Dark-field Imaging System Research Articles

Dark-field surface defects detection method for multi-surface-shape large aperture optical components

In large-scale high-power optical systems such as inertial confinement fusion systems, the design of various optical components is often larger and more complex. Therefore, determining how to ensure the quality evaluation of optical components faces new challenges. As a key evaluation step for component quality, surface defects detection needs to consider improving the detection capability for various complex surface shapes and large aperture components. Meanwhile, the accuracy level of detection does not decrease with an increase in detection aperture size. The defects that need to be detected are typically small in size and randomly distributed throughout the aperture. Comprehensive aperture-wide information is required to ensure the thorough detection of defects in the components. Therefore, it is required that the detection system maintains compatibility with multi-surface shapes while balancing detection efficiency and accuracy. Against this background, the surface defects detection technology with high compatibility is explored in this paper. The illumination system of the dark-field imaging system and a generalized scanning path search strategy is proposed. Under the condition of ensuring a detection sensitivity of 0.5 µm, surface defects detection for various types of optical components with apertures several hundred times larger than the detection field of view is achieved.

Automatic identification and classification of surface defects in small-sized optics

Abstract Precision optics are widely used in lighting systems, imaging systems, and high-precision inspection equipment. The presence of surface defects in optics can seriously affect the design performance of the equipment, so the optics must be rigorously inspected to eliminate defective optics. Currently, engineering products are moving toward smaller sizes, resulting in smaller optics in them also moving towards smaller sizes. However, realizing the surface defect detection of small-sized optics is a great challenge because it requires micrometer-scale high-resolution image acquisition and automatic detection of defects on their surfaces. A machine learning-based automatic surface defect detection method is proposed for surface defects with many types and small sizes. A dark-field micro-scattering imaging system is used to acquire dark-field images. Then the image segmentation and feature analysis are performed on each image to extract the original feature data, and the original feature data are compressed by reliefF algorithm. Based on the compressed feature data, a classification model is built using a support vector machine. The detection method can classify three kinds of defects, namely, dig, scratch, and scuff mark. The accuracy of the method is up to 98%. The experimental results of this study show that the method can automatically and accurately detect a wide range of micro defects occurring in small-sized optics, thus providing valuable insights into the realization of mass production of small-sized optics. In addition, our proposed method provides some ideas for further research in the field of defect detection in small-sized optics.

Accurate nanoparticle size measurement by combining photometry and tracking analysis

Accurate determination of nanoparticle size is paramount in various fields, including molecular imaging, food safety, drug delivery, and nanobiotechnology. Conventional methods face limitations in accurately resolving different size distributions. Here, we introduce an approach to measure size distribution by combining photometry and tracking analysis of single particles. Our approach is based on a plasmonic dark field imaging system to image and track individual nanoparticles. Scattering intensity, spot size, and diffusion coefficient are quantified and fed into the machine learning model to establish the relationship with particle sizes. Compared with conventional methods, our approach shows improved accuracy in size measurement. To verify the universality of our method, we further demonstrate the sizing capability at diverse and complex conditions, enabling us to discriminate particle size differences within 20 nm in polydisperse systems.

Elucidation of the visualization process and its influencing factors for the preparation of microcircuits based on arc-beam laser-induced forward transfer

Arc beam laser-induced forward transfer (AB-LIFT) based on an arc beam is a promising micro additive manufacturing process to efficiently prepare high-performance metal microcircuits by transferring metal microdroplets. Therefore, elucidating the transfer mechanism of arc-beam-induced microdroplets is crucial to controlling the printing process. In this paper, a dark-field imaging system is developed to visually analyze the transfer process of metal microdroplets, and the injection mechanism of Gaussian and arc-beam laser-induced forward transfer is analyzed. Meanwhile, by studying the deposition behavior of laser-induced forward transfer micro drops with an arc beam at a single pulse energy, it is concluded that the factors affecting the laser-induced forward transfer with an arc beam mainly include the center angle and diameter of the arc beam. Then the effects of the center angle and diameter of the arc beam and the laser parameters on the preparation of microcircuits by AB-LIFT are systematically investigated, and a set of process methods for the effective preparation of high-performance microcircuits is proposed.

