1373 results about "Microscopy" patented technology

The invention belongs to the technical field of optical precision measurement, and particularly relates to a wafer defect detection system based on dual-channel optical imaging. According to the invention, dark field scattering and diffraction phase microscopy are combined as two optical imaging channels; optical paths of the two imaging modes are compatible and synchronized; through optical system calibration and optimization, high-precision acquisition and alignment of multi-modal images are realized; calibrating the sensitivity and resolution of the system by using a standard sample to ensure that the detection performance meets a preset requirement; carrying out dual-channel synchronous imaging on the wafer sample to be detected, and respectively obtaining a dark field scattering image and a diffraction phase image; carrying out conjoint analysis on the multi-modal data through an image algorithm, extracting size, depth and structure information of defects, and generating a high-precision defect detection report; the system specifically comprises a dark field scattering imaging module, a diffraction phase microscopic imaging module and an image processing unit. According to the invention, the defect of a single imaging mode can be effectively overcome, and the detection precision and robustness of the system are remarkably improved.

In a method, for training an artificial neural network (ANN) to reduce noise and / or artefacts in an electron microscopy image, a plurality of training image pairs is generated. For each pair, an undistorted synthetic specimen image and a distorted image are created by simulating additional noise and / or artefact features. The ANN is trained, in which the distorted images are used as input and the corresponding undistorted images as output. An adversarial training strategy is used in which the ANN is trained, as a generator network, in conjunction with concomitantly training a further ANN, as a discriminator network, to differentiate output produced by the generator network from synthetic images in the training set. In training, parameters of the ANN and further ANN are optimized using a generator loss function and a discriminator loss function, in which a dependency exists between said loss functions to train the networks in an adversarial manner.

A microscopy method includes a trained deep neural network that is executed by software using one or more processors of a computing device, the trained deep neural network trained with a training set of images comprising co-registered pairs of high-resolution microscopy images or image patches of a sample and their corresponding low-resolution microscopy images or image patches of the same sample. A microscopy input image of a sample to be imaged is input to the trained deep neural network which rapidly outputs an output image of the sample, the output image having improved one or more of spatial resolution, depth-of-field, signal-to-noise ratio, and / or image contrast.

The invention relates to the technical field of chip optical detection, and discloses a high-precision defect detection method and system for a semiconductor chip. Initial detection data are obtained through laser scattering and photo-thermal microscopic detection, key visual features are extracted and converted into a multi-dimensional visual feature matrix, and the multi-dimensional visual feature matrix is used for detecting the defects of the semiconductor chip; according to the method, a fuzzy sensing area is positioned and potential defects are identified by using a map clustering algorithm, adaptive analysis of a supplementary visual angle is carried out and supplementary detection data is generated, and finally defect identification is carried out through the supplementary data and popularity information to obtain a chip defect result. The method effectively improves the precision and reliability of defect detection, especially has obvious advantages in recognition of complex and fuzzy regions, and solves the problem of insufficient precision of single-path optical imaging detection in the prior art.

The invention provides a deep frequency shift super-resolution microscopic imaging chip and a deep frequency shift super-resolution microscopic imaging system for omni-directional ultra-high wave vector illumination, and belongs to the technical field of optical super-resolution microscopy. The chip comprises a substrate plated with an adhesion layer and multiple layers of films on the adhesion layer, and sub-wavelength-scale circular ring gratings are engraved on the substrate and the adhesion layer; exciting light enters the circular ring grating through the substrate, and evanescent waves of ultrahigh wave vectors in all directions are generated through the circular ring grating. The evanescent waves are coupled to the upper surface through the multi-layer thin film to excite surface plasmons with ultrahigh transverse wave vectors in the corresponding direction, and the surface plasmons are used for carrying out all-directional ultrahigh wave vector illumination and deep frequency shift super-resolution microscopic imaging on a sample placed on the upper surface of the multi-layer thin film. The system comprises an excitation module, a deep frequency shift super-resolution microscopic imaging chip for omni-directional ultra-high wave vector illumination, an imaging module and an excitation control and image acquisition module. According to the invention, omni-directional ultra-high resolution imaging of a complex fine structure sample can be realized, so that application requirements in related fields can be met.

