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The National Ignition Facility (NIF) uses an in-situ system called the Final Optics Damage Inspection (FODI) system to monitor the extent of damage on installed optical components. Among this system's uses is to alert operators when damage sites on a Grating Debris Shield (GDS) require repair (≈300 microns) and triggers the removal of the damaged optic. FODI, which can reliably detect damage sites larger than 50 microns, records the size and location of observed sub-critical damage observed on the optic, so each of these sites can be repaired before the optic is next installed. However, by only identifying, and hence repairing sites larger than ≈50 microns, optics are left with numerous smaller sites, some fraction of which resume growing when the host optic is reinstalled. This work presents a method of identifying and repairing damage sites below the FODI detection limit that have a significant probability of growth. High resolution images are collected of all likely damage candidates on each optic, and a machine learning based automated classification algorithm is used to determine if each candidate is a damage site or something benign (particle, previously repaired site, etc.). Any damage site greater than 20 microns is flagged for subsequent repair. By repairing these smaller sites, recycled optics had a 40% increased lifetime on the NIF.

The National Ignition Facility (NIF) regularly operates at fluences above the onset of laser-induced optics damage. To do so, it is necessary to routinely recycle the NIF final optics, which involves removing an optic from a beamline, inspecting and repairing the laser-induced damage sites, and re-installing the optic. The inspection and repair takes place in our Optics Mitigation Facility (OMF), consisting of four identical processing stations for performing the repair protocols. Until recently, OMF has been a labor-intensive facility, requiring 10 skilled operators over two shifts to meet the throughput requirements. Here we report on the implementation of an automated control system—informed by machine learning— that significantly improves the throughput capability for recycling of NIF optics while reducing staffing requirements. Performance metrics for mid-2018 show that approximately 85% of all damage sites can be automatically inspected and repaired without any required operator input. Computer keystrokes have been reduced from about 6000 per optic to under 300.

Orchestration of every National Ignition Facility (NIF) shot cycle is managed by the Integrated Computer Control System (ICCS), which uses a scalable software architecture running code on more than...

The National Ignition Facility (NIF) at Lawrence Livermore National Laboratory (LLNL) houses the world’s largest laser system, composed of 192 individual, 40-cm-aperture beamlines. The NIF laser ro...

Two machine-learning methods were evaluated to help automate the quality control process for mitigating damage sites on laser optics. The mitigation is a cone-like structure etched into locations on large optics that have been chipped by the high fluence (energy per unit area) laser light. Sometimes the repair leaves a difficult to detect remnant of the damage that needs to be addressed before the optic can be placed back on the beam line. We would like to be able to automatically detect these remnants. We try Deep Learning (convolutional neural networks using features autogenerated from large stores of labeled data, like ImageNet) and find it outperforms ensembles of decision trees (using custom-built features) in finding these subtle, rare, incomplete repairs of damage. We also implemented an unsupervised method for helping operators visualize where the network has spotted problems. This is done by projecting the credit for the result backwards onto the input image. This shows regions in an image most responsible for the networks decision. This can also be used to help understand the black box decisions the network is making and potentially improve the training process.

Capsule ablators are precision hollow spheres used in inertial confinement fusion targets used in high-peak-power laser systems such as the National Ignition Facility. These capsules have high surf...

The National Ignition Facility (NIF) is a high-energy laser facility comprised of 192 beamlines that house thousands of optics. These optics guide, amplify and tightly focus light onto a tiny target for fusion ignition research and high energy density physics experiments. The condition of these optics is key to the economic, efficient and maximally energetic performance of the laser. Our goal, and novel achievement, is to find on the optics any imperfections while they are tens of microns in size, track them through time to see if they grow and if so, remove the optic and repair the single site so the entire optic can then be re-installed for further use on the laser. This paper gives an overview of the image analysis used for detecting, measuring, and tracking sites of interest on an optic while it is installed on the beamline via in situ inspection and after it has been removed for maintenance. In this way, the condition of each optic is monitored throughout the optic's lifetime. This overview paper will summarize key algorithms and technical developments for custom image analysis and processing and highlight recent improvements. (Associated papers will include more details on these issues.) We will also discuss the use of OI Analysis for daily operation of the NIF laser and its extension to inspection of NIF targets.

