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Issue 17 ， 2022
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Uncertainty evaluation analysis of surface features in surface structured light measurement
Abstract：Focusing on the problem of uncertainty in the traceability of free-form surface contours measured using a surface structured light scanner， two different uncertainty evaluation models were proposed. The transparent box model， based on error source analysis， was used to calculate the matrix of transformation of the measurement point from the measurement coordinate system into the coordinate system in the CAD simulation. The measurement uncertainty was then analyzed according to the functional relationship of the surface profile， and the rotation and translation parameters obtained from the coordinate transformation. By contrast， the black box model， based on quantitative statistics， obtained the characteristic index of the measurement system from analysis of the measurement result. The evaluation model was established according to the indication， self-calibration， point cloud splicing， and point cloud registration errors， as well as the repeatability， reproducibility， and instrument resolution to enable uncertainty evaluation. The experimental results show that the uncertainty based on the transparent box model is 0.020 mm and the uncertainty based on the black box model is 0.023 mm. The quantitative statistical method is efficient and convenient for evaluating the uncertainty of measurements of a free surface obtained using the structured light scanning measurement system.Keywords：optical three-dimensional measurement;surface structured light;surface contour;transparent box model;black box;uncertainty- 310
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Published：2022-10-08 -
Optical system and stray light suppression of Lyot coronagraph
Abstract：The Io plasma torus is the most densest and most widely studied region of Jupiter's magnetosphere. To meet observe the Io plasma torus observation， a Lyot Coronagraph was installed in the planet's atmosphere spectrum telescope to meet the needs of imaging observation of Io plasma torus capture the emissive spectrum. The parameters of the instrument were determined according to the radiation characteristics of the Jupiter and the parameters of PAST. Then， the initial structure was optimized. The imaging performance of the system was analyzed， and the MTF value at 37 lp/mm is above 0.6 that meet the design specification. Considering the character of stray light in system， a structure system for suppressing stray light was built considering the characteristics of stray light. The level of stray light in the system was measured in a class 1 000 cleanroom. The experimental results show that the main sources of stray light can be suppressed entirely， and the stray light suppression level of the system is equal to 10-5 at 2.5Rj， which satisfies the system requirements for observing Io plasma torus.Keywords：optical design;optical remote sensing;astronomical instrument;stray light;coronagraph- 202
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Published：2022-10-08 -
Micro-displacement measurement based on interference of vortex beams and plane wave
Abstract：Owing to its high sensitivity and repeatability， optical interferometry is often used to measure geometric quantities， such as micro-displacement， with high accuracy. To achieve the accurate measurement of nanodisplacements， a linear relationship between the rotation angle of a petal-like interferogram and the displacement， was established and verified based on the interference of vortex beams and plane waves. A novel method of microdisplacement measurement was proposed， which used the structure of an interference system with vortex beams. The feasibility of the principle was verified by simulation， and a displacement experimental system was established for the measurement experiments. The experimental results show that when the displacement is 200.0 nm， the measurement result is 196.3 nm， with an error of 3.7 nm or 1.9%， which can achieve microdisplacement measurement on a nanometer scale. Compared with traditional displacement measurement schemes， such as spherical wave interference and conjugate vortex beams interference， the proposed method has significant advantages in measurement reliability and accuracy.Keywords：interferometry measurement;vortex beams;micro-displacement;plane wave- 299
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Published：2022-10-08 -
Thickness measurement of radial gradient index lens under eccentricity by spectral confocal method
Abstract：To accurately measure the thickness of the radial GRIN lens， the measurement error caused by eccentricity was studied. First， the working principle of the spectral confocal thickness measurement system was introduced. Then， the thickness measurement model for the radial GRIN lens was established by using the ray trace equation， optical Lagrange function， and ray arc differential equation in the Cartesian coordinate system. The thickness measurement error caused by the eccentricity of the radial GRIN lens was analyzed through theoretical research and simulation， and the experimental platform was built. The thickness measurement was completed by using the precision displacement table to drive the GRIN lens to simulate lens eccentricity. The experimental results show that the thickness measurement error of radial GRIN lens increases with the eccentricity. The influence of axial measurement position on the thickness measurement is negligible. The accuracy of the theoretical analysis is verified. The thickness measurement error of the GRIN lens with an actual thickness of 4.012 6 mm is 4.6 μm after correcting for eccentricity. This indicates that the spectral confocal method can realize the precise measurement of radial GRIN lens thickness.Keywords：spectral confocal method;thickness measurement;radial gradient index（GRIN） lens;decentration;spectral peak drift- 154
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Published：2022-10-08
Modern Applied Optics
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Prediction for subsurface damage depth of silicon wafers in workpiece rotational grinding
