Paper
16 November 2018 Characterization and repair of small damage sites and their impact on the lifetime of fused silica optics on the National Ignition Facility
Christopher F. Miller, Laura M. Kegelmeyer, Mike C. Nostrand, Rajesh Raman, David Cross, Zhi Liao, Raminder Garcha, Christopher W. Carr
Author Affiliations +
Abstract
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.
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Christopher F. Miller, Laura M. Kegelmeyer, Mike C. Nostrand, Rajesh Raman, David Cross, Zhi Liao, Raminder Garcha, and Christopher W. Carr "Characterization and repair of small damage sites and their impact on the lifetime of fused silica optics on the National Ignition Facility", Proc. SPIE 10805, Laser-Induced Damage in Optical Materials 2018: 50th Anniversary Conference, 108051D (16 November 2018); https://doi.org/10.1117/12.2501839
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Cited by 1 scholarly publication.
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KEYWORDS
National Ignition Facility

Silica

Image classification

Classification systems

Machine learning

Convolutional neural networks

Microscopy

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