Machine learning control of an elliptically bent hard X-ray mirror

被引：2

作者：

Mashrafi, Sheikh ^{[1
]}

Harder, Ross ^{[1
]}

Shi, Xianbo ^{[1
]}

Shu, Deming ^{[1
]}

Qiao, Zhi ^{[1
]}

Wyman, Max ^{[1
]}

Mooney, Tim ^{[1
]}

Anton, Jayson ^{[1
]}

Kearney, Steven ^{[1
]}

Rebuffi, Luca ^{[1
]}

Qian, Jun ^{[1
]}

Shi, Bing ^{[1
]}

Assoufid, Lahsen ^{[1
]}

机构：

[1] Argonne Natl Lab, Adv Photon Source, 9700 S Cass Ave, Lemont, IL 60439 USA

来源：

ADVANCES IN X-RAY/EUV OPTICS AND COMPONENTS XV | 2020年 / 11491卷

关键词：

Adaptive optics; in-situ surface profiler; machine learning; neural network; ADAPTIVE OPTICS;

D O I：

10.1117/12.2567725

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This article showcases the high-resolution control of an elliptically bent hard X-ray mirror optics at the Advanced Photon Source. The mirror uses a compact laminar flexure bending mechanism to achieve elliptical shapes covering a large range of focal distances. An array of capacitive sensors are used as a surface profiler for in-situ monitoring of the mirror shape. Machine learning and control techniques were used to change the mirror shape and focus the incident X-ray at predefined focal planes. The mirror surface shape error can be controlled to be within 40 nm rms with high repeatability. This technique gives the capability to focus incident X-ray beam within a range of focal distances corresponding to shape deformation range of a mirror optics. This work would be beneficial for controlling similar adaptive optics for multiple adaptive optics systems.

引用

页数：9

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