Operating a fusion-class laser to its full potential requires a balance of operating constraints. On the one hand, the total laser energy delivered must be high enough to give an acceptable probability for ignition success. On the other hand, the laser-induced optical damage levels must be low enough to be acceptably handled with the available infrastructure and budget for optics recycle. Our research goal was to develop the models, database structures, and algorithmic tools (which we collectively refer to as ''Loop Tools'') needed to successfully maintain this balance. Predictive models are needed to plan for and manage the impact of shot campaigns from proposal, to shot, and beyond, covering a time span of years. The cost of a proposed shot campaign must be determined from these models, and governance boards must decide, based on predictions, whether to incorporate a given campaign into the facility shot plan based upon available resources. Predictive models are often built on damage ''rules'' derived from small beam damage tests on small optics. These off-line studies vary the energy, pulse-shape and wavelength in order to understand how these variables influence the initiation of damage sites and how initiated damage sites can grow upon further exposure tomore » UV light. It is essential to test these damage ''rules'' on full-scale optics exposed to the complex conditions of an integrated ICF-class laser system. Furthermore, monitoring damage of optics on an ICF-class laser system can help refine damage rules and aid in the development of new rules. Finally, we need to develop the algorithms and data base management tools for implementing these rules in the Loop Tools. The following highlights progress in the development of the loop tools and their implementation.« less

The Final Optics Damage Inspection (FODI) system automatically acquires and utilizes the Optics Inspection (OI) system to analyze images of the final optics at the National Ignition Facility (NIF). During each inspection cycle up to 1000 images acquired by FODI are examined by OI to identify and track damage sites on the optics. The process of tracking growing damage sites on the surface of an optic can be made more effective by identifying and removing signals associated with debris or reflections. The manual process to filter these false sites is daunting and time consuming. In this paper we discuss the use of machine learning tools and data mining techniques to help with this task. We describe the process to prepare a data set that can be used for training and identifying hardware reflections in the image data. In order to collect training data, the images are first automatically acquired and analyzed with existing software and then relevant features such as spatial, physical and luminosity measures are extracted for each site. A subset of these sites is 'truthed' or manually assigned a class to create training data. A supervised classification algorithm is used to test if the features can predict the class membership of new sites. A suite of self-configuring machine learning tools called 'Avatar Tools' is applied to classify all sites. To verify, we used 10-fold cross correlation and found the accuracy was above 99%. This substantially reduces the number of false alarms that would otherwise be sent for more extensive investigation.

The National Ignition Facility (NIF) at the Lawrence Livermore National Laboratory (LLNL) will routinely fire high energy shots (approaching 10 kJ per beamline) through the final optics, located on the target chamber. After a high fluence shot, exceeding 4J/cm2 at 351 nm wavelength, the final optics will be inspected for laser-induced damage. The FODI (Final Optics Damage Inspection) system has been developed for this purpose, with requirements to detect laser-induced damage initiation and to track and size it's the growth to the point at which the optic is removed and the site mitigated. The FODI system is the 'corner stone' of the NIF optic recycle strategy. We will describe the FODI system and discuss the challenges to make optics inspection a routine part of NIF operations.

Phase-defects on optics used in high-power lasers can cause light intensification leading to laser-induced damage of downstream optics. We introduce Linescan Phase Differential Imaging (LPDI), a large-area dark-field imaging technique able to identify phase-defects in the bulk or surface of large-aperture optics with a 67 second scan-time. Potential phase-defects in the LPDI images are indentified by an image analysis code and measured with a Phase Shifting Diffraction Interferometer (PSDI). The PSDI data is used to calculate the defects potential for downstream damage using an empirical laser-damage model that incorporates a laser propagation code. A ray tracing model of LPDI was developed to enhance our understanding of its phase-defect detection mechanism and reveal limitations.

Lawrence Livermore National Laboratory is a large, multidisciplinary institution that conducts fundamental and applied research in the physical sciences. Research programs at the Laboratory run the gamut from theoretical investigations, to modeling and simulation, to validation through experiment. Over the years, the Laboratory has developed a substantial research component in the areas of signal and image processing to support these activities. This paper surveys some of the current research in signal and image processing at the Laboratory. Of necessity, the paper does not delve deeply into any one research area, but an extensive citation list is provided for further study of the topics presented.

Laser-induced damage growth on the surface of fused silica optics has been extensively studied and has been found to depend on a number of factors including fluence and the surface on which the damage site resides. It has been demonstrated that damage sites as small as a few tens of microns can be detected and tracked on optics installed a fusion-class laser, however, determining the surface of an optic on which a damage site resides in situ can be a significant challenge. In this work demonstrate that a machine-learning algorithm can successfully predict the surface location of the damage site using an expanded set of characteristics for each damage site, some of which are not historically associated with growth rate.