Effects of primary aberrations on a dark-field microscopy imaging system with the sharper focusing lens

The effects of primary aberrations of the sharper focusing lens on the image quality is analyzed for a dark-field microscopy imaging system, which is illuminated by a left-circularly (LC) polarized Laguerre–Gaussian (LG) vortex beam. To confirm the aberration tolerance range of the sharper focusing lens for precise measurement, the analytical expression of the field distribution on the image plane is deduced. Then the dark-field image results corresponding to a defect with a scale of 6µm×6µm are simulated. The influence of the spherical error, coma, and astigmatism on the imaging quality is compared in detail. Then the verification test has been carried out to show the effects of different primary aberrations. Finally, both the simulation and experiment results help us to identify that the tolerance range of spherical aberration, astigmatism, and coma should be less than 1.5λ, 1λ, and 1.25λ, respectively.

Deep focus-extended darkfield imaging for in situ observation of marine plankton

Darkfield imaging can achieve in situ observation of marine plankton with unique advantages of high-resolution, high-contrast and colorful imaging for plankton species identification, size measurement and abundance estimation. However, existing underwater darkfield imagers have very shallow depth-of-field, leading to inefficient seawater sampling for plankton observation. We develop a data-driven method that can algorithmically refocus planktonic objects in their defocused darkfield images, equivalently achieving focus-extension for their acquisition imagers. We devise a set of dual-channel imaging apparatus to quickly capture paired images of live plankton with different defocus degrees in seawater samples, simulating the settings as in in situ darkfield plankton imaging. Through a series of registration and preprocessing operations on the raw image pairs, a dataset consisting of 55 000 pairs of defocused-focused plankter images have been constructed with an accurate defocus distance label for each defocused image. We use the dataset to train an end-to-end deep convolution neural network named IsPlanktonFE, and testify its focus-extension performance through extensive experiments. The experimental results show that IsPlanktonFE has extended the depth-of-field of a 0.5× darkfield imaging system to ~7 times of its original value. Moreover, the model has exhibited good content and instrument generalizability, and considerable accuracy improvement for a pre-trained ResNet-18 network to classify defocused plankton images. This focus-extension technology is expected to greatly enhance the sampling throughput and efficiency for the future in situ marine plankton observation systems, and promote the wide applications of darkfield plankton imaging instruments in marine ecology research and aquatic environment monitoring programs.

Efficient and precise detection for surface flaws on large-aperture optics based on machine vision and machine learning

Large-aperture optical components have a wide application in high-power laser facilities. Surface flaws such as damages and contaminants generated under high-power laser irradiation can greatly affect the optical or mechanical performance of optics, so the optical components must be carefully inspected to evaluate optics damage and cleanliness. However, it is of great challenge to achieve rapid and accurate positioning, classification, and measurement of micron-level flaws on the surface of large-aperture optics. This paper proposes a novel surface flaw detection technology based on machine vision and machine learning. To balance efficiency and accuracy, a dark-field imaging system based on progressive scanning is designed to obtain the image of optics surface. A set of surface flaw detection algorithms based on machine learning including object segmentation, flaw feature extraction, and classification and size calibration of flaw are proposed to accurately assess the state of surface flaws. The experiments show that the system can complete the detection of an optical component (430 mm × 430 mm) within 6 min. The minimum detectable size of the flaws can reach 20 μm, and the position accuracy of the flaws is better than 50 μm. The classification accuracy of flaw is 98.59 %, and the average relative errors of the size measurement of damages and contaminants are 3.23 % and 6.58 %, respectively. The experimental results demonstrate the effectiveness of the method in detecting surface flaws of large-aperture optics.

Elucidating ejection regimes of metal microdroplets in voxel-based laser-induced forward transfer

Voxel-based laser-induced forward transfer (VB-LIFT) is a promising micro additive manufacturing process to efficiently print microstructures by ejecting a metal microdroplet array. Elucidating the ejection regimes of microdroplets is crucial to manipulating the printing process. This study investigates the ejecting behaviors of microdroplets induced by irradiating a femtoliter metal voxel with an ultraviolet nanosecond laser. A time-resolved dark-field imaging system was developed to capture the ejecting microdroplets. The ejecting velocity increased linearly with the laser fluence but was independent of the voxel side length. The analysis of the laser energy conversion revealed that one-thousandth of the laser energy was converted to the kinetic energy of the ejecting microdroplets, and the majority of the remaining were converted into internal energy. Meanwhile, the relaxation of the thermal compressive stress induced by laser heating drove the metal voxel detaching from the donor glass to generate a microdroplet. Furtherly, the ejection map in the VB-LIFT is proposed, including the non-detaching, detaching without melting, detaching and melting, and broken regimes. This study provides insights to eject debris-free metal microdroplets with the desired temperature and velocity in laser-induced forward transfer. • We develop a dark-field imaging system to capture the ejecting metal microdroplets. • Ejecting velocity is proportional to laser fluence but independent of voxel length. • Metal voxel detaching is driven by thermal compressive stress due to laser heating. • An ejection map containing four regimes is proposed for the VB-LIFT process.