A composite electrode material, method for its production and use of the material are provided. The composite electrode material comprises: i) a current collector material layer exhibiting a surface roughness value selected from at least one of the following: —an Sdr value of more than 40%; and —an Sdq value of more than 1.0; each value being determined by white light interferometry according to standard method ISO 25178; ii) optionally, a first silicon layer positioned on the current collector material layer, wherein the first silicon layer has a porosity of less than 30%, as determined by electron microscopy; and iii) at least a second silicon layer positioned on either the optional first silicon layer or the current collector material layer, wherein the second silicon layer has a porosity ranging from a porosity higher than the porosity of the optional first layer, to a porosity of less than 80%, as determined by electron microscopy.

The invention discloses a super-resolution laser direct writing and imaging method and device based on a two-photon and stimulated radiation loss effect, and the method comprises the steps: adding a photoinitiator into photoresist, and achieving two-photon STED high-precision micro-nano structure laser direct writing through the two-photon absorption effect and the stimulated radiation loss effect of the photoinitiator; and meanwhile, the two-photon STED super-resolution microscopic imaging is carried out on the inscribed structure by utilizing the fluorescence luminescence characteristic, the two-photon absorption characteristic and the stimulated depletion (STED) characteristic of the two-photon STED super-resolution microscopic imaging of the two-photon STED super-resolution microscopic imaging of the inscribed structure. According to the invention, the two-photon and STED effects of the initiator are utilized, so that super-resolution inscribing and imaging can be realized at the same time, and the components of the photoresist are simplified. Besides, the same exciting light and loss light can be respectively utilized during inscribing and imaging, so that high-precision inscribing and super-resolution imaging can be simultaneously realized in one optical system, and the complexity of the system can be effectively simplified by multiplexing the optical paths of the exciting light and the loss light for inscribing and imaging.

Systems, components, and methods for detecting characteristic signals are described. A detector includes a detector cell. The detector cell can be configured to generate an electrical signal in response to a particle incident on an active layer of the detector cell, The active layer can define an absorption surface. The detector can include a filter. The filter can include a membrane of carbon material. The filter can be disposed relative to the detector cell to shield the absorption surface from a subset of the incident particles. The subset of the incident particles can include electrons, ions, and photons. The photons can have an energy less than about 40 eV.

The invention discloses a differential polarization confocal microscopic imaging system and method of a multi-focus channel, and relates to the technical field of optical microscopy. Comprising a light source emission module, a dichroscope (6), a two-dimensional galvanometer module, a scanning module, a sample acquisition module, a detection module and an imaging terminal (17). According to the invention, differential polarization imaging and a multi-focus channel confocal technology are combined, so that the imaging quality of the confocal microscopic system is further improved on the premise of ensuring the confocal imaging speed; meanwhile, differential imaging is introduced, so that the imaging contrast ratio can be greatly improved, and the confocal microscopic system can obtain a better imaging effect in the application field of thick tissue microscopic imaging.

A composition of matter is provided comprising hierarchically ordered crystalline microporous material having well-defined long-range mesoporous ordering of lamellar symmetry. The composition possesses mesopores having walls of crystalline microporous material and a mass of mesostructure between mesopores of crystalline microporous material. Long-range ordering is defined by presence of secondary peaks in an X-ray diffraction (XRD) pattern and / or lamellar symmetry observable by microscopy.

