Optical Surface Defects Research Articles

Wavelength optimization of fine optical surface defect detection based on FDTD

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.

Composite scattering characteristics analysis of micro-ellipsoidal periodic structure optical surface and microdefects

In order to adjust and detect micro-nano periodic structure optical surface accurately and efficiently, the problem of composite scattering between micro-ellipsoidal periodic structure optical surface and pore defects is studied use the multi-resolution time domain (MRTD) approach. A calculation model is established for the intensity distribution of composite scattering, which is modulated by the micro-ellipsoidal periodic structure optical surface and microdefects. Results are in good agreement with those obtained using CST Microwave Studio software and the finite-different time-domain (FDTD) approach, which demonstrates the effectiveness of the calculation model and method. By combining the field distribution of the micro-ellipsoidal periodic structure optical surface containing microdefects with the optical response at different wavelengths, it is necessary to study the influence of various parameters of the micro-ellipsoidal structure and microdefects on the optical system of metamaterials. The effects of the parameters such as roughness, structure of micro-ellipsoidal unit, defect sizes and buried depths on the composite scattering characteristics are analyzed numerically. The results provide technical support for the fields of functional surface design, ultrasensitive detection, scattering peak orientation and frequency selection.

A 3D curved optical surface defect detection method with micro structured-light based on Multi-Parameter calibration

To solve the difficulty of 3D curved surface defect detection, a 3D curved optical surface defect detection method based on micro structured-light detection (MSLD) with large field of view (FOV) is proposed to detect typical defects such as scratches and digs. And a multi-parameter calibration method for MSLD is proposed, which does not have strict requirements on the placement of the camera and the screen and can effectively correct the distortion. Simulations verify the feasibility of the proposed method. 3D detection of different types of weak defects on convex and concave surfaces is also performed. The experimental results show that the reprojection error after calibration is less than sub-pixel, and also show that MSLD can achieve comparable accuracy to white light interferometry (WLI). The difference in measured defect depth between MSLD and WLI is less than 0.5 nm, and the difference in measured defect width is less than 1 μm.

Optical Surface Defect Stitching Method Based on Sub-Aperture Feature Dataset

Optical Surface Defect Stitching Method Based on Sub-Aperture Feature Dataset

Efficient surface defect identification for optical components via multi-scale mixed Kernels and structural re-parameterization.

Surface defect identification plays a vital role in defective component rapid screening tasks in optics-related industries. However, the weakness and complexity of optical surface defects pose considerable challenges to their effective identification. To this end, a deep network based on multi-scale mixed kernels and structural re-parameterization is proposed to identify four manufacturing and two non-manufacturing optical surface defects. First, we design a multi-size mixed convolutional kernel with multiple receptive fields to extract rich shallow features for characterizing the defects with varying scales and irregular shapes. Then, we design an asymmetric mixed kernel integrating square, horizontal, vertical, and point convolutions to capture rotationally robust middle-and-deep features. Moreover, a structural re-parameterization strategy is introduced to equivalently convert the multi-branch architecture in the training phase into a deploy-friendly single-branch architecture in the inference phase, so that the model can obtain higher inference speed without losing any performance. Experiments on an optical surface defect dataset demonstrate that the proposed method is efficient and effective. It achieves a remarkable accuracy of 97.39% and an ultra-fast inference speed of 201.76 frames/second with only 5.23M parameters. Such a favorable accuracy-speed trade-off is capable of meeting the requirements of real-world optical surface defect identification applications.

A visual defect detection for optics lens based on the YOLOv5 -C3CA-SPPF network model.

Defects in the optical lens directly affect the scattering properties of the optical lens and decrease the performance of the optical element. Although machine vision instead of manual detection has been widely valued, the feature fusion technique of series operation and edge detection cannot recognize low-contrast and multi-scale targets in the lens. To address these challenges, in this study, an improved YOLOv5-C3CA-SPPF network model is proposed to detect defects on the surface and inside of the lens. The hybrid module combining the coordinate attention and CSPNet (C3) is incorporated into YOLOv5-C3CA for improving the extraction of target feature information and detection accuracy. Furthermore, an SPPF features fusion module is inserted into the neck of the network model to improve the detection accuracy of the network. To enhance the performance of supervised learning algorithms, a dataset containing a total of 3800 images is created, more than 600 images for each type of defect samples. The outcome of the experiment manifests that the mean average precision (mAP) of the YOLOv5-C3CA-SPPF algorithm is 97.1%, and the detection speed FPS is 41 f/s. Contrast to the traditional lens surface defects detection algorithms, YOLOv5-C3CA-SPPF can detect the types of optical lens surface and inside defects more accurately and quickly, the experimental results show that the YOLOv5-C3CA-SPPF model for identifying optical lens defects has good generalizability and robustness, which is favorable for on-line quality automatic detection of optical lens defects and provide an important guarantee for the quality consistency of finished products.