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.

Gold triangular nanoplates with edge effect for reaction monitoring under dark-field microscopy

True morphology imaging of individual nanoprobes with high sensitivity and in situ dynamic response is crucial in the dark-field microscopy imaging analysis. However, the geometry and orientation of the probes are experimentally challenging to visualize as their physical dimensions are usually below the diffraction limit. In this work, two-dimensional triangular gold nanoplates (AuNPLs) with the edge length > 600 nm and the thickness ~25 nm displayed clear triangular-shaped far-field scattering patterns (TFSPs) under a dark-field imaging system owing to the strong electrical field strength around the sharp edges and tips. Thus, their actual shape and orientation specific scattering image are directly visualized only by dark-field microscopy and furtherly applied to real-time monitor the etching and alloying progress of AuNPLs. The results showed that the reactive sites of AuNPLs are located at their vertices and sharp edges owing to the sensitive edge effect. This work provides a new perspective to directly monitor the conformation and dynamics of chemical reaction at the single particle level.

Influence of light polarization state on the imaging quality of dark-field imaging system

Annular linear polarized light is used as the illumination source of a reflective dark-field detection system in this paper. According to the theories of the bidirectional reflectance distribution function and multi-beam interference, the influence of the light polarization state on the intensity distribution of the scattered light is analyzed in detail. For surface defects, a simulation model of dark-field imaging is established based on the finite-difference time-domain method. Both the near-field and the far-field scattering intensity distributions caused by surface defects are calculated under different illumination conditions. The incidence angle and polarization state of the illuminated light are optimized. Simulation and experimental results show that the image quality will be minimally affected by the interference effect, while P-polarized light illuminates with an incident angle of 45°. The higher measurement accuracy of the dark-field imaging detection system can be obtained when the optimized illumination scheme is used.

Phase and dark-field imaging with mesh-based structured illumination and polycapillary optics.

X-ray phase and dark-field (DF) imaging have been shown to improve the diagnostic capabilities of X-ray systems. However, these methods have found limited clinical use due to the need for multiple precision gratings with limited field of view or requirements on X-ray coherence that may not be easily translated to clinical practice. This work aims to develop a practicable X-ray phase and DF imaging system that could be translated and practiced in the clinic. This work employs a conventional source to create structured illumination with a simple wire mesh. A mesh-shifting algorithm is used to allow wider Fourier windowing to enhance resolution. Deconvolution of the source spot width and camera resolution improves accuracy. Polycapillary optics are employed to enhance coherence. The effects of incorporating optics with two different focal lengths are compared. Information apparent in enhanced absorption images, phase images, and DF images of fat embedded phantoms were compared and subjected to a limited receiver operator characteristic (ROC) study. The DF images of the moist and dry porous object (sponges) were compared. The mesh-based phase and DF imaging system constructs images with three different information types: scatter-free absorption images, differential phase images, and scatter magnitude/DF images, simultaneously from the same original image. The polycapillary optic enhances the coherence of the beam. The deblurring technique corrects the phase signal error due to geometrical blur and the limitation of the camera modulation transfer function (MTF) and removes image artifacts to improve the resolution in a single shot. The mesh-shifting method allows the use of a wider Fourier processing window, which gives even higher resolution, at the expense of an increased dose. The limited ROC study confirms the efficacy of the system over the conventional system. DF images of moist and dry porous object show the significance of the system in the imaging of lung infections. The mesh-based X-ray phase and DF imaging system is an inexpensive and easy setup in terms of alignment and data acquisition and can produce phase and DF images in a single shot with wide field of view. The system shows significant potential for use in diagnostic imaging in a clinical setting.

Illumination characteristics of vortex beams in dark-field microscopic systems.

