The present invention can detect early an abnormality or signs of abnormality in a workpiece. A sensor system 1 is provided with: a first sensor 30a that measures a workpiece; a second sensor 30b that measures the workpiece in a relatively longer cycle than the first sensor 30a; and a master unit 10. The master unit 10 includes: an acquisition unit 11 that acquires data measured by the first sensor 30a and data measured by the second sensor 30b; and a generation unit 12 that generates learning data which is used for machine learning of a learning model and in which the acquired data of the first sensor 30a is regarded as input data and the acquired data of the second sensor 30b is regarded as label data indicating a property of the input data.

A system includes a processing device, operatively coupled to the memory device, to perform operations comprising obtaining a plurality of sensor values associated with a deposition process performed, according to a recipe, in a process chamber to deposit film on a surface of a substrate; generating a manufacturing data graph based on the plurality of sensor values; receiving, via a user interface, a selection of a data point on the manufacturing graph; receiving failure data associated with the data point; and storing, in a data structure, the failure data to be accessible via the user interface presenting the manufacturing data graph.

A sensor monitors a treatment status of a predetermined manufacturing device, and an abnormality detection device detects an abnormality of a sensor signal that is a monitoring result of the sensor. The sensor signal is a digital data group obtained by sampling an analog waveform at a predetermined sampling period. A management apparatus learns characteristics of a plurality of digital data groups accumulated in past times through use of artificial intelligence to generate a learned model. An abnormality detection device holds the learned model and determines whether an abnormality is present in the digital data group of a current processing target by using the learned model.

To provide a numerical controller and a machine learning device that predict an abnormality, based on machine learning with perception of temporal change in data. The numerical controller includes the machine learning device provided with a learning unit that conducts machine learning of trends in operation of a machine on occasions of occurrence of abnormalities in the machine, based on time-series data acquired by a data logger device and relating to the operation of the machine and abnormality information relating to the abnormalities which have occurred in the machine and a prediction unit that predicts an abnormality which will occur in the machine, based on results of the machine learning in the learning unit and time-series data acquired by the data logger device and relating to current operation of the machine.

A sensor monitors a treatment status of a predetermined manufacturing device, and an abnormality detection device detects an abnormality of a sensor signal that is a monitoring result of the sensor. The sensor signal is a digital data group obtained by sampling an analog waveform at a predetermined sampling period. A management apparatus learns characteristics of a plurality of digital data groups accumulated in past times through use of artificial intelligence to generate a learned model. An abnormality detection device holds the learned model and determines whether an abnormality is present in the digital data group of a current processing target by using the learned model.

The invention relates to a method and a computer-implemented system for automatically monitoring and determining the status of entire process sections in a process unit in a computer-implemented manner.

A sensor monitors a treatment status of a predetermined manufacturing device, and an abnormality detection device detects an abnormality of a sensor signal that is a monitoring result of the sensor. The sensor signal is a digital data group obtained by sampling an analog waveform at a predetermined sampling period. A management apparatus learns characteristics of a plurality of digital data groups accumulated in past times through use of artificial intelligence to generate a learned model. An abnormality detection device holds the learned model and determines whether an abnormality is present in the digital data group of a current processing target by using the learned model.

The invention relates to a method and a computer-implemented system for automatically monitoring and determining the status of entire process sections in a process unit in a computer-implemented manner

A method and a computer-implemented system for automatically monitors and determines the status of entire process sections in a process unit in a computer-implemented manner.

A sensor monitors a treatment status of a predetermined manufacturing device, and an abnormality detection device detects an abnormality of a sensor signal that is a monitoring result of the sensor. The sensor signal is a digital data group obtained by sampling an analog waveform at a predetermined sampling period. A management apparatus learns characteristics of a plurality of digital data groups accumulated in past times through use of artificial intelligence to generate a learned model. An abnormality detection device holds the learned model and determines whether an abnormality is present in the digital data group of a current processing target by using the learned model.