Acta Optica Sinica, Volume. 42, Issue 23, 2328001(2022)

Alignment Technique of Slope and Curvature Hybrid Wavefront Sensor Based on Scalar Diffraction Theory

Peng Qin, Ke Liu*, Hui Zhong, Yanqiu Li, Xiaotian Zhang, and Fei He
Author Affiliations
  • Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
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    The four-hole amplitude-modulated wavefront sensor (FHAM-WS) introduces amplitude modulation in each sub-aperture of Shack-Hartmann sensor (SHS) to measure the slope and curvature of the wavefront in the sub-aperture. The precise alignment of FHAM-WS is essential to achieve high precision wavefront sensing. In this paper, the scalar diffraction theory is used to analyze the slope and curvature measurement errors of the wavefront introduced by the alignment error of the microlens array in FHAM-WS in each sub-aperture. Taking the error as input, the aberration introduced by the microlens array alignment error in the whole wave surface measurement is obtained by using the slope and curvature mixed wavefront reconstruction technique. The sensitivities of various aberrations introduced by the focal plane offset error and tilt error of the microlens array in FHAM-WS are simulated and analyzed, and the alignment technique scheme of FHAM-WS is established. The validity of this method is verified by the zero-test experiment of FHAM-WS. The experimental results show that the absolute measurement accuracy of FHAM-WS can reach 0.005λ (root mean square, wavelength of λ=635 nm) after the calibration by this method.

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    Peng Qin, Ke Liu, Hui Zhong, Yanqiu Li, Xiaotian Zhang, Fei He. Alignment Technique of Slope and Curvature Hybrid Wavefront Sensor Based on Scalar Diffraction Theory[J]. Acta Optica Sinica, 2022, 42(23): 2328001

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    Paper Information

    Category: Remote Sensing and Sensors

    Received: May. 10, 2022

    Accepted: Jun. 14, 2022

    Published Online: Dec. 14, 2022

    The Author Email: Liu Ke (liuke@bit.edu.cn)

    DOI:10.3788/AOS202242.2328001

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