A method of detecting and classifying anomalies during semiconductor processing includes executing a wafer recipe a semiconductor processing system to process a semiconductor wafer; monitoring sensor outputs from a sensors that monitor conditions associated with the semiconductor processing system; providing the sensor outputs to models trained to identify when the conditions associated with the semiconductor processing system indicate a fault in the semiconductor wafer; receiving an indication of a fault from at least one of the models; and generating a fault output in response to receiving the indication of the fault.

Industrial quality control is challenging for artificial neural networks (ANNs) and deep neural networks (DNNs) because of the nature of the processed data: there is an abundance of consistent data representing good products, but little data representing bad products. In quality control, the task is changed from conventional DNN task of “recognize what I learned best” to “recognize what I have never seen before.” Lifelong DNN (L-DNN) technology is a hybrid semi-supervised neural architecture that combines the ability of DNNs to be trained, with high precision, on known classes, while being sensitive to any number of unknown classes or class variations. When used for industrial inspection, L-DNN exploits its ability to learn with little and highly unbalanced data. L-DNN's real-time learning capability takes advantage of rare cases of poor-quality products that L-DNN encounters after deployment. L-DNN can be applied to industrial inspections and manufacturing quality control.

A product inspection device and method for correctly calculating consumer and producer risks irrespective of the type of distribution of products. A characteristic value representing a predetermined product characteristic is measured for each product as a product measurement value, and a standard deviation of measurement variations in measurement results is calculated as a measurement value standard deviation. The products are determined to be conforming based on whether the measured product measurement value falls within a range of a product standard. Consumer and producer risks are calculated based on the measurement variations. The calculated consumer and producer risks are respectively and successively added up and it is determined whether the number of products having undergone the adding have reached a predetermined number. If so, the added up consumer risk and producer risk are divided by the number of products to calculate a final consumer risk and a final producer risk.

A method includes processing a substrate in a process chamber according to a recipe, wherein the substrate comprises at least one of a film or a feature after the processing. The method further includes generating a profile map of the first substrate. The method further includes processing data from the profile map using a first model, wherein the first model outputs at least one of an estimated mesa condition of a substrate support for the process chamber, an estimated lift pin location condition of the substrate support an estimated seal band condition of the substrate support, or an estimated process kit ring condition for a process kit ring for the process chamber. The method further includes outputting a notice as a result of the processing.

A method for determining the level of unusualness of individuals, in particular in order to statistically detect unusual individuals in a set of previously gathered data resulting from measurements of parameters of individuals taken by a plurality of measuring systems. The data is pre-processed, and a multivariate unusualness index is determined. The index being transformed by a function so as to be between 0 and 1, on the set of measurements for each individual based on the preprocessed data. Unusual individuals are then identified.

Techniques for inspection of articles having multiple features of one or more types are disclosed. Input data indicative of one or more selected features of interest is used for inspection by a given inspection system characterized by associated imaging configuration data. The input data is analyzed to extract information regarding one or more inspection tasks, and an inspection plan data is generated, to be used as a recipe data for operation of the given inspection system to provide measured data in accordance with the one or more inspection tasks. Selected inspection mode data corresponding to the inspection task data may be retrieved from a database system and utilized to generate the inspection plan data.

A system and method for inspection maintenance and/or diagnosis of a variety of workpieces is provided. The system serves workers working on a workpiece, inspectors who are distal from the workers and/or can be used for remote training or for advanced diagnosis and/or repair. The system preferably includes a template of a set of one or more predefined required images of a workpiece required by an inspector to perform their inspection or diagnosis. The set of predefined required images is provided to the worker. The worker captures the images with an appropriate workpiece data capture device and provides them to the inspector for review. The inspector examines the provided images and either approves the workpiece based on their content, requests additional images for further examination and/or provides annotations and other information to the worker to address identified issues. The system maintains a database of all images and information.

The embodiment of the present disclosure provides an Industrial Internet of Things system for inspection operation management of an inspection robot and a method thereof. The system includes a user platform, a service platform, a management platform, a sensor network platform, and an object platform that are interacted sequentially from top to bottom. The management platform is configured to perform operations including: determining an inspection task, the inspection task including detecting at least one detection site; sending instructions to an inspection robot based on the inspection task to move the inspection robot to a target position to be inspected; obtaining detection data based on the inspection robot, and determining subsequent detection or processing operations based on the detection data.

A system and a method of enhancing reliability of fused deposition modelling (FDM) process are disclosed. The system instructs a three-dimensional (3D) printer to employ a 3D printer nozzle for dispensing material for forming a 3D print object. The system receives images from a nozzle camera. The nozzle camera captures images of the 3D printer nozzle dispensing the material. The system detects printing failures from the images of the 3D printer nozzle. The system creates bounding boxes around the printing failures. The system classifies the printing failures based on type of errors. The system adjusts printing parameters or terminates the printing process based on the printing failures classified. The system further includes a bracket for positioning the nozzle camera for capturing images of the 3D printer nozzle.

A substrate processing apparatus includes a process station for processing a substrate; a cassette station integrated with the process station; a substrate carriage equipped for transferring the substrate between said process station and the cassette station through a passage located at an interface between the process station and said cassette station; and a substrate scanner equipped at said interface between the process station and the cassette station for capturing surface image data during transportation of the substrate that passes through the passage.