The invention provides a microscopic automatic focusing method and system based on image gray histogram characteristics, and the method comprises the steps: collecting an image sequence under different focal lengths, carrying out the fuzzy processing, extracting a gray histogram of each frame of image, and calculating the peak position and full width at half maximum of the histogram as the evaluation characteristics of the image definition; calculating the variance of each feature and automatically allocating a weight according to the relative response degree; and finally, comprehensively evaluating the image definition through a weighted definition scoring function, and selecting the focal length corresponding to the image with the optimal score as the optimal focusing position. The method is based on the global features of the gray histogram, is high in anti-noise capability, is adaptive to different imaging scenes through weight adaptive adjustment, is low in calculation complexity, supports real-time focusing, is especially suitable for high-noise fluorescence microscopic imaging scenes, is high in system portability, is low in operation threshold, and effectively improves the accuracy and stability of microscopic automatic focusing.

A federated machine learning method is provided. The method includes providing, from a central model server, an initial trained machine learning (ML) model to a plurality of clients as a respective local ML model. The initial trained ML model is configured to identify defect features from scanning electron microscopy (SEM) images. The method additionally includes receiving, from at least one client by the central model server, information indicative of a respective updated local ML model. The method also includes determining, based on the information indicative of the respective updated local ML models, an updated global ML model.

The invention discloses a differential nonlinear competitive super-resolution microscopy method and a differential nonlinear competitive super-resolution microscopy device. Light beams emitted by the same light source simultaneously irradiate the surface of a fluorescent sample. Wherein one light beam is focused on a sample after passing through a spatial light modulator to form a high-energy hollow light spot for generating a saturation effect; the other beam of light is modulated into time sequence light by an acousto-optic modulator, the time sequence light is focused on the sample to form a low-energy solid light spot, and signal light emitted by each scanning point of the sample is collected; and performing intensity control on the light intensity of the hollow light through a light intensity modulation device to obtain two nonlinear competition result images, and performing differential processing to obtain a super-resolution image. The device is simple and convenient to operate; on the basis of a competition mechanism of solid spots and hollow spots during sample fluorescence excitation, a high-resolution image can be obtained under low illumination light intensity by utilizing differential processing, and negative values of the differentiated image are greatly reduced.

The present invention provides a method for identifying clue cells in a leucorrhea microscopic image, which belongs to the technical field of clue cell identification, and comprises: firstly, pre-processing the stained leucorrhea microscopic image, including graying, medical image enhancement and noise removal. Through spectral feature analysis, a feature space is constructed and adaptive threshold segmentation is adopted to extract the cell area, and then edge detection and morphological operations are performed to accurately extract the complete outline of the cell. The morphological feature parameters such as cell area, perimeter, roundness, grayscale mean and variance are innovatively calculated, and a feature discrimination equation group is established in combination with the spectral feature vector to realize accurate classification and identification of cells. The identification results are post-processed to remove noise interference and perform connected area analysis, the number and proportion of clue cells are counted, and annotated images are generated; and the iterative optimization of the data set verified by medical experts solves the technical problem that the consistency of the diagnosis results is poor due to the subjective experience differences of doctors.

A spatiotemporal illumination microscope system is presented. The system includes a multi-well plate configured to receive live cells and a DMD configured to deliver targeted illumination to selectively illuminate a plurality of the wells. The system further includes a tandem-lens system having an objective lens and an imaging lens positioned facing each other to capture fluorescent light emitted from the cells. The system also includes an image sensor that receives fluorescent light captured by the tandem-lens system, generating imaging data. The system further includes a controller configured to use the imaging data to control closed-loop modulation of the live cells.

A super-resolution light-sheet fluorescence microscopy method, comprising: using reversibly photoswitchable fluorescent proteins (RSFPs) to process a biological sample (1000) to be tested; using a light sheet as activation light to be incident onto said biological sample (1000), so as to generate an activated portion in the reversibly photoswitchable fluorescent proteins (RSFPs); using modulated striped structured light as excitation light to be incident onto the activated portion of the reversibly photoswitchable fluorescent proteins (RSFPs) towards a surface where the activated portion is located, such that the activated portion of the reversibly photoswitchable fluorescent proteins (RSFPs) on a plane where the light sheet is incident emits fluorescence, wherein the activation light and the excitation light have different wavelengths; and collecting a fluorescence image with structured light stripe information as a two-dimensional fluorescence image for super-resolution image processing. Further provided is a super-resolution light-sheet fluorescence microscopy system.

