Systems and methods are provided for determining a depth map and a reflectivity map from a structured light image. The depth map can be determined by capturing the structured light image and then using a triangulation method to determine a depth map based on the dots in the captured structured light image. The reflectivity map can be determined based on the depth map and based on performing additional analysis of the dots in the captured structured light image.

An apparatus for volumetric imaging is provided. The apparatus comprises an illumination assembly arranged to direct light to illuminate a plurality of planes in a sample region sequentially at an illumination rate, each plane extending over a plurality of depths in the sample region; an image sensor comprising a plurality of sections of pixels and arranged to sense each section of pixels sequentially at a sensing rate; and a light-receiving assembly arranged to receive light from the sample region and to direct light received from each of said planes in the sample region to a different respective section of said sections of pixels. The light-receiving assembly comprises a multi-plane optical assembly arranged to receive light from the plurality of depths in the sample region and, for each section of said sections of pixels, to direct light simultaneously from each of the plurality of depths in the respective plane to a different respective subsection of said section. The illumination rate is equal to the sensing rate, such that each section of pixels is arranged to sense light from the plurality of depths in the respective plane as the plane is illuminated by the illumination assembly.

Disclosed are methods and apparatus for inspecting a vertical semiconductor stack of a plurality of layers is disclosed. The method includes (a) on a confocal tool, repeatedly focusing an illumination beam at a plurality of focus planes at a plurality of different depths of a first vertical stack, wherein a defect is located at an unknown one of the different depths and the illumination beam has a wavelength range between about 700 nm and about 950 nm, (b) generating a plurality of in-focus images for the different depths based on in-focus output light detected from the first vertical stack at the different depths, wherein out-of-focus output light is inhibited from reaching the detector of the confocal system and inhibited from contributing to generation of the in-focus images, and (c) determining which one of the different depths at which the defect is located in the first vertical stack based on the in-focus images.

A method is disclosed for selecting an optimal value for an adjustable parameter of a structured light metrology (SLM) system, for scanning an object. The SLM system performs test scans of the object to acquire a plurality of sets of measurements of the object, wherein a different value is used for the parameter for each test scan. For each test scan, a value of a quality metric is calculated, based on the set of measurements of the object associated with the test scan and simulation data representing a simulated scan of the object by the SLM system. A test scan is then identified that has a quality metric value that satisfies a specified optimization criterion; and a value of the adjustable parameter that was used for the identified test scan is selected as the optimal value of the adjustable parameter, for scanning the object.

An example method for detecting target particles in a detection apparatus includes receiving, by at least one processor of the detection apparatus, a first image data at a first wavelength from an image sensor of the detection apparatus. The method includes receiving, by the at least one processor, a second image data at a second wavelength from a particle detector of the detection apparatus. The method includes obtaining, by the at least one processor, a 2D image based on the first image data. The method includes obtaining, by the at least one processor, a depth map data based on the first image data. The method includes obtaining, by the at least one processor, a particle map data based on a ratio of the second image data to the 2D image. The method includes determining, by the at least one processor, a concentration result based on the depth map data and the particle map data.

Instruments and methods of obtaining a three-dimensional image of a biological specimen are described herein. The methods include capturing a first image of a first frame of the specimen using a sensor array. The first frame is an in-focus area of the specimen at a first depth level of the specimen. The specimen is then moved relative to the sensor array in a scanning direction by a predetermined number of rows of pixels. A second image of a second frame of the specimen is captured using the sensor array. The second frame is an in-focus area of the specimen at a second depth level of the specimen. The second depth level is spaced apart from the first depth level in a direction perpendicular to the scanning direction. The first image and the second image are processed to obtain the three-dimensional image of the specimen.

A method and a system for analyzing a fluid sample for a biological activity. The method includes, providing a filter unit including a membrane having a front face and a rear face, passing the fluid sample through the filter membrane from its front face, applying the filter unit in a container, adding a medium into the container, and performing a scanning and image analyzing procedure of the filter membrane using at least one selected scanning wavelength. The scanning is an optical 3D scanning and includes acquiring a plurality of images along at least one scanning path.

A metrology three-dimensional (3D) scanning system includes a metrology 3D scanning application (app) comprising computing instructions that, when executed by one or more processors, causing the one or more processors to: record human-robot interaction (HRI) data as a human operator operates the HRI device; generate a preliminary scan path based on the HRI data for operating a robotic element within an operating environment; move the robotic element along at least a portion of the preliminary scan path and record preliminary scan data comprising at least a subset of dimension data defining at least a target object; generate a metrology scanning path plan and a motion plan for the robotic element based on the preliminary scan data; and execute instructions to move the robotic element within the operating environment according to the metrology scanning path plan and the motion plan for scanning the target object.

An imaging system may include an imaging metrology tool with an illumination source, one or more illumination optics to direct illumination from the illumination source to a sample, a detector, one or more collection optics to image the sample onto the detector; and one or more aberration-controlling components. The one or more aberration-controlling components may provide aberration correction for imaging the sample onto the detector according to one or more degrees of freedom, where the one or more degrees of freedom include at least a defocus of the imaging system, and where the one or more aberration-controlling components are integrated with at least one of the one or more illumination optics, the one or more collection optics, or the detector.

The invention describes an iterative reconstructing method allowing a spatial distribution of fluorescence in an object to be obtained. The method comprises acquiring images of fluorescence in various planes at various depths in the object, so as to form a three-dimensional acquired image. It comprises an iterative reconstructing algorithm with, in each iteration, an initial fluorescence distribution or a fluorescence distribution resulting from a preceding iteration being taken into account, and the fluorescence light wave propagating through the object being simulated, so as to obtain a reconstruction of the acquired image. The acquired image, or a differential image corresponding to a comparison between the acquired image and the reconstructed image, is then back-propagated through the object, so as to update the fluorescence distribution. FIG. 5B.