Abstract：Workpiece rotational grinding is the primary machining process for the bare wafer flattening and pattern wafer back-thinning of large silicon wafers. However， the grinding process inevitably causes surface and subsurface damage on the ground silicon wafers. The subsurface damage depth of ground silicon wafers is critical for evaluating the grinding process. To predict the subsurface damage depth of silicon wafers in workpiece rotational grinding and optimize the grinding parameters， the wafer surface topography， material removal mechanism， and the underlying fracture mechanics were comprehensively analyzed， and a mathematical relationship among the grain cut depth， surface roughness Ra， and subsurface damage depth was derived. Subsequently， a predictive model for the subsurface damage depth of silicon wafers due to workpiece rotational grinding was established， and silicon wafer grinding experiments were conducted to validate the model. The experimental results indicate that the subsurface damage depth of silicon wafers machined via workpiece rotational grinding increases with the ground surface roughness. The predicted subsurface damage depths of ground silicon wafers are consistent with the actual measured values， and the accuracy of predictive model is less than 10%. These results can provide a basis for the subsurface damage control and parameter optimization of grinding of large-sized silicon wafers.Keywords：grinding;Silicon wafers;surface roughness;subsurface damage depth- 276
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Published：2022-10-08 -
Continuous cubic phase plate with long depth of focus fabricated by femtosecond laser and its optical performance
Abstract：Traditional methods for generating beams with a long depth of focus， such as spatial light modulator and a combination of positive and negative lens， have several limitations， including a large size of optical elements， difficult integration with micro-nano optics and others. This makes the application of optical devices in aviation areas challenging. Thus， it is necessary to develop a method for generating beams with a long depth of focus beam， and fabricating an optical element with a simple structure， compact size， and good imaging quality. In this paper， we achieve this by using continuous cubic phase plate modulation. A phase plate with high dimensional accuracy （108 μm in diameter， 1.1 μm in height） and adjustable focal length （50-150 μm） and focal depth range （100-600 μm） is fabricated via femtosecond laser direct writing technology. The optical test of the phase plate demonstrates its long depth of focus and imaging ability. This method indicates new avenues in high performance integrated optics， micro/nano-optics manufacturing， and laser micro/nano manufacturing for aeronautical science and technology.Keywords：femtosecond laser fabrication;long depth of focus beam;phase plate;optical simulation;optical testing- 178
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Published：2022-10-08 -
Mini-piezo-element drive microactuator based on triangular amplification
Abstract：This paper proposes a mini-piezo-element drive microactuator based on triangular amplification. The actuator is composed of two 1.6 mm×1.6 mm×5.0 mm mini piezo-elements， two triangular waists serving as symmetrical stretch arms， and one large apex flexure hinge （scanning end）. When both stretch arms are driven by the mini-piezo-elements an amplified output displacement on the scanning end can be obtained through triangular amplification. Theoretical analysis and finite element simulation show that when the angle between each triangular stretch arm and the bottom edge is 6°， the ratio of the displacement at the scanning end to the expansion at the mini-piezo-element’s approaches 9∶1. Furthermore， the simulation results show that compared with the displacement of the mini-piezo-element of 3.2 μm， under 80 V driving voltage， the displacement of the scanning end can be enlarged to 29.5 μm. Micro-motion measurement experiments were conducted under the same driving voltage， and the displacement of the scanning end was measured as 26.6 μm， with an actual amplification ratio of 8.3. The proposed mini-piezo-element drive microactuator was successfully employed as the slow axis scanner of an atomic force microscope （AFM） and for wide-range AFM imaging （4 μm×26 μm）. In conclusion， the proposed microactuator is a novel， simple structure that yields good results at low cost and is expected to be widely applied in optics， precision machinery， and micro/nano technology.Keywords：microactuator;triangular amplification;mini-piezo-element;Output displacement;atomic force microscope- 171
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Published：2022-10-08 -
Measuring methods and instruments of high precision gear helix artifact
Abstract：Gear helix deviation is an important index for evaluating gear accuracy class and bearing uniformity， and it is one of the four mandatory inspection items specified in the national standard for gear GB/T 10095.1-2008. The datum for tracing the source of gear helix deviation and value transmission is the gear helix artifact. To realize high-precision helix deviation measurement， studies have considered the perspectives of high-precision helix artifact structure， manufacturing， and development of high-precision special measuring instrument， which have improved the measurement accuracy of helix deviation to a certain extent. However， the structural parameters of existing helix artifact and the measurement uncertainty of existing special measuring instruments do not meet the specification and measurement requirements of a Class-1 gear helix artifact in China， respectively. This paper summarizes two conventional measurement methods and instruments of helix deviation， followed by a summary of the design and structural characteristics of gear helix artifacts and the research results of special measuring instruments for high-precision gear helix deviation measurement. A pure rolling measurement method and instrument of a gear helix artifact with an equal common normal is proposed in line with the Chinese national standard.Keywords：precision measurement;measurement uncertainty;helix deviation;gear helix artifact;pure-rolling generating motion- 151
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Published：2022-10-08
Micro/Nano Technology and Fine Mechanics
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Neural architecture search algorithm based on voting scheme
