A machine learning device is provided with: an input data obtaining unit that, in burnishing process in which a processing surface of an arbitrary workpiece is surface-treated with an arbitrary tool, obtains as input data processing information including information of the workpiece prior to the burnishing process and information of a processing condition; a label obtaining unit that obtains label data indicating processed state information including a processed state of the workpiece after the burnishing process and surface roughness of the workpiece when the processed state is normal; and a learning unit that carries out supervised learning using the input data and the label data thus obtained to generate a learned model to which processing information of an upcoming burnishing process is input and which outputs processed state information for the burnishing process.

A product state estimation device includes: an examination result acquisition device that acquires an examination result related to a state of a product obtained through each shot by a die-casting machine; a time series data acquisition device that acquires time series data based on an output from a sensor that detects an operation state of the die-casting machine at each shot; a time series data manipulation device that performs manipulation that clips data of a predetermined time interval out of the time series data; an estimation model generation device that generates an estimation model by using a neural network with the examination result of the product and the manipulated time series data as learning data; and an estimation device that estimates information related to quality of the product based on the manipulated time series data obtained from a plurality of detection signals at each shot by using the estimation model.

A method includes identifying sets of sensor data associated with wafers processed via wafer processing equipment and identifying sets of metrology data associated with the wafers processed via the wafer processing equipment. The method further includes generating sets of aggregated sensor-metrology data, each of the sets of aggregated sensor-metrology data including a respective set of sensor data and a respective set of metrology data. The method further includes causing, based on the sets of aggregated sensor-metrology data, performance of a corrective action associated with the wafer processing equipment.

A computing system may include a virtual cross metrology engine configured to construct a given virtual metrology model. The given virtual metrology model may take, as inputs, process parameters applied for the given step of a semiconductor fabrication process. The virtual cross metrology engine may also be configured to construct a subsequent virtual metrology model, and the subsequent step is performed after the given step in the semiconductor fabrication process. Doing so may include determining inputs for the subsequent virtual metrology model from a combination of the process parameters applied for the given step of the semiconductor fabrication process, process parameters applied for the subsequent step of the semiconductor fabrication process, and a wafer value for the given step of the semiconductor fabrication process that the given virtual metrology model is configured to predict.

A computer implemented methods for estimating at least one quality function of a given layered coating on a transparent substrate allows to predict at least one non in-process measured quality function of a given layered coating on a transparent substrate from an in-process measured quality function which can be acquired on the coated substrate as deposited at any location, preferably at the end of a coating process. The method allows to get rid of in-process real-time continuous measurements of quality functions of the coated transparent substrate and real-time monitoring of coating process parameters.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimizing material processing. In one aspect, a method includes collecting, from a set of sensors, a set of current manufacturing conditions. Based on the set of current manufacturing conditions collected from the sensors, a set of current qualities of a material currently being processed by manufacturing equipment is determined. A baseline production measure for processing the material according to the set of current qualities is obtained. A candidate set of manufacturing conditions that provide an improved production measure relative to the baseline production measure is determined. A set of candidate qualities for the material produced under the candidate set of manufacturing conditions is determined. A visualization that presents both of the set of candidate qualities of the material and the set of current qualities of the material currently being processed is generated.

Various systems and methods for modeling a manufacturing assembly line are disclosed herein. Some embodiments relate to operating a processor to receive cell data, extract feature data from the cell data, determine a plurality of cell configurations, determine an efficiency score by applying the feature data to a predictive model generated for predicting a production level of the manufacturing assembly line, determine at least one target cell configuration from the cell configurations based on the efficiency score, and apply the at least one target cell configuration to at least one cell by implementing each target cell configuration to a corresponding cell.

When inspection data of a process inspection in a production line is input, a machine learning unit 420 of a prediction score calculation device 202 performs machine learning so as to output a prediction score of quality determination of a final inspection. In addition, a prediction score calculation unit 410 outputs a prediction score predicting the quality determination result of the final inspection from the inspection data of the process inspection using a machine learning model that has performed the machine learning. In addition, a threshold value determination unit 440 compares the prediction score calculated by the prediction score calculation unit 410 and determines a threshold value for predicting the quality determination from learning data, the prediction score, and cost data.

Aspects of the disclosed technology encompass the use of a deep learning controller for monitoring and improving a manufacturing process. In some aspects, a method of the disclosed technology includes steps for: receiving a plurality of control values from two or more stations, at a deep learning controller, wherein the control values are generated at the two or more stations deployed in a manufacturing process, predicting an expected value for an intermediate or final output of an article of manufacture, based on the control values, and determining if the predicted expected value for the article of manufacture is in-specification. In some aspects, the process can further include steps for generating control inputs if the predicted expected value for the article of manufacture is not in-specification. Systems and computer-readable media are also provided.

A yield-rate assessment apparatus for a manufacture system including a plurality of machines, each machine participating in one or more manufacture steps of a batch of products in the manufacture system, performs for each machine: calculating a bad-piece expectation value and a quantity of potential bad pieces at each corresponding manufacture step based on a quantity of bad pieces detected after the last one of the manufacture steps is finished and an initial yield rate of the current machine; calculating a good-piece expectation value based on a quantity of good pieces detected after the last one of the manufacture steps is finished and a summation of all quantities of potential bad pieces calculated for the current machine; and assessing a yield rate according to the good-piece expectation value calculated for the current machine and a summation of the bad-piece expectation value calculated for the current machine at each corresponding step.