The invention discloses a reticulocyte recognition and grading system based on a blood smear, and particularly relates to the field of medical cell morphology examination, and the system comprises an image acquisition module, a staining normalization module, an instance segmentation module, an erythrocyte classification module, a reticulocyte grading module and a statistics output module. The image acquisition module adopts an automatic microscope platform to continuously scan the blood smear under a 100-time visual field, acquires a high-quality bright field channel image through an automatic focusing technology, and monitors image definition, contrast and illumination uniformity; the statistical output module is used for calculating the proportion and bluish violet intensity statistics of reticulocytes of each level, and generating a visual quality control result and a diagnostic report; according to the invention, full-automatic processing from samples to reports is realized, interpretability and high classification precision are both considered, subjective difference and omission ratio of manual microscopic examination are significantly reduced, and efficiency and consistency of clinical detection are improved.

A short-wave infrared photothermal (SWIP) microscopy system and method for vibrational imaging of a sample generates shortwave infrared excitation light probe light. The excitation light and the probe light are combined to generate a combined beam, which is focused to generate a focused combined beam, which is directed onto the sample to obtain a SWIP signal generated by absorption-induced thermo-optic selective heating of the sample. The SWIP signal is collected through an aperture in a condenser and detected.

The invention discloses a method for displaying and observing aggregated niobium compounds causing stretching layering cracking of wheel steel. The method comprises the steps of sampling, grinding, low-power defect display, stripping, inlaying, grinding, polishing, corrosion, scanning electron microscope observation and phase component and element atomic ratio determination through an energy disperse spectroscopy. According to the method, the periphery is ground after sampling, then the segregation defect is displayed through corrosion of a low-power corrosive liquid, the sample is stripped off from the position 0.5-1 mm away from a segregation defect line through linear cutting, hot inlaying is conducted, and after grinding, polishing and corrosion are conducted, the shape, size and distribution of aggregated and distributed niobium compounds can be clearly displayed under a scanning electron microscope; and the components and atomic ratio of the phase can be determined by using an energy disperse spectroscopy.

The invention includes systems and methods to combine acoustic sorting, high-throughput imaging technology and machine learning, such as convolutional neural networks (ConvNet) analysis, to analyze cells, pathogens, and other target particles from biological samples resolvable by high-throughput imaging microscopy, or other comparable instrument.

The invention relates to the technical field of electron microscopic imaging, in particular to an electron microscopic imaging automatic focusing method and device, computer equipment, a storage medium and a computer program product. The method comprises: acquiring an initial image; taking the initial image as a starting point, searching a to-be-detected object by using a current positioning model, and determining a focusing search starting point; the focusing search starting point is adjusted based on preset search parameters, a detection image corresponding to the adjusted imaging parameters is obtained, and the search step length and the search direction corresponding to the search parameters are dynamically adjusted according to the definition evaluation result of the historical detection image; in response to the condition that the adjusted detection image does not meet the definition evaluation index, updating the search parameter; and adjusting the imaging parameter based on the updated search parameter until the detection image meets a preset focusing requirement. By adopting the method, the efficiency of automatic focusing can be improved, and the accuracy of focus point positioning and the robustness of algorithm implementation can be improved.

