A method for determining processing chamber conditions using sensor data and a machine learning model is provided. The method includes receiving, by a processing device, sensor data that include chamber data indicating a state of an environment of a processing chamber processing a substrate according to a set of process parameters of a current process. The sensor data further include spectral data indicating optical emission spectra (OES) measurements of a plasma disposed within the processing chamber. The method further includes using the sensor data as input to a machine learning model and obtaining one or more outputs that indicate one or more chamber condition metrics. The method further includes determining a recovery status of a processing chamber based on the one or more chamber condition metrics. The method further includes causing a modification to a performance of the processing chamber based on the recovery status of the processing chamber.

A quality control system may include a controller configured to be communicatively coupled with a monitoring assembly including one or more detectors. The controller may implement two or more AI quality control (AIQC) modules associated with two or more process steps for fabricating a composite material, where each of the two or more AIQC modules is associated with a different one of the two or more process steps. A particular AIQC module may receive monitoring data associated with the particular process step for a workpiece, generate quality control data using a particular AI model, and update the particular AI model based on testing data associated with the workpiece from one or more testing tools after at least the particular process step.

An anomaly determination device and an anomaly determination method determine an anomaly of a device based on state data of the device, by using a first determination model configured to determine whether a predetermined anomaly has occurred in the device, and a second determination model configured to classify state of the device, and output the determined anomaly of the device as an unknown anomaly in a case where the anomaly of the device is not the predetermined anomaly.

Systems and methods of monitoring a production level of a vibratory feeder configured to process a workpiece are described herein. The methods include operating a processor to: receive device data associated with the vibratory feeder during operation of the vibratory feeder, the device data comprising at least one input state of the vibratory feeder; receive production data associated with the vibratory feeder, the production data being representative of a production level of the vibratory feeder; determine, based on the device data and/or the production data, one or more faults corresponding to the vibratory feeder when the production level falls below a threshold production level; and determine, based on the one or more faults, a corrective action to return the production level to or above the threshold production level.

Systems and methods for improving wafer fabrication. Wafers may be inspected at various points in the fabrication process to generate inspection data. The inspection data and wafer-in-progress data may be used to identify defect patterns and tools and/or processes that cause wafer defects. The inspection data may be stacked to form virtual wafer maps that amplify signals to detect patterns more easily. Defect patterns and tools and/or processes may also be identified through machine learning models receiving artificial defect visualizations as input.

A manufacturing condition setting automating apparatus includes: a quality judging unit that computes a present process quality from facility data at predetermined time intervals, and judges whether or not it is in a quality tolerance range; a manufacturing condition candidate creating unit that computes a feature quantity, searches a database for condition change cases having similar feature quantities, tabulates condition change cases basis on whether the condition change cases are successes or failures, and outputs manufacturing condition candidates in descending order of rates of successes; an imbalance-preventing manufacturing condition candidate creating unit that changes scores that decide ranks of manufacturing condition candidates, and creates a ranking of manufacturing condition candidates; and a manufacturing condition output unit that outputs a set value of a condition change of a top manufacturing condition candidate to the manufacturing facility, and registers a new condition change in the condition change history.

A distributed computing system for product defect analysis is disclosed. The distributed computing system for product defect analysis includes a computing cluster for processing product manufacturing messages, a computing cluster for identifying product defect, a product image database, and a client device.

Methods, apparatus, and computer program products for analyzing, monitoring, and/or modeling the manufacture of a type of part by a manufacturing process. Non-destructive evaluation data and/or quality related data collected from manufactured parts of the type of part may be aligned to a simulated model associated with the type of part. Based on the aligned data, the manufacturing process may be monitored to determine whether the manufacturing process is operating properly; aspects of the manufacturing process may be spatially correlated to the aligned data; and/or the manufacturing process may be analyzed.

A method for producing material boards in a production plant in which apparatuses form a material into a mat that is pressed to obtain the material board which has specific quality parameters. The production plant and/or the apparatuses are controlled in an open- or closed-loop manner by a controller, which preferably includes a programmable logic controller, and input parameters are received, processed and/or output by the controller. The input parameters are formed at least from settable product parameters for the material board to be produced, from settable and/or recorded plant parameters of the production plant and/or the apparatuses and/or from recorded material parameters. A quality value of at least one quality parameter of the material board to be produced is determined based on the input parameters by an algorithm based on artificial intelligence. The algorithm is trained or formed by a database which has at least one quality parameter and input parameters correlating with the quality parameter.

A visual feedback controller includes: a camera that outputs a target object image; a template image storage unit that stores a template image; an image processing unit that uses the target object image and the template image to measure an actual position of a target object with subpixel accuracy, and outputs the measured actual position as an image processing measurement value; a drive command generation unit that generates a drive command signal; an error compensation control unit that compensates an error of the drive command signal using the image processing measurement value and generates a drive compensation signal; a machine drive unit that changes a position of a machine tip relative to the target object based on the drive compensation signal; and a template image analysis unit that analyzes the template image, calculates measurement accuracy of the image processing measurement value, and outputs the calculated measurement accuracy.