Evaluation of protection films for optics in space industry

J. Eck; S. Fontorbes; M. Bonhomme; Y. Goueffon

doi:10.1117/12.2567283

21 August 2020 Evaluation of protection films for optics in space industry

J. Eck, S. Fontorbes, M. Bonhomme, Y. Goueffon

Proceedings Volume 11489, Systems Contamination: Prediction, Control, and Performance 2020; 114890N (2020) https://doi.org/10.1117/12.2567283
Event: SPIE Optical Engineering + Applications, 2020, Online Only

Abstract

Newly developed space instruments are increasingly efficient and accurate. One of the main drawbacks is that they are consequently more and more sensitive to contamination. Once integrated, optics are continuously exposed to cleanroom environment. Most of the time, cleaning operations are risky or even impossible considering coatings fragility or accessibility constraints. Thus even in cleanrooms, with the use of specific covers and/or the implementation of purging, inducing altogether stringent operational constraints, molecular and particulate contamination deposition appears to be unavoidable. Hence, the use of a protection film on the most critical optics during manufacturing assembly and tests would allow a significant reduction of the overall contamination levels or the release of the operational constraints to reach the same cleanliness target. This study presents the results obtained in the frame of a test campaign aiming at identifying and evaluating the most promising protection films (including varnishes, adhesive tapes and electrostatic films) dedicated to space instruments optics. Impacts on both molecular and particulate contamination were investigated together with associated effects on protected substrates (aspect, spectral properties, …). The best solution has been qualified at Airbus and is currently implemented on flight hardware.

Conference Presentation

Citation Download Citation

J. Eck, S. Fontorbes, M. Bonhomme, and Y. Goueffon "Evaluation of protection films for optics in space industry", Proc. SPIE 11489, Systems Contamination: Prediction, Control, and Performance 2020, 114890N (21 August 2020); https://doi.org/10.1117/12.2567283

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