Abstract：A neural-architecture search algorithm based on a voting scheme was proposed to address the difference between network architectures that are automatically searched by existing algorithms and those that were evaluated by the algorithm. First， to solve the problem that uniform sampling ignores the importance of each network architecture， the training losses tested on small batch training data were used as performance estimators to sample candidate networks， thus concentrating computing resources on high-performance candidate network architectures. Second， a group sparsity regularization strategy was adopted to rank all candidate operations to solve the problem of selecting candidate operations in each node. This strategy could screen suitable candidate operations and further enhance the precision of path selection in the cell structure. Finally， by integrating the differentiable architecture search， noise and sparse regularization strategies， the optimal cell structure was selected using a weighted voting scheme， and the network architecture for 3D model recognition and classification was constructed. Experimental results indicate that the classification accuracy of the constructed network for 3D models reaches 93.9% on the ModelNet40 dataset， which is higher than that of current mainstream algorithms. The proposed algorithm effectively narrows the gap between the network architecture during the search and evaluation phases， thereby resolving the problem of inefficient network training caused by uniform sampling in previous neural-architecture search methods.Keywords：neural architecture search;weighted voting scheme;3D model classification;performance estimator;group sparsity regularization- 103
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Published：2022-10-08 -
Underwater image enhancement based on color balance and multi-scale fusion
Abstract：This study proposes an underwater enhancement algorithm based on color balance and multi-scale fusion to address the color deviation， detail blur， and low contrast of underwater images caused by water absorbing and scattered light. A color balance method was used to correct color. Then， the color-corrected image was converted from the RGB space to Lab space， and the L-channel was processed with the contrast limited adaptive histogram equalization method to enhance the contrast. Subsequently， the image was converted back to the RGB space. Finally， the multi-scale fusion method was used to fuse the color-corrected image with the contrast-enhanced image according to weight maps. After image enhancement， the enhancement effect of the proposed algorithm was compared with that of other algorithms in terms of visual effect and image quality evaluations. Experiments show that the proposed algorithm can remove color deviation of an underwater image， as well as improve its clarity and contrast. Compared with the original image， the entropy， UIQM， and UCIQE of the processed image increase by at least 5.2%， 1.25 times， and 30.8%， respectively， thereby proving that the proposed algorithm can effectively improve the visual quality of underwater images.Keywords：image enhancement;color balance;multi-scale fusion;Adaptive histogram equalization- 192
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Published：2022-10-08 -
Automatic classification of retinopathy with attention ConvNeXt
Abstract：Due to the small differences in image features between classes and the relative fuzzy classification threshold of retinopathy， automatic classification algorithms are challenged by problems related to low recognition and classification accuracy. This paper proposes an automatic classification model for retinopathy based on an improved ConvNeXt network. Aiming at solving the problem of insufficient data in the data set， the horizontal flip left and right transformation method is used to expand the data， and related data sets are introduced to balance data distribution. To solve problems related to image blurring and uneven illumination in the fundus image， the Graham method was used to predict the image. The characteristics of the lesions are also highlighted. In this paper， an attention-fused ConvNeXt network was proposed to assist doctors in diagnosing retinopathy， an efficient channel attention mechanism was introduced， and an E-Block module was designed to channel interaction information while avoiding dimensionality reduction. The transfer learning method was used to train all layer parameters of the network， and the dropout method was added to avoid the overfitting problem caused by the strong learning ability of the ConvNeXt network. The results show that the sensitivity， specificity， and accuracy of the proposed model are 95.20%， 98.80%， and 95.21%， respectively. Compared with the ConvNeXt and other networks， the performance indexes of this network model for automatic classification of retinopathy.Keywords：retinopathy recognition and grading;transfer learning;ConvNeXt network;Efficient Channel Attention（ECA）;E-Block- 171
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Published：2022-10-08 -
Super-resolution reconstruction method for space target images based on dense residual block-based GAN
Abstract：To obtain the optical images of space targets with higher resolution and clarity， it is necessary to perform super-resolution reconstruction on the degraded images corrected by ground-based adaptive optics （AO） imaging telescopes. The image super-resolution reconstruction method based on deep learning has a fast operation speed and provides rich high-frequency detail information of the image； it has been widely used in natural， medical， and remote sensing images， among other applications. Aiming at the characteristics of spatial target AO images with a single background， limited resolution， motion blur， turbulent blur， and overexposure， this study proposes using a deep learning-based generative adversarial network （GAN） method to realize the super-resolution of spatial target AO images. For resolution reconstruction， a training set of spatial target AO simulation images is first constructed for neural network training， and a GAN super-resolution reconstruction method based on dense residual blocks is then proposed. By changing the traditional residual network to dense residual blocks， improving the network depth， and introducing a relative average loss function into the discriminator network， the discriminator becomes more robust， and the training of the generative adversarial network becomes more stable. Experiments show that the proposed method improves the peak-to-noise ratio （PSNR） and structural similarity index measure （SSIM） by more than 11.6% and 10.3%， respectively， compared with traditional interpolation super-resolution methods. In addition， it improves the PSNR and SSIM by 6.5% and 4.9% on average， respectively， compared with the deep learning-based blind image super-resolution method. The proposed method effectively realizes the clear reconstruction of a spatial target AO image， reduces the artifacts of the reconstructed image， enriches image details， and achieves a better reconstruction effect.Keywords：space object images;super resolution;Generative Adversarial Network（GAN）;dense residual blocks- 217
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Published：2022-10-08
Information Sciences
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