Image classification of optical element surface defects based on convolutional neural network

光学元件的表面疵病，即表面缺陷，其形状的大小会直接影响光学系统的性能，在对表面缺陷进行分类时，所面对的很多表面缺陷的形状都是不规则的，依靠普通的模式识别技术，分类很难达到预期的效果。为解决精密光学元件表面缺陷分类方法中精度低、耗时长的问题，提出了基于卷积神经网络的精密光学元件表面缺陷分类方法。采用散射法获取表面缺陷图像,分析其成像特点，通过对图像进行旋转，镜像扩增了数据集，加强了网络的训练能力。使用AC训练网络模型，在不增加额外计算量的同时加强了网络的特征获取力。通过Softmax分类器，将精密光学元件表面缺陷分为划痕、麻点及噪点3类。实验结果表明，所使用的模型对缺陷分类精度超过99.05%。

Real-time batch inspection system for surface defects on circular optical filters.

Optical filters, one of the essential parts of many optical instruments, are used to select a specific radiation band of optical devices. There are specifications for the surface quality of the optical filter in order to ensure the instrument's regular operation. The traditional machine learning techniques for examining the optical filter surface quality mentioned in the current studies primarily rely on the manual extraction of feature data, which restricts their ability to detect optical filter surfaces with multiple defects. In order to solve the problems of low detection efficiency and poor detection accuracy caused by defects too minor and too numerous types of defects, this paper proposes a real-time batch optical filter surface quality inspection method based on deep learning and image processing techniques. The first part proposes an optical filter surface defect detection and identification method for seven typical defects. A deep learning model is trained for defect detection and recognition by constructing a dataset. The second part uses image processing techniques to locate the accurate position of the defect, determine whether the defect is located within the effective aperture, and analyze the critical eigenvalue data of the defect. The experimental results show that the method improves productivity and product quality and reduces the manual workload by 90%. The proposed model and method also compare the results of surface defect detection with the actual measurement data in the field, verifying that the method has good recognition accuracy while improving efficiency.

Video-Based Two-Stage Network for Optical Glass Sub-Millimeter Defect Detection

Since tiny optical glass is the key component in various optical instruments, more and more researchers have paid attention to automatic defect detection on tiny optical glass in recent years. It remains a challenging problem, as the defects are extremely small. In this paper, we propose a video-based two-stage defect detection network to improve detection accuracy for small defects. Specifically, the detection process is carried out in a coarse-to-fine manner to improve the detection precision. First, the optical glass area is located on the down-sampled version of the input image, and then defects are detected only within the optical glass area with a higher resolution version, which can significantly reduce the false alarming rate. Since the defects may exist on any place of the optical glass, we fuse the results of multiple video frames captured from various perspectives to promote recall rates of the defects. Additionally, we propose an image quality evaluation module based on a clustering algorithm to select video frames with high quality for improving both detection recall and precision. We contribute a new dataset called OGD-DET for tiny-scale optical glass surface defect detection experiments. The datasets consist of 3415 images from 40 videos, and the size of the defect area ranges from 0.1 mm to 0.53 mm, 2 to 7 pixels on images with a resolution of 1536 × 1024 pixels. Extensive experiments show that the proposed method outperforms the state-of-the-art methods in terms of both accuracy and computation cost.

Error analysis and measurement methods of curved optical element surface defects dark-field imaging inspection system based on multi-axis kinematics

In surface defect dark-field inspection of the curved optical element, the camera field of view (FOV) is much smaller than the aperture of the element, so the images of different positions on the element surface, which are also called sub-aperture images, need to be collected and stitched to obtain the whole surface image of the element. It is usually achieved by controlling multi-axis motion guide rails to make relative motion between camera and optical element. During sub-aperture images collection, there is not only relative translation between camera and optical element, but also optical element spin and swing with its optical axis for curved optical element. The motion errors of each motion axis will make sub-aperture scanning path deviate its predetermined path. Hence, it will lead to the dislocation of the stitching image, and will cause defects break and dislocation in stitching image, which will largely affect the optical element surface quality quantitative evaluation. Since multi-axis kinematics (MAK) is a mathematical model which is often used to describe the motion of objects located in multi-axis system, such as numerical control machine and mechanical arm. The multi-axis kinematics error analysis (MAKEA) model is put forward in this paper and applied in the optical element surface defects dark-field imaging inspection system. The effect of all axis motion errors including optical axis motion errors to sub-aperture scanning path has been specifically simulated and analyzed. The simulation result indicates that the optical axis swing error caused by assembly is the main error of dark-field imaging system, whereupon a swing angle assembly error measurement (SAAEM) method by measuring the optical axis positions of different thickness spherical optical elements is proposed. By adjusting the motor of the element optical axis swing, the optical axis swing error can be compensated. The sub-aperture images stitching experiment is carried out to verify the validity of optical axis swing error compensation. Results show the situation of sub-aperture images stitching dislocation is improved. The root-mean-square-error (RMSE) of sub-aperture images scanning path after error compensation is nearly 1/20 of the RMSE before compensation.

Effect of change in a normative approach on the evaluation of surface scratches on high-precision optical surfaces.