The invention relates to the technical field of biological microscopic imaging, and discloses a denoising and deconvolution super-resolution imaging method and device based on optical switch molecules, electronic equipment and a computer readable storage medium. The method comprises the following steps: acquiring a plurality of fluorescence image sequences, wherein to-be-imaged structures of images in the fluorescence image sequences are marked by optical switch fluorescence molecules; each sequence is generated by utilizing the photoswitch characteristic of the photoswitch fluorescent molecules, and the sequence comprises at least one bright-state image and at least one dark-state image; performing de-noising processing on each bright-state image and each dark-state image in the plurality of fluorescence image sequences to obtain de-noised images; and carrying out deconvolution processing on the denoised image to obtain a corresponding super-resolution imaging image. The requirement for imaging equipment is low, the method can be used for various imaging technologies, the size of an imaging area is not limited, the requirement for large-view-field fixed biological sample imaging in an application scene can be met, and fixed cell and tissue large-view-field super-resolution imaging is achieved.

The invention discloses a multi-mode image information fusion method based on deep learning, and relates to the technical field of image processing, and the method comprises the steps: obtaining image data of an optical microscopic mode and an electronic microscopic mode, and carrying out the spatial resolution alignment; a quantum entangled state light field coding method is adopted for the optical microscopic image data to generate quantum enhancement features, and topological persistent coherence analysis is performed on the electronic microscopic image data to extract multi-scale structural features; the features are mapped to a tensor space, cross-modal feature fusion is carried out through a tensor ring decomposition method of dynamic rank adjustment, a joint representation tensor is generated, physical constraint reconstruction is carried out on the joint representation tensor, and a super-resolution fusion image is generated. Through fusion of quantum enhancement features and multi-scale topological features, tensor ring decomposition of dynamic rank adjustment and Tucker decomposition dimension reduction compression, efficient fusion of high-resolution images is achieved, calculation redundancy is reduced, and processing efficiency is remarkably improved.

The invention discloses a gradient refractive index lens-based micropore microscopic imaging system and method, light emitted by a white light source passes through a series of elements in a device and then illuminates the inner wall of a micropore, and reflected light on the surface of the inner wall sequentially passes through a reflector, a gradient refractive index lens, an optical fiber image transmitting bundle and a dichroscope and then reaches a focusing lens; and the focusing lens converges the parallel light to the surface of the CCD for imaging. The micropore microscopic imaging measuring device has the advantages of small size, high resolution and in-situ imaging, a sample is imaged by adopting a combination of a gradient refractive index lens and a reflecting prism which are NA = 0.5, the transverse working distance of an imaging system can be controlled by adjusting the distance between the gradient refractive index lens and the reflecting prism while the resolution is ensured, and the imaging precision is improved. Therefore, the probe can carry out imaging detection in a hole with a smaller diameter.

The invention discloses a rock and ore microscopic image splicing method and system based on deep learning, and relates to the field of image processing and the technical field of microscopes, and the method comprises the steps: obtaining a local rock and ore microscopic image of a rock and ore slice, carrying out the preprocessing of the local rock and ore microscopic image, and carrying out the overlapping region coarse registration of the preprocessed local rock and ore microscopic image through a phase correlation method; based on an improved image feature detection model, basic features and description features in the local rock and ore microscopic image after coarse registration are extracted, and local image features of the local rock and ore microscopic image are obtained; performing feature matching on the local image features of the two groups of local rock and mineral microscopic images by using an image feature matching model to obtain a matching corresponding relation of the local image features; and based on an image fusion algorithm of a homography matrix and a partial differential equation, splicing and optimizing the local rock and ore microscopic images in combination with a matching corresponding relation of local image features, and generating a large-view-field rock and ore microscopic image. According to the method, the complex transformation between the images can be better processed.

