A device includes a catalog storage unit that stores a catalog having versatility; a catalog control unit that specifies the catalog corresponding to a target machine tool to be subjected to state determination based on machine tool information including an item of facility type and acquires the catalog from the catalog storage unit; a feature quantity extraction unit that extracts a feature quantity from sensor data detected from the target machine tool; a sensor data processing unit that performs state determination of the target machine tool based on the feature quantity distribution included in the acquired catalog and the feature quantity extracted from the sensor data; a feature quantity tuning unit that performs tuning of the feature quantity distribution by mapping the extracted feature quantity to the feature quantity distribution; and a catalog updating unit that updates the catalog based on the feature quantity distribution after tuning.

A system including a deep learning processor obtains response data of at least two data types from a set of process stations performing operations as part of a manufacturing process. The system analyzes factory operation and control data to generate expected behavioral pattern data. Further, the system uses the response data to generate actual behavior pattern data for the process stations. Based on an analysis of the actual behavior pattern data in relation to the expected behavioral pattern data, the system determines whether anomalous activity has occurred as a result of the manufacturing process. If it is determined that anomalous activity has occurred, the system provides an indication of this anomalous activity.

A method of forming a component by an electroforming process using an electroforming apparatus is presented. The electroforming apparatus includes an anode, a cathode and an electrolyte including a metal salt. The method includes receiving a set of training electroforming process parameters; training a machine learning algorithm based on at least a subset of the set of training electroforming process parameters; generating a set of updated operating electroforming parameters from the trained machine learning algorithm; and operating the electroforming apparatus based on the set of updated operating electroforming parameters. The step of operating the electroforming apparatus includes applying an electric current between the anode and the cathode in the presence of the electrolyte and depositing a plurality of metal layers on a cathode surface to form the component. A system of forming a component is also presented.

Systems and methods for managing an additive manufacturing production line include an additive manufacturing machine having a first sensor and an auxiliary equipment having a second sensor. A server includes security protocols, a workflow module, an industrial Internet of things (IIoT) module and a machine learning module. The workflow module, IIoT module, machine learning module, additive manufacturing machine and auxiliary equipment are in communication with each other using the security protocols. The machine learning module processes feedback from the first sensor and the second sensor to control operation of the additive manufacturing machine through the workflow module and the IIoT module.

A machine learning device performs machine learning related to optimization of a compensation value of a compensation generation unit with respect to a servo control device that includes a compensation generation unit configured to generate a compensation value to be added to a control command for controlling a servo motor and a limiting unit configured to limit the compensation value or the control command to which the compensation value is added so as to fall within a setting range. During a machine learning operation, when the compensation value or the control command is outside the setting range and the limiting unit limits the compensation value or the control command so as to fall within the setting range, the machine learning device applies the compensation value to the learning and continues with a new search to optimize the compensation value generated by the compensation generation unit.

Operating a substrate processing system includes receiving a plurality of sets of training data, storing a plurality of machine learning models, storing a plurality of physical process models, receiving a selection of a machine learning model from the plurality of machine learning models and a selection of a physical process model from the plurality of physical process models, generating an implemented machine learning model according to the selected machine learning model, calculating a characterizing value for each training spectrum in each set of training data thereby generating a plurality of training characterizing values with each training characterizing value associated with one of the plurality of training spectra, training the implemented machine learning model using the plurality of training characterizing values and plurality of training spectra to generate a trained machine learning model, and passing the trained machine learning model to a control system of the substrate processing system.

A machine learning includes a state observation unit that observes, as state variables representing a current state of an environment, PID control parameter data indicating the a parameter of the PID control during machining, machining condition data indicating a machining condition of the machining, and machining environment data relating to a machining environment of the machining, a determination data acquisition unit that acquires, as determination data, tool life determination data indicating an appropriateness determination result relating to depletion of the life of a tool during the machining, and cycle time determination data indicating an appropriateness determination result relating to the cycle time of the machining, and a learning unit that learns the machining condition and the machining environment of the machining, and the parameter of the PID control in association with each other.

A process control method for manufacturing semiconductor devices, including determining a quality metric of a production semiconductor wafer by comparing production scatterometric spectra of a production structure of the production wafer with reference scatterometric spectra of a reference structure of reference semiconductor wafers, the production structure corresponding to the reference structure, the reference spectra linked by machine learning to a reference measurement value of the reference structure, determining a process control parameter value (PCPV) of a wafer processing step, the PCPV determined based on measurement of the production wafer and whose contribution to the PCPV is weighted with a first predefined weight based on the quality metric, and based on a measurement of a different wafer and whose contribution to the PCPV is weighted with a second predefined weight based on the quality metric, and controlling, with the PCPV, the processing step during fabrication.

A thermal displacement compensation apparatus for compensating a dimensional measurement error due to a thermal displacement of a workpiece, including a machine learning device for learning shape measurement data at the time of inspection of the workpiece, wherein the machine learning device observes image data showing the temperature distribution of the workpiece and shape data after machining as state variables representing the current state of the environment, acquires judgment data indicating the shape measurement data at the time of inspection, and learns the image data showing the temperature distribution of the workpiece and shape data after machining and the shape measurement data at the time of inspection in association with each other using the observed state variables and the acquired judgment data.

This learning device provides a learned model to an adjuster including the learned model learned to output a predetermined compensation amount to a controller based on parameters of an object to be processed, in a system including the controller outputting a command value obtained by compensating a target value based on a compensation amount; and a control object performing a predetermined process on the object and outputting a control variable as a response to the command value. The learning device includes: a learning part generating candidate compensation amounts based on operation data including a target value, command value and control variable, learning with the generated candidate compensation amounts and the parameters of the object as teacher data, and generating or updating the learned model; and a setting part providing, to the adjuster, the generated or updated learned model.