An inspection apparatus includes a light source. A first measurement unit is configured to receive light from the light source and direct it to a first measurement object. A second measurement unit is configured to receive the light from the light source and direct it to a second measurement object. An inspection unit is configured to receive a first optical signal provided from the first measurement unit and inspect the first measurement object using the first optical signal, and to receive a second optical signal provided from the second measurement unit and inspect the second measurement object using the second optical signal. A measurement position selection unit is configured to alternately enable the inspection of the two measurement units by adjusting an angle of a reflection mirror.

A metrology system includes an illumination source to generate an illumination beam, a multi-channel spectral filter, a focusing element to direct illumination from the single optical column to a sample, and at least one detector to capture the illumination collected from the sample. The multi-channel spectral filter includes two or more filtering channels having two or more channel beam paths. The two or more filtering channels filter illumination propagating along the two or more channel beam paths based on two or more spectral transmissivity distributions. The multi-channel spectral filter further includes a channel selector to direct at least a portion of the illumination beam into at least one selected filtering channel to filter the illumination beam. The multi-channel spectral filter further includes at least one beam combiner to combine illumination from the two or more filtering channels to a single optical column.

An optical measuring system for determining a measured variable in a medium includes a light source and a container with medium. The light source radiates measuring light into the container on a first light path, wherein the measuring light is converted into reception light as a function of the measured variable and radiates reference light past the container on a second light path. A diffusion disk is arranged between the container and a receiver, wherein the diffusion disk is configured and arranged such that the reception light impinges on the receiver through the diffusion disk. The diffusion disk is configured such that the reference light impinges on the receiver through the diffusion disk. The receiver receives the reception light and the reference light, and a data processing unit connected to the light source and to the receiver determines the measured variable from the measuring light and the reception light.

An inspection apparatus includes a light source. A first measurement unit is configured to receive light from the light source and direct it to a first measurement object. A second measurement unit is configured to receive the light from the light source and direct it to a second measurement object. An inspection unit is configured to receive a first optical signal provided from the first measurement unit and inspect the first measurement object using the first optical signal, and to receive a second optical signal provided from the second measurement unit and inspect the second measurement object using the second optical signal. A measurement position selection unit is configured to alternately enable the inspection of the two measurement units by adjusting an angle of a reflection mirror.

Various characteristics in laser absorption spectroscopy using multiple reflections are improved. A gas measurement apparatus 1 according to the present disclosure measures a gas concentration by laser absorption spectroscopy. The gas measurement apparatus 1 includes a first corner cube 10, a second corner cube 20 disposed opposite the first corner cube 10, a laser source 30 that emits a laser beam to the first corner cube 10, and a light receiving element 40 that receives the laser beam that has been reflected multiple times through a target gas between the first and second corner cubes 10 and 20. The second corner cube 20 is disposed so that a centerline parallel to incident and exit light centering at incident and exit positions of the first corner cube 10 is displaced from a centerline parallel to incident and exit light centering at incident and exit positions of the second corner cube 20.

A method for 3D imaging of samples using a machine learning algorithm is disclosed. The method uses multimodality focal stacks, which consist of a plurality of images acquired at two or more distances between the sample and a front focal plane, with at least one image acquired using the first modality and additional images acquired using additional modalities. The modalities may have different illumination angles and optionally different spectral distributions. The method may include receiving training images of samples, which include a plurality of training focal stacks, and ground truth 3D data (depth maps) for each training focal stack, and training a machine learning algorithm based on the training images and the ground truth data. The method subsequently receives product images of a sample, the product images including a focal stack from an optical assembly, and generates a 3D depth map.

A metrology system includes an illumination source to generate an illumination beam, a multi-channel spectral filter, a focusing element to direct illumination from the single optical column to a sample, and at least one detector to capture the illumination collected from the sample. The multi-channel spectral filter includes two or more filtering channels having two or more channel beam paths. The two or more filtering channels filter illumination propagating along the two or more channel beam paths based on two or more spectral transmissivity distributions. The multi-channel spectral filter further includes a channel selector to direct at least a portion of the illumination beam into at least one selected filtering channel to filter the illumination beam. The multi-channel spectral filter further includes at least one beam combiner to combine illumination from the two or more filtering channels to a single optical column.

A sample analyzer has an illuminator for illuminating an assay sample to cause luminescence, and a support for a sample vessel containing the assay sample. The support is adapted to position the assay sample proximate the illuminator. A detector is positioned along an optical axis extending from the illuminator, through the positioned assay sample, to the detector, so as to detect the luminescence from the assay sample. A reflector is removably disposed between the illuminator and the assay sample so as to reflect a portion of the luminescence back through the positioned assay sample toward the detector.

A device for examining material samples via electromagnetic radiation. The device comprises a lighting unit for generating the electromagnetic radiation with at least two radiation sources. The radiation of the radiation sources can be selectively directed onto the material sample. The device further comprises a detection unit having at least two detectors for capturing electromagnetic radiation emanating from the material sample. A deflection element is arranged in the detection unit, via which the electromagnetic radiation emanating from the material sample can be selectively deflected onto one of the detectors. This deflection element comprises a mirror, via which the radiation emanating from the material sample can be selectively deflected onto one of the detectors. The mirror is rotated about an axis extending substantially perpendicular to the optical axes of the detectors.

A method for 3D imaging of samples using a machine learning algorithm is disclosed. The method uses multimodality focal stacks, which consist of a plurality of images acquired at two or more distances between the sample and a front focal plane, with at least one image acquired using the first modality and additional images acquired using additional modalities. The modalities may have different illumination angles and optionally different spectral distributions. The method may include receiving training images of samples, which include a plurality of training focal stacks, and ground truth 3D data (depth maps) for each training focal stack, and training a machine learning algorithm based on the training images and the ground truth data. The method subsequently receives product images of a sample, the product images including a focal stack from an optical assembly, and generates a 3D depth map.