The invention discloses a mixing parameter regulation and control method and system for an SBS-T modified asphalt mixture and a terminal. The method comprises the following steps: analyzing a modification mechanism through a scanning electron microscope and X-ray photoelectron spectroscopy combined technology, dividing three stages of rapid melting and the like, and establishing a temperature-viscosity model; constructing a multi-scale capture system by using an atomic force microscope, a Fourier transform infrared spectrum and a dynamic shear rheometer; designing a five-factor three-level test matrix by adopting a response surface method, and establishing a road performance prediction model in combination with a BP neural network; mixing parameters are optimized in a multi-objective mode based on a genetic algorithm, and dynamic correction is achieved through an Internet of Things sensor and Kalman filtering. The system comprises a mechanism analysis module, a multi-scale detection module, a terminal integrated storage unit, a processing unit and a man-machine interaction unit. According to the scheme, the limitation of traditional single-factor analysis is broken through, microscopic and macroscopic collaborative optimization and dynamic parameter regulation and control are achieved, and the construction adaptability and precision are improved.

The invention relates to the technical field of biological microscopic imaging, and discloses a deconvolution super-resolution imaging method and device based on deep learning denoising and a medium. The method comprises the following steps: acquiring a to-be-denoised image of a to-be-imaged structure marked by fluorescent molecules; de-noising the image to be de-noised by using a preset image de-noising neural network to obtain a de-noised image; a training data set for training the network is generated by utilizing the optical switch characteristics of optical switch fluorescent molecules, and the optical switch fluorescent molecules are used for marking to-be-imaged structures of samples in the training data set; the emission spectrum of the fluorescent molecules is overlapped with the emission spectrum of the fluorescent molecules of the optical switch; and carrying out deconvolution processing on the denoised image to obtain a super-resolution imaging image. According to the invention, the large-scale and high-quality true value image is obtained by using the optical switch characteristic of the optical switch fluorescent molecules, and the denoising effect of the image denoising neural network is improved; and denoising the image by using the network, and then carrying out deconvolution on the denoised image to realize large-view living cell single-frame super-resolution imaging.

The invention relates to a method for in-situ observation of an alkali metal solid-state battery. The method comprises the following steps: S1, observing morphology and component changes of interface cross sections of a positive electrode, a negative electrode and an electrolyte of the alkali metal solid-state battery through a scanning electron microscope; and S2, carrying out structure evolution characterization on an interface of the positive electrode, the negative electrode and the electrolyte of the alkali metal solid-state battery through synchrotron radiation X-ray diffraction. According to the method for in-situ observation of the alkali metal solid-state battery, the scanning electron microscope is combined with the synchrotron radiation X-ray diffraction, so that the defect that a traditional in-situ characterization method is single is overcome, and morphology, morphology and morphology of an air-sensitive alkali metal solid-state battery electrode and an electrolyte contact interface under the conditions of different working currents, voltages and temperatures are realized. And comprehensive and accurate real-time characterization and analysis of components and structures are realized.

Systems and methods are provided for analyzing a biomechanical property of a medium using stimulated Brillouin scattering microscopy. The method can include a first step of applying a probe beam and pulsed pump beam to a target section of the medium, wherein the pump beam interacts with the probe beam to generate at least one acoustic wave in the medium and at least one Brillouin signal is produced as a result of the generated acoustic wave. The method can also include a second step of receiving the produced Brillouin signal and a third step of determining, using a processor and the Brillouin signal, information associated with at least one biomechanical property of the target section.

A deep learning-based super-resolution fluorescence lifetime imaging microscopy (SR-FLIM) method includes the steps of: S1, performing fluorescence microscopic imaging on a sample to obtain confocal intensity images and stimulated emission depletion (STED) intensity images at a same location; S2, co-registering the acquired confocal and STED intensity images; S3, pairing the co-registered confocal and STED intensity images as input (Input) and ground truth (GT) to assemble a dataset; S4, partitioning the dataset into training and validation sets following a predefined ratio; and S5, constructing a network, and selecting hyperparameters and an optimizer. This method may achieve SR-FLIM within a conventional confocal FLIM system, surpassing spatial resolution limitations of FLIM, breaking through resolution barriers of conventional optical microscopy, while preserving normal fluorescence lifetime characteristics of fluorescent probes.