved.During the test process, take some photos of the state of the samples. When you report, they're particularly persuasive--after all, seeing is believing!