Photonics (Mar 2022)

Error Analysis and Correction of Thickness Measurement for Transparent Specimens Based on Chromatic Confocal Microscopy with Inclined Illumination

  • Qing Yu,
  • Chong Wang,
  • Yali Zhang,
  • Shengming Hu,
  • Ting Liu,
  • Fang Cheng,
  • Yin Wang,
  • Tianliang Lin,
  • Lin Xi

DOI
https://doi.org/10.3390/photonics9030155
Journal volume & issue
Vol. 9, no. 3
p. 155

Abstract

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As a fast, high-accuracy and non-contact method, chromatic confocal microscopy is widely used in micro dimensional measurement. In this area, thickness measurement for transparent specimen is one of the typical applications. In conventional coaxial illumination mode, both the illumination and imaging axes are perpendicular to the test specimen. At the same time, there are also geometric measurement limitations in conventional mode. When measuring high-transparency specimen, the energy efficiency will be quite low, and the reflection will be very weak. This limitation will significantly affect the signal-to-noise ratio. The inclined illumination mode is a good solution to overcome this bottleneck, but the thickness results may vary at different axial positions of the sample. In this paper, an error correction method for thickness measurement of transparent samples is proposed. In the authors’ work, the error correction model was analyzed and simulated, and the influence caused by the different axial positions of sample could be theoretically eliminated. The experimental results showed that the thickness measurement of the samples was practically usable, and the measurement errors were significantly reduced by less than 2.12%, as compared to the uncorrected system. With this error correction model, the standard deviation had decreased significantly, and the axial measurement accuracy of the system can reach the micron level. Additionally, this model has the same correction effect on the samples with different refractive indexes. Therefore, the system can realize the requirement of measurement at different axial positions.

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