A Laguerre-Gaussian (LG) vortex beam is employed as an illumination source for a dark-field microscopy imaging system. To discover the influences of beam characteristics on the imaging quality, an analysis model has been established to show the light-field change rule on both object and image planes. The analytic expressions of the light field on the two planes are deduced. When a rectangular defect is simulated, the light distributions on the object and image planes with different parameters are calculated. The results show that the size of the beam spot on the object plane can be changed by adjusting the topological charge of the vortex beam to obtain the best imaging effect for defects of different scales.

Optical Element Surface Defect Size Recognition Based on Decision Regression Tree

Defect size recognition is significant to the evaluation of optical element surface quality. Currently, it’s mainly achieved by the conventional image process, such as threshold segmentation. However, as the defect size gradually approaches the diffraction limit of the imaging system, the defect gray distribution changes from bimodal to unimodal, which makes it difficult to be accurately recognized. In this paper, an electromagnetic simulation model of the microscopic scattering dark-field imaging (MSDI) system is built based on the finite-difference time-domain (FDTD) method to research the defect imaging mechanism. The point spread function (PSF) of our MSDI system is measured to revise the far-field simulation light intensity distribution, and the mean value of the distance between three groups of feature points, whose intensity is 0.75, 0.5, and 0.25 of the light intensity distribution peak value, is taken as the feature parameter of the light intensity distribution. To obtain the defect size, the decision regression tree (DRT) is proposed to get the relationship between the feature parameter and the defect size. Besides, some scratches samples are made to verify the validity of the DRT. The results show the relative error of DRT is within 10%, which is better than the threshold segmentation.

Surface Defect Detection for Mobile Phone Back Glass Based on Symmetric Convolutional Neural Network Deep Learning

Defect detection based on machine vision and machine learning techniques has drawn much attention in recent years. Deep learning is very suitable for such segmentation and detection tasks and has become a promising research area. Surface quality inspection is essentially important in the manufacturing of mobile phone back glass (MPBG). Different types of defects are produced because of the imperfection of the manufacturing technique. Unlike general transparent glass, screen printing glass has totally different reflection and scattering characteristics, which means the traditional dark-field imaging system is not suitable for this task. Meanwhile, the imaging system requires high resolution since the minimum defect size can be 0.005 mm2. According to the imaging characteristics of screen printing glass, this paper proposes a coaxial bright-field (CBF) imaging system and low-angle bright-field (LABF) imaging system, and 8K line-scan complementary metal oxide semiconductor(CMOS) cameras are utilized to capture images with the resolution size of 16,000*8092. The CBF system is applied for the weak-scratch and discoloration defects while the LABF system is applied for the dent defect. A symmetric convolutional neural network composed of encoder and decoder structures is proposed based on U-net, which produces a semantic segmentation with the same size as the original input image. More than 10,000 original images were captured, and more than 30,000 defective and non-defective images were manually annotated in the glass surface defect dataset (GSDD). Verified by the experiments, the results showed that the average precision reaches more than 91% and the average recall rate reaches more than 95%. The method is very suitable for the surface defect inspection of screen printing mobile phone back glass.

A New Stitching Method for Dark-Field Surface Defects Inspection Based on Simplified Target-Tracking and Path Correction.

A camera-based dark-field imaging system can effectively detect defects of microns on large optics by scanning and stitching sub-apertures with a small field of view. However, conventional stitching methods encounter problems of mismatches and location deviations, since few defects exist on the tested fine surface. In this paper, a highly efficient stitching method is proposed, based on a simplified target-tracking and adaptive scanning path correction. By increasing the number of sub-apertures and switching to camera perspective, the defects can be regarded as moving targets. A target-tracking procedure is firstly performed to obtain the marked targets. Then, the scanning path is corrected by minimizing the sum of deviations. The final stitching results are updated by re-using the target-tracking method. An experiment was carried out on an inspection of our specially designed testing sample. Subsequently, 118 defects were identified out of 120 truly existing defects, without stitching mismatches. The experiment results show that this method can help to reduce mismatches and location deviations of defects, and it was also effective in increasing the detectability for weak defects.

Classification between digs and dust particles on optical surfaces with acquisition and analysis of polarization characteristics.

