The FDTD analysis for dark field in-process depth measurements of fine microgrooves

Guan, Yizhao; Kadoya, Shotaro; Michihata, Masaki; Takahashi, Satoru

doi:10.1016/j.measen.2021.100257

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…Edited by Nai Mui Ling Sharon and A. Senthil Kumar has a cutoff condition that light can hardly go into the microgroove when the width is smaller than half of the wavelength. The theoretical cutoff condition is given by the following equation [6]. Therefore, the TE phase-depth relation has almost no dependency when the pitch size is smaller than the wavelength of 633 .…”

Section: Table 1 Parameters For Numerical Simulationmentioning

confidence: 99%

“…The previously mentioned problem of phase blur can be solved by dark-field observation: the reflection from the top surface of the microgroove is not collected by microscopy under oblique illumination, while the bottom reflection can propagate perpendicularly and be collected by microscopy. Therefore, a dark-field depth measurement method has been proposed that the phase of TM can measure the depth of the microgroove by taking the TE phase as a reference [6].…”

Section: Introductionmentioning

confidence: 99%

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Optical Depth Measurement for Microgrooves: A Self-interferometry Method based on Near-field Polarization Analysis

Guan¹,

Masui²,

Kadoya³

et al. 2022

Asian Society for Precision Engineering and Nanotechnology (ASPEN 2022)

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Section: Table 1 Parameters For Numerical Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optical Depth Measurement for Microgrooves: A Self-interferometry Method based on Near-field Polarization Analysis

Guan¹,

Masui²,

Kadoya³

et al. 2022

Asian Society for Precision Engineering and Nanotechnology (ASPEN 2022)

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Wavelength optimization of fine optical surface defect detection based on FDTD

Wang,

Lan,

Wang

et al. 2024

Appl. Opt.

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Due to the similar order of magnitude of the defect size and the detection wavelength, when detecting micro-/nano-scale defects on the surface of a fine optical component, the intense modulation of the optical field poses challenges in correlating imaging widths of defects with actual widths. A dark-field scattering imaging (DFSI) model, based on the finite difference time domain (FDTD) method, is established to study the imaging for triangular and circular section defects and investigate the influence of wavelength on defect width detection. Simulated results indicate that a shorter wavelength of the light source in a DFSI detection system leads to a larger mapping range between the imaging width and the actual defect width, which makes calibration less difficult. A DFSI detection system for micro-/nano-scale surface defects on optical components is built to test defects with rectangular cross-sections on a calibration board. The defect widths estimated from the experimental and simulated results are in good agreement, with a root-mean-square error (RMSE) of 0.11 µm.

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Advanced Sensing and Machine Learning Technologies for Intelligent Measurement in Smart and Precision Manufacturing

Sato,

Li,

Michihata

et al. 2024

IJAT

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This paper provides an overview of state-of-the-art sensing and machine learning technologies for intelligent measurement in smart and precision manufacturing. Length, angle, and force are identified as the fundamental quantities for production quality management based on process monitoring as well as geometrical metrology in optical lithography and mechanical machining. Advancements in length-based measurement technologies such as laser interferometers and optical encoders, as well as advancements regarding depth and thickness measurements, are presented. Various types of optical microscopes, such as evanescent field microscopes, structured illumination microscopes, and confocal microscopes, are also described. For angle-based measurement technologies, in addition to the conventional continuous-wave laser autocollimators, the newly developed Fabry–Pérot angle sensor and nonlinear optics angle sensor using an ultrashort pulse laser are presented. Finally, on-machine and in-process force sensing and machining learning techniques for dimensional and machining process monitoring are reviewed.

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The FDTD analysis for dark field in-process depth measurements of fine microgrooves

Cited by 3 publications

References 6 publications

Optical Depth Measurement for Microgrooves: A Self-interferometry Method based on Near-field Polarization Analysis

Optical Depth Measurement for Microgrooves: A Self-interferometry Method based on Near-field Polarization Analysis

Wavelength optimization of fine optical surface defect detection based on FDTD

Advanced Sensing and Machine Learning Technologies for Intelligent Measurement in Smart and Precision Manufacturing

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