The invention specifies an apparatus for evaluating sensor measured values (1.1), having: —a sensor (1), wherein a model function that is suitable for a least squares regression and definable by a parameter vector is provided for evaluating the sensor measured values (1.1) of the sensor (1), wherein at least one parameter of the parameter vector forms a sensor output signal (3), and —a computing and evaluation unit (2) that has a neural network (2.1), which estimates the parameter vector on the basis of actually ascertained sensor measured values (1.1), and a least squares regression module (2.2), wherein the neural network (2.1) is trained with parameter vectors and the associated sensor measured values, and that is set up: .sup.∘—to use the trained neural network (2.1) to ascertain at least one parameter estimate vector for sensor measured values (1.1) measured using the sensor (1) as an input variable for the least squares regression module (2.2), .sup.∘—if a convergence criterion is satisfied for the performance of the least squares regression, to terminate the least squares regression and .sup.∘—to output the at least one parameter of the most recently ascertained parameter vector as sensor output signal (3). An associated automated method for evaluating sensor measured values and a use of the apparatus are likewise specified.

An apparatus to detect optical flatness of an OLED display layer includes a light-emitting assembly, a light-receiving assembly, and an image processor. The light-emitting assembly includes a light source and a first enhancement element. The light source emits reference light through the first enhancement element. The first enhancement element enhances brightness of the reference light and guides the enhanced reference light to a display layer of a display device being detected. The light-receiving assembly receives light reflected by the display layer according to the reference light and generates an image thereof. The image processor receives the image and obtains a result of detection as to surface flatness of the display layer according to the image.

A method and apparatus for optical metrology is disclosed. There is disclosed, for example, a method involving a radiation intensity distribution for a target measured using an optical component at a gap from the target, the method including calculating a correction factor for the variation of radiation intensity of the radiation intensity distribution as a function of variation of the distance of the gap.

An inspection device inspects a formation state of a pocket portion formed in a container film of a blister pack and includes: an illumination device that irradiates a container film including the pocket portion with a predetermined electromagnetic wave; an imaging device that takes an image of at least the electromagnetic wave transmitted through a bottom portion of the pocket portion and obtains image data; a control device that extracts, based on the image data, shading pattern data corresponding to a shading pattern occurring in the bottom portion of the pocket portion by irradiation with the electromagnetic wave; a storage that stores a neural network and a model, the model being generated by learning of the neural network using, as learning data, only shading pattern data of a pocket portion without any formation defect among the extracted shading pattern data.

Methods and systems for generating a simulated image from an input image are provided. One system includes one or more computer subsystems and one or more components executed by the one or more computer subsystems. The one or more components include a neural network that includes two or more encoder layers configured for determining features of an image for a specimen. The neural network also includes two or more decoder layers configured for generating one or more simulated images from the determined features. The neural network does not include a fully connected layer thereby eliminating constraints on size of the image input to the two or more encoder layers.

A method for inspecting containers, wherein the containers are transported in the form of a container mass flow by a transporter and are recorded as first measurement data by a first inspection unit and as second measurement data by a second inspection unit, wherein the first measurement data and the second measurement data are evaluated jointly by an evaluation unit using an evaluation method operating based on artificial intelligence to give output data, in order to ascertain an inspection result, such as for example a fill level, from the output data.

A method for optically inspecting containers in a drinks processing system, wherein the containers are transported as a container mass flow using a transporter and captured as camera images by an inspection unit arranged in the drinks processing system, and wherein the camera images are inspected for faults by a first evaluation unit using a conventional image processing method, wherein the camera images with faulty containers are classified as fault images and the faults are correspondingly assigned to the fault images as fault markings, wherein the camera images with containers considered to be good quality are classified as fault-free images, the fault images, the fault markings and the fault-free images are compiled as a specific training data set, and wherein, using the specific training data set, a second evaluation unit is trained in situ with an image processing method working on the basis of artificial intelligence.

The present disclosure proposes a method for classifying defects and the like by using a learning device that has been suitably trained, a system, and a computer-readable medium. As one aspect thereof, the present disclosure proposes (see FIG. 1) a defect inspection method, etc., in which one or more computers are used to inspect a defect on a sample on the basis of output information from detectors that detect scattered light produced via the irradiation of the sample with light, wherein defect information is outputted by: receiving output from a plurality of detectors disposed at a plurality of angles of elevation with reference to the sample surface, and at a plurality of sample surface-direction orientations with reference to the irradiation points of the light on the sample; and inputting the output information of the plurality of detectors into a learning device that has been trained using the output information from the plurality of detectors and the defect information.

An inspection system includes an inspection device having at least one image capture device. The image capture device captures image data of a sheet part passing through the inspection device. A processing unit of the inspection device provides the image data representative of the sheet part to a plurality of neural networks, where each of the neural networks is trained to identify a corresponding defect in the sheet part and output data indicative of the presence of the corresponding defect. The processing unit determines a quality category of the sheet part based on the data indicative of the presence of the corresponding defect output by each corresponding neural network. The processing unit can further output the quality category of the sheet part to a sorter that can sort the sheet part based on the quality category.

Wafer taping apparatuses and methods are provided for determining whether taping defects are present on a semiconductor wafer, based on image information acquired by an imaging device. In some embodiments, a method includes applying an adhesive tape on a surface of a semiconductor wafer. An imaging device acquires image information associated with the adhesive tape on the semiconductor wafer. The presence or absence of taping defects is determined by defect recognition circuitry based on the acquired image information.