The evaluation of optical surface defects is an integral part of the realization of precise optical elements. In addition to the traditionally used visual inspection procedures based on MIL-O-13830A, procedures based on ISO standards, which underwent some changes in 2017, are coming to the forefront. One of these changes was the omission of equivalent radiometric sizes and their replacement by geometric dimensions. This paper focuses just on the analysis of the impact of this change on the values of specifications defining the requirements on the scratch and dig quality of optical elements. Among other things, this presents a possible way to correct specification classes based on the statistical processing of a set of parameters of imperfections detected on optical element surfaces, which have been realized in the TOPTEC Centre.

Current situation and development trend of aspheric optical surface defect detection technology (invited)

Current situation and development trend of aspheric optical surface defect detection technology (invited)

Development of a Reflective 193-nm DUV Microscope System for Defect Inspection of Large Optical Surfaces

We developed a 193-nm deep ultraviolet (DUV) microscope system based on the reflection mode for a precise inspection of various types of defects/cracks on large optical surfaces of the order of one meter in size. Without preprocessing the sample at room temperature and atmospheric pressure, which is commonly necessary for electron microscopy, the reflective 193-nm DUV microscope was used to directly observe optical surface defects in a manner similar to conventional optical microscopes. In addition, the limitations on the selection of materials and thickness of optical samples of transmittive DUV microscopes were overcome. DUV microscope imaging and the analysis on the spatial resolution were verified using a 1D grating structure with a 225-nm line width. This system could be widely applied as an inspection tool because it provides high resolution at the 200-nm scale that is close to the diffraction limit of a 193-nm DUV beam. In the near future, it is expected that our system would be extended to nano/bio imaging as well as the inspection of large optical surfaces.

Optical and Microstructural Origins of Thermomechanical Streaking Defects in Hot Extruded AA6060

Thermomechanical streaking is a common optical surface defect that affects architectural 6xxx series aluminum extrusions, and can be cause for rejection of the product. AA6060 profiles were extruded at a range of ram speeds using a specialized die with internal geometry purposely designed to produce thermomechanical variation throughout the profile. Subsequently, the extrudates underwent an industrial anodization pretreatment process that revealed the presence of streaks throughout three designated regions. The optical appearance, microstructure and surface topography of streaked and surrounding regions of the extrudate surface were analyzed using colorimetry, electron backscattered diffraction, and optical profilometry. Differences in perceived lightness, roughness, and grain size were observed between streaked and surrounding regions. Changes in appearance of the surface directly correlated with the surface roughness, with rougher surfaces yielding an increase in the perceived lightness. The surface roughness was determined to be primarily dependent on the size and distribution of grain etching steps as related to the surface grain size. A difference in grain size in the regions surrounding the streaks was determined to be the microstructural origin of the visual defect.

Research methods of IR-range DOE

A set of methods for studying diffractive optical elements (DOE) operating in the far infrared region and the parameters of the radiation formed by them has been proposed. The results of optical surface defects local controlfor DOEs with binary and semitone reliefobtained by non-contact methods have been presented. Approbation of the panoramic diagnostics methodfor determiningparameters of laser beam transformed by DOE has been carried out.

Vision system with high dynamic range for optical surface defect inspection.

We report a machine vision system with high dynamic range designed for optical surface defect inspection. The system consists of two motorized linear stages and one motorized rotation stage for automatically scanning the surface. As the intensity of long scratches and digs differ a lot under dark-field illumination, gains of red, green, and blue channels are set to be different values to extend the dynamic range of an ordinary colored detector in a single snapshot, which greatly improves the efficiency compared with multiexposure-based approaches. Image stitching is then employed to get a high-resolution image of the entire optical surface for further processing to quantitatively evaluate surface quality based on the standards of MIL-PRF-13830B and ISO 10110-7:2008. The system can be widely applied in the optical industry, as it provides a low-cost solution for optical surface quality checks.

Analysis of composite/difference field scattering properties between a slightly rough optical surface and multi-body defects

The effective extraction of optical surface roughness and defect characteristic provide important realistic values to improve optical system efficiency. Based on finite difference time domain/multi-resolution time domain (FDTD/MRTD) mixed approach, composite scattering between a slightly rough optical surface and multi-body defect particles with different positions is investigated. The scattering contribution of defect particles or the slightly rough optical surface is presented. Our study provides a theoretical and technological basis for the nondestructive examination and optical performance design of nanometer structures.

光学元件表面缺陷相对位置分布对近场光束质量的影响

光学元件表面缺陷相对位置分布对近场光束质量的影响

Detection Status of Surface Defect for Precise Optical Element

The optical surface defects have a significant impact on functions and reliability of the whole optical system. In recent years, with the great attention on research of large-scale high-power solid-state laser device at home and abroad, the detection of optical surface defect has also been highly valued. Through continuous exploration, defect detection technology has made some achievements, but there are still some urgent technical problems to be solved. In this paper, the technology situation of optical surface defect will be summarized in two aspects: the surface cleanliness and surface flaws. Moreover, the technical difficulties and the problems which are currently facing will be analysis.