Abstract: An exemplary embodiment of the present disclosure discloses a method of setting a model threshold value for detecting an anomaly of a facility monitoring system, the method including: acquiring sensor data output from each sensor; extracting a feature value for the sensor data of each sensor; acquiring output data by inputting input data including the extracted feature value to a trained neural network model; and comparing the input data and the output data and setting a threshold value for detecting an anomaly based on a calculated comparison result value.

Abstract: An exemplary embodiment of the present disclosure discloses a method of setting a model threshold value for detecting an anomaly of a facility monitoring system, the method including: acquiring sensor data output from each sensor; extracting a feature value for the sensor data of each sensor; acquiring output data by inputting input data including the extracted feature value to a trained neural network model; and comparing the input data and the output data and setting a threshold value for detecting an anomaly based on a calculated comparison result value.

Abstract: Disclosed is a method of updating a model of a facility monitoring system, the method including: checking occurrence of an event; when the event occurs, retraining a neural network model that performs a failure diagnosis; extracting a previous threshold value of the neural network model; calculating a new threshold value based on the extracted previous threshold value and a median value of state variables calculated through the retraining of the neural network model; and updating the threshold value of the neural network model based on the new threshold value.

Abstract: An exemplary embodiment of the present disclosure discloses a method of setting a model threshold value for detecting an anomaly of a facility monitoring system, the method including: acquiring sensor data output from each sensor; extracting a feature value for the sensor data of each sensor; acquiring output data by inputting input data including the extracted feature value to a trained neural network model; and comparing the input data and the output data and setting a threshold value for detecting an anomaly based on a calculated comparison result value.

Abstract: An exemplary embodiment of the present disclosure discloses a method of classifying facility failure of a facility monitoring system, the method including: acquiring sensor data output from each sensor; acquiring output data by inputting input data including the sensor data of each sensor to a trained neural network model; calculating a comparison result value by comparing the input data and the output data; diagnosing failure based on specific parameters included in the comparison result value; and classifying failure corresponding to the input data based on a combination of the specific parameters.

Abstract: In a method for controlling a processing apparatus, an error value between an input value of the processing apparatus for processing a subject to be processed, and a measurement value obtained by measuring the subject being processed is obtained. A correction value is computed for correcting the input value of the processing apparatus in the direction of decreasing the error value, and the values are managed as processing data to be utilized in computing a next correction value. Previous processing data having a history identical to that of the subject loaded to the processing apparatus is searched, and a current bias correction value is predicted from a plurality of most recent correction values having the identical history. Also, a current random correction value is predicted by means of a neural network on the basis of a plurality of most recent random correction values. The predicted bias correction value is summed with the random correction value as a current correction value of the processing apparatus.

Abstract: In a method for controlling a processing apparatus, an error value between an input value of the processing apparatus for processing a subject to be processed, and a measurement value obtained by measuring the subject being processed is obtained. A correction value is computed for correcting the input value of the processing apparatus in the direction of decreasing the error value, and the values are managed as processing data to be utilized in computing a next correction value. Previous processing data having a history identical to that of the subject loaded to the processing apparatus is searched, and a current bias correction value is predicted from a plurality of most recent correction values having the identical history. Also, a current random correction value is predicted by means of a neural network on the basis of a plurality of most recent random correction values. The predicted bias correction value is summed with the random correction value as a current correction value of the processing apparatus.

Abstract: A method of controlling thicknesses of thin film layers in manufacturing semiconductor devices begins with loading monitoring wafers in a thin film forming apparatus. The apparatus has multiple film formation zones, and one of the zones is a reference zone. After forming thin films on the monitoring wafers, thicknesses of the thin films formed on the monitoring wafers are measured. Then, process time and process temperatures are adjusted so that the thicknesses of films are the same as a target film thickness. Finally, thin films are formed on semiconductor wafers using the adjusted process time and temperatures.