Abstract
This paper introduces a sensitivity test for characterizing the laser damage behavior of a sample. A sensitivity test analyzes unbinned laser damage test data to estimate the damage probability curve. The means of estimation is by employing a parametric model of the probability of damage and identifying the parameters most likely to produce the observed results using the maximum-likelihood (ML) method. The ML method applied to laser damage measurements is reviewed. The sensitivity test is analyzed for its performance using Monte Carlo methods. A series of laser damage tests are simulated on a test of a hypothetical test optic. A Weibull distribution is selected for the damage probability model, while the virtual test optic was chosen to have a non-Weibull shaped damage probability curve. Damage measurements for varying the number of sites exposed are modeled to show the convergence of the Weibull parameters. This paper concludes by showing how the underlying defect distribution is calculated from results of the sensitivity test.
Highlights
Introduction and MotivationThis paper introduces a sensitivity test for the characterization of laser damage behavior of a test sample
Application of maximum-likelihood (ML) methods has a long history in the study of laser damage, especially laser safety measurements, “laser damage of the eye,” and date back to at least 1970.5–7 The first application of ML methods to a nonbiological test sample is the determination of a laser damage threshold with a binomial model on multiple shot testing done at China Lake in the late 1970s
This paper has introduced a sensitivity test for laser damage measurements
Summary
This paper introduces a sensitivity test for the characterization of laser damage behavior of a test sample. There has been a renaissance in the application of ML techniques to various aspects of the laser damage problem by several groups.[9,10,11,12] A group from the Lawrence Livermore National Laboratory has used ML damage characterization as part of a procedure to extract the distributions of defects on fused silica, an idea we will revisit later in this work.[9] The group from Vilnius University in Lithuania has used ML methods applied to a degenerate defect model on data binned by fluence to study many aspects of the laser damage threshold determination problem.[10,11] The results of the Vilnius group show a major increase in the quality of the determination of the threshold over damage frequency methods, frequently used in ISO standard.
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