In many high energy laser systems, optics with HMDS sol gel antireflective coatings are placed in close proximity to each other making them particularly susceptible to certain types of strong optical interactions. During the coating process, halo shaped coating flaws develop around surface digs and particles. Depending on the shape and size of the flaw, the extent of laser light intensity modulation and consequent probability of damaging downstream optics may increase significantly. To prevent these defects from causing damage, a coating flaw removal tool was developed that deploys a spot of decane with a syringe and dissolves away the coating flaw. The residual liquid is evacuated leaving an uncoated circular spot approximately 1mm in diameter. The resulting uncoated region causes little light intensity modulation and thus has a low probability of causing damage in optics downstream from the mitigated flaw site.

Many automated image-based applications have need of finding small spots in a variably noisy image. For humans, it is relatively easy to distinguish objects from local surroundings no matter what else may be in the image. We attempt to capture this distinguishing capability computationally by calculating a measurement that estimates the strength of signal within an object versus the noise in its local neighborhood. First, we hypothesize various sizes for the object and corresponding background areas. Then, we compute the Local Area Signal to Noise Ratio (LASNR) at every pixel in the image, resulting in a new image with LASNR values for each pixel. All pixels exceeding a pre-selected LASNR value become seed pixels, or initiation points, and are grown to include the full area extent of the object. Since growing the seed is a separate operation from finding the seed, each object can be any size and shape. Thus, the overall process is a 2-stage segmentation method that first finds object seeds and then grows them to find the full extent of the object. This algorithm was designed, optimized and is in daily use for the accurate and rapid inspection of optics from a large laser system (National Ignition Facility (NIF), Lawrence Livermore National Laboratory, Livermore, CA), which includes images with background noise, ghost reflections, different illumination and other sources of variation.

That National Ignition Facility (NIF) at Lawrence Livermore National Laboratory (LLNL) will be the world's largest and most energetic laser. It has thousands of optics and depends heavily on the quality and performance of these optics. Over the past several years, we have developed the NIF Optics Inspection Analysis System that automatically finds defects in a specific optic by analyzing images taken of that optic. This paper describes a new and complementary approach for the automatic detection of defects based on detecting the diffraction ring patterns in downstream optic images caused by defects in upstream optics. Our approach applies a robust pattern matching algorithm for images called Gradient Direction Matching (GDM). GDM compares the gradient directions (the direction of flow from dark to light) of pixels in a test image to those of a specified model and identifies regions in the test image whose gradient directions are most in line with those of the specified model. For finding rings, we use luminance disk models whose pixels have gradient directions all pointing toward the center of the disk. After GDM identifies potential rings locations, we rank these rings by how well they fit the theoretical diffraction ring pattern equation. We perform false alarm mitigation by throwing out rings of low fit. A byproduct of this fitting procedure is an estimate of the size of the defect and its distance from the image plane. We demonstrate the potential effectiveness of this approach by showing examples of rings detected in real images of NIF optics.

DNA microarrays encompassing the entire genome of Yersinia pestis were used to characterize global regulatory changes during steady-state vegetative growth occurring after shift from 26 to 37°C in the presence and absence of Ca 2+ . Transcriptional profiles revealed that 51, 4, and 13 respective genes and open reading frames (ORFs) on pCD, pPCP, and pMT were thermoinduced and that the majority of these genes carried by pCD were downregulated by Ca 2+ . In contrast, Ca 2+ had little effect on chromosomal genes and ORFs, of which 235 were thermally upregulated and 274 were thermally downregulated. The primary consequence of these regulatory events is profligate catabolism of numerous metabolites available in the mammalian host.

To more accurately measure fluorescent signals from microarrays, we calibrated our acquisition and analysis systems by using groundtruth samples comprised of known quantities of red and green gene-specific DNA probes hybridized to cDNA targets. We imaged the slides with a full-field, white light CCD imager and analyzed them with our custom analysis software. Here we compare, for multiple genes, results obtained with and without preprocessing (alignment, color crosstalk compensation, dark field subtraction, and integration time). We also evaluate the accuracy of various image processing and analysis techniques (background subtraction, segmentation, quantitation and normalization). This methodology calibrates and validates our system for accurate quantitative measurement of microarrays. Specifically, we show that preprocessing the images produces results substantially closer to the known groundtruth for these samples.

After every other failure mode has been considered, in the end, the high-performance limit of all lasers is set by optical damage. The demands of inertial confinement fusion (ICF) pushed lasers des...