A method for determining a correction relating to a performance metric of a semiconductor manufacturing process, the method including: obtaining a set of pre-process metrology data; processing the set of pre-process metrology data by decomposing the pre-process metrology data into one or more components which: a) correlate to the performance metric; or b) are at least partially correctable by a control process which is part of the semiconductor manufacturing process; and applying a trained model to the processed set of pre-process metrology data to determine the correction for the semiconductor manufacturing process.

Systems and methods are described herein for real-time data processing and for emergency planning. Scenario test data may be collected in real-time based on monitoring local or regional data to ascertain any anomaly phenomenon that may indicate an imminent danger or of concern. A computer-implemented method may include filtering a plurality of different test scenarios to identify a sub-set of test scenarios from the plurality of different test scenarios that may have similar behavior characteristics. A sub-set of test scenarios is provided to a trained neural network to identify one or more sub-set of test scenarios. The one or more identified sub-set of test scenarios may correspond to one or more anomaly test scenarios from the sub-set of test scenarios that is most likely to lead to an undesirable outcome. The neural network may be one of: a conventional neural network and a modular neural network.

An operation path generation unit (210) generates an operation quantity time series for an actuator (111) based on a measurement state quantity output from a state sensor (101). A predictive model unit (220) generates a state quantity predictive time series by calculating a predictive model by using as an input the measurement state quantity and the operation quantity time series. A neural network unit (230) corrects the state quantity predictive time series by performing arithmetic operation of a neural network, by using as an input a measurement environment quantity output from an environment sensor (102) and the state quantity predictive time series. A state quantity evaluation unit (240) generates an evaluation result for the state quantity time series after the correction. The operation path generation unit outputs an operation quantity at the head of the operation quantity time series to the actuator when the evaluation result fulfils an appropriate criterion.

Disclosed herein is technology for performing predictive modeling to identify inputs for a manufacturing process. An example method may include receiving expected output data for a manufacturing process, wherein the expected output data defines an attribute of an output of the manufacturing process; accessing a plurality of machine learning models that model the manufacturing process; determining, using a first machine learning model, input data for the manufacturing process based on the expected output data for the manufacturing process, wherein the input data comprises a value for a first input and a value for a second input; combining the input data determined using the first machine learning model with input data determined using the second machine learning model to produce a set of inputs for the manufacturing process, wherein the set of inputs comprises candidate values for the first input and candidate values for the second input.

A computer aided process for creation of a manufacturing facility, for production of a user-selected product, relies on a set of functional modules for specification of the facility's floorspace requirements, manufacturing equipment, and equipment layout to allow optimization of the facility for a production capacity specified by the user.

A textile mill system and associated method include a plurality of spinning mills each having textile machines. A computer system determines adapted machine parameters for the textile machines and processes within the spinning mills. The computer system includes a receiving and transmitting section configured to receive operational information from the spinning mills and the textile machines, and a first database configured to store the received operational information. A processing section includes an optimizer section with a neural network, wherein the neural network uses the operational information stored in the first database with processes for or derived from supervised or unsupervised machine or deep learning to determine the adapted machine parameters.

The present invention discloses a prediction control method and system for component contents in a rare earth extraction process. The prediction control method includes: establishing an Elman neural network model of a rare earth extraction process; obtaining a predicted output value of the rare earth extraction process through the Elman neural network model of the rare earth extraction process; calculating an optimal set value through steady-state optimization; dynamically predicting an extractant flow increment and a detergent flow increment based on the predicted output value and the optimal set value; and controlling component contents in the rare earth extraction process according to the extractant flow increment and the detergent flow increment. According to the present invention, an optimal setting problem of a set point is solved through steady-state optimization calculation, and then an optimal control effect is achieved in combination with a dynamic prediction control method, thereby achieving optimal setting control over the component contents in the rare earth extraction process, and ensuring the product quality of the rare earth extraction process.

Implementations described herein generally relate to a method for detecting anomalies in time-series traces received from sensors of manufacturing tools. A server feeds a set of training time-series traces to a neural network configured to derive a model of the training time-series traces that minimizes reconstruction error of the training time-series traces. The server extracts a set of input time-series traces from one or more sensors associated with one or more manufacturing tools configured to produce a silicon substrate. The server feeds the set of input time-series traces to the trained neural network to produce a set of output time series traces reconstructed based on the model. The server calculates a mean square error between a first input time series trace of the set of input time series traces and a corresponding first output time series trace of the set of output time-series traces. The server declares the sensor corresponding to the first input time-series trace as having an anomaly when the mean square error exceeds a pre-determined value.

Robots, including robot arms, can interface with other modules to affect the world surrounding the robot. However, designing modules from scratch when human analogues exist is not efficient. In an embodiment, a mechanical tool, converted from human use, to be used by robots includes a monolithic adaptor having two interface components. The two interface components include a first interface component cabal be of mating with an actuated mechanism on the robot side, the second interface capable of clamping to an existing utensil. In such a way, utensils that are intended for humans can be adapted for robots and robotic arms.

In some embodiments, identifying a replay attack in an industrial control system of an industrial asset includes receiving a first set of time series data associated with an ambient condition of one or more first monitoring nodes at a first location of the industrial control system. An actual system feature value for the industrial asset is determined based upon the first set of time series data. A second set of time series data indicative of the ambient condition at a second location is received, and a nominal system feature value is determined based upon the second set of time series data. A correlation between the actual feature value and the nominal system feature value is analyzed to determine a correlation result. A request received by the industrial control system is selectively categorized as a replay attack based upon the correlation result.