Mitigation of laser damage on National Ignition Facility optics in volume production

Folta, James A.; Nostrand, Mike C.; Honig, J.; Wong, Nan; Ravizza, Frank; Geraghty, Paul B.; Taranowski, Mike; Johnson, Gary W.; Larkin, G. Richard; Ravizza, Doug; Peterson, J.L.; Welday, Brian; Wegner, Paul J.

doi:10.1117/12.2030475

Cited by 28 publications

(13 citation statements)

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“…An optic that has been installed online and has incurred damage is monitored and a limited number of damage sites are blocked to stop them from growing. When the maximum size of the damage sites reaches a threshold, the optic is exchanged and the laser damage is laser mitigated to limit its ability to grow [8,14]. The number of recycles corresponds to the number of times the optic has been exchanged and laser mitigated.…”

Section: Analysis Resultsmentioning

confidence: 99%

“…In our initial analysis of WFL damage performance, we established the life cycle of the optics from the first installation to when the optic eventually was taken to be refinished [8]. This is because we had believed that most damage precursors are introduced in the grinding and polishing processing of an optic [12].…”

Section: Analysis Resultsmentioning

confidence: 99%

“…In addition, not all essential parameters needed for accurate damage modeling are measured; for example, NIF does not measure 3ω local fluences at all 192 beams. Furthermore, the number of optics NIF handles for its operation is staggering, especially if accounting for optics that have been recycled multiple times [8]. Unlike in an offline facility, these parts have been processed over long periods (years) and some of this optics processing could have changed, either intentionally or unintentionally.…”

Section: Introductionmentioning

confidence: 96%

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Optics damage modeling and analysis at the National Ignition Facility

et al. 2014

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Comprehensive modeling of laser-induced damage in optics for the National Ignition Facility (NIF) has been performed on fused silica wedge focus lenses with a metric that compares the modeled damage performance to online inspections.The results indicate that damage models are successful in tracking the performance of the fused silica final optics when properly accounting for various optical finishes and mitigation processes. This validates the consistency of the damage models and allows us to further monitor and evaluate different system parameters that potentially can affect optics performance.

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Section: Analysis Resultsmentioning

confidence: 99%

Section: Analysis Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

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Optics damage modeling and analysis at the National Ignition Facility

et al. 2014

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“…These systems constantly monitor damage on optics and track its growth from as small as around 10 µm. When the condition of an optic reaches its optic‐specific limit, that optic is removed and each qualifying damage site is repaired . The repair process was automated, in part, by using a deep learning algorithm for quality control of the repaired sites .…”

Section: Introductionmentioning

confidence: 99%

A hybrid deep learning architecture for classification of microscopic damage on National Ignition Facility laser optics

Amorin

Kegelmeyer

2019

Statistical Analysis

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Accurately classifying microscopic damage helps automate the repair and recycling of National Ignition Facility optics and informs the study of damage initiation and growth. This complex 12-class problem previously required human experts to distinguish and label the various damage morphologies. Finding image analysis methods to extract and calculate distinguishing features would be time consuming and challenging, so we sought to automate this task by using convolutional neural networks (CNNs) pretrained on the ImageNet database to take advantage of its automated feature discovery and extraction. We compared three model architectures on this dataset and found the one with highest overall accuracy, 99.17%, was a novel hybrid architecture, one in which we removed the final decision-making layer of the deep learner and replaced it with an ensemble of decision trees (EDT). This combines the power of feature extraction by CNNs with the decision-making strength of EDT.The accuracy of the hybrid architecture over the deep learning alone is shown to be significantly improved. Furthermore, we applied this novel hybrid architecture to an entirely different dataset, one containing images of repaired damage sites, and improved on the previously published findings, also with a demonstrably significant increase in accuracy over using the deep learner alone. K E Y W O R D Sautomation, deep learning, laser optic damage, machine learning

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“…All of these target by-products can affect their near environment surfaces, especially components of the vacuum target chamber that such experiments are performed in. The largest and most expensive optical components of the focusing systems are located close to the targets but are usually protected by debris shields [DS], [4]. The non-linear optical properties of standard materials allows the use of debris shields in the nanosecond regime without the degradation of focal spot irradiance but this is often not true for Petawatt class, picosecond order duration pulses from beams derived from chirped pulsed amplified [CPA] systems [5].…”

Section: Introductionmentioning

confidence: 99%

The effects of plasma physics target shrapnel and debris plumes arising from early operations of the Orion laser

et al. 2014

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When lasers are used to produce high temperature, high density plasmas from solid targets it is inevitable that the targets are turned into a variety of products [gas, liquid, solid, sub-atomic particles and electromagnetic radiation] that are distributed around the surfaces of the vacuum chamber used to field such experiments. These by products are produced in plumes of debris and shrapnel that depend on the irradiation conditions, target materials and target geometry. We have monitored the distribution of such plumes by witness plates and used microscopy, photography and spectrophotometry to determine the physical state of material in the plumes and the spatial distribution from various target geometries. The impact of this material on the operations of laser optics and plasma physics diagnostics is discussed.

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Mitigation of laser damage on National Ignition Facility optics in volume production

Cited by 28 publications

References 0 publications

Optics damage modeling and analysis at the National Ignition Facility

Optics damage modeling and analysis at the National Ignition Facility

A hybrid deep learning architecture for classification of microscopic damage on National Ignition Facility laser optics

The effects of plasma physics target shrapnel and debris plumes arising from early operations of the Orion laser

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