In the automatic detection for surface defects of optical components, the digs and dust particles exhibit similar features: point-like shape and variable intensity reflectivity. On this condition, these two types with entirely different damages are easily confused so that misjudgments will be induced. To solve this problem, a polarization-characteristics-based classification method of digs and dust particles (PCCDD) is proposed based on the polarimetric imaging technique and dark-field imaging technique. First, a dark-field imaging system equipped with a polarization state generator (PSG) and a polarization state analyzer (PSA) is employed to measure and establish normalized Mueller matrices' datasets of digs and dust particles. And by a nonlinear global search combined with a separability evaluation method, the optimal number of acquisitions and corresponding polarization measurement states of the PSG and the PSA are obtained, as well as the parameters of classification function. Then, multiple polarization images are acquired under the optimal states to extract a multidimensional feature description that relates only to the polarization characteristics of the defect; this subsequently acts as the input vector of the classifier to finally achieve the classification. This method takes full advantage of both the difference in polarization properties between digs and dust particles and the characteristic that the polarization properties of digs are relatively invariant while those of dust particles have a large variability. The classification process involves only simple matrix operations. Compared to the traditional discrimination method based on intensity images, the features obtained by this method have a higher separability. Experiments show that the classification accuracy reaches over 90%. This method can be further applied to the recognition and discrimination of other defects in the field of surface defects' detection.

Super-contrast-enhanced darkfield imaging of nano objects through null ellipsometry.

We rediscover the null ellipsometry principle for an outstanding image-contrast enhancement method for darkfield imaging. Simply by adding polarizers, compensators, and a photodiode sensor to a conventional darkfield imaging system and applying the null principle, Si nano-cylinder structures as small as D20nm (H20nm) on non-patterned wafer, and gap defects as small as 14.6nm and bridge defects as small as 21.9nm on 40nm line and 40nm space patterns (H40nm), which are invisible in conventional darkfield imaging, can be distinguished from scattered noise. To the best of our knowledge, no method has been successful for identifying such small non-metal (silicon) nanoscale objects with such low magnification (×20) optics.

A Microfluidic Chip and Dark-Field Imaging System for Size Measurement of Metal Wear Particles in Oil

We present a dark-field video microscopy setup and microfluidic sample cell to detect suspended particles and measure their size. The microfluidic chip was fabricated by etching of shallow chambers in silicon and bonding with glass, thus achieving robust devices with low background signal for dark-field microscopy. The system is suitable for measuring particles in liquid media, such as metallic wear particles originating from lubricated tribocontacts in oil. Here, sample wear particles were generated in the laboratory by sliding a piston ring section against a cylinder liner section with a reciprocating tribometer using base oil (PAO8) as lubricant. Individual microparticles and nanoparticles are visualized by means of their scattered light, and sizes are determined by tracking of the diffusive Brownian motion in liquid. By heating the oil sample, the viscosity is reduced, which increases the extent of Brownian motion and facilitates tracking-based size calculations. The height of the microfluidic chamber was matched with the focal depth of the optical system, so that particles stay in focus during the whole measurement time. The resulting particle size distributions were monomodal and displayed a peak diameter of 230 nm, as confirmed by reference measurements with dynamic light scattering. Our approach represents a straightforward way to determine the size of microparticles and nanoparticles and has the potential for a continuous online operation. Compared with the state-of-the-art particle counters used in condition monitoring of industrial machinery, it is possible to detect much smaller particles and, therefore, allow early detection of wear before severe failure events take place.

A Novel and Effective Surface Flaw Inspection Instrument for Large-Aperture Optical Elements

Surface defects on precision optical elements must be carefully inspected since they impact the normal operation of an optical system. It is a challenge to inspect defects of large-aperture optical elements using an imaging system because of efficiency and accuracy. This paper designs a novel and effective inspection instrument with two imaging systems for large-aperture optical elements. They are the dark-field imaging system (DFIS) with a line scan camera in 10-μm resolution and the bright-field (BF) imaging system with a microscopic camera in a 0.85-μm resolution. To keep the clarity of the DFIS in large-scope quickly scanning, an adaptive scan path planning method is proposed. A set of novel algorithms is proposed to process a large number of dark-field images. Due to the limitations of the DFIS in scattering effect, the corresponding BF images are obtained according to the dark-field images. The classification of flaws and their sizes measurement based on BF images are also presented. The experiments show that the instrument can scan an optical element with the size of 810 mm × 460 mm in less than 6 min and the inspection precision can reach 3 μm.