Robust machine learning predictions. Temporal dependencies of process targets for different machine learning models can be captured and evaluated for the impact on process performance for target. The most robust of these different models is selected for deployment based on minimizing variance for the desired performance characteristic.

Controlling a conveyor installation of a real warehouse having automated machines and persons that are virtualized in a central computer for storing a virtual model of the conveyor installation having the dimensions of the individual conveyor components and the movement parameters thereof. Images of the objects to be conveyed, automated machines and persons in the conveyor installation are captured by sensors at predefined short time intervals and identified by image recognition, and the positions thereof in the conveyor installation are determined. The virtual model is continuously updated with the identification and position determination of the objects in the central computer such that a virtualized real-time model is generated, and the real conveyor installation is centrally controlled with the aid of the model, where material flow control commands are generated for the real actuators for controlling the conveying movement of the automated machines to avoid endangering the persons.

Provided is an optimization system capable of creating a large amount of data for optimization and specifying values of control variables in order to acquire an optimum result in consideration of uncertainty of predictive values. A simulation means 2 is given a model which is information modeling an object to be analyzed therein and including a parameter containing predictive values and their error ranges, control variables and an objective variable, determines values of the control variables per simulation for specifying a value of the objective variable, and conducts simulation multiple times based on the model. Further, the simulation means 2 determines definite values of the predictive values based on a random number and the parameter per simulation, and conducts simulation by use of values of the control variables and definite values of the predictive values. A control variable value specification means 3 specifies values of the control variables when the objective variable takes an optimum value.

Various embodiments of the teachings herein include methods for transforming an industrial standard specification into an instantiated rule for amending an information model of a component of an industrial automation control system. An example method may include: loading a text corpus pertaining to the specification into a memory; semantically processing the text corpus to identify a textual rule block and extract the textual rule block; classifying the textual rule block into a plurality of rule categories to obtain a classified textual rule block; assigning a rule template to the classified textual rule block, wherein assigning is driven by one of the plurality of rule categories assigned to the classified textual rule block; and instantiating the rule template adapted by constraints of the information model of the industrial automation control system to obtain the instantiated rule.

An energy monitoring system includes a memory storing instructions to execute an energy modeling technique and processing circuitry for executing the instructions to operate the energy modeling technique. The energy modeling technique includes receiving energy data from a plurality of segments representative of one or more logical subgroups. The energy modeling technique includes categorizing the energy data of the logical subgroups into a plurality of segments. The energy modeling technique includes organizing the plurality of segments into a plurality of state-based hierarchical levels. The energy modeling technique includes calculating energy usage and factors associated with the plurality of state-based hierarchical levels via an energy model. The energy modeling technique includes outputting a visualization representative of the energy data corresponding to each of the segments to a monitoring and control system, resulting in a graphical representation accessible by a user-viewable screen.

Methods, systems, and computer programs are presented for predicting the performance of semiconductor manufacturing equipment operations. One method includes an operation for obtaining machine-learning (ML) models, each model related to predicting a performance metric for an operation of a semiconductor manufacturing tool. Further, each ML model utilizes features defining inputs for the ML model. The method further includes an operation for receiving a process definition for manufacturing a product with the semiconductor manufacturing tool. One or more ML models are utilized to estimate a performance of the process definition used in the semiconductor manufacturing tool. Additionally, the method includes presenting, on a display, results showing the estimate of the performance of the manufacturing of the product. In some aspects, the use of hybrid models improves the predictive accuracy of the system by augmenting the capabilities of data-driven models with the reinforcement provided by the physics-based models.

Generating a digital representation of a process automation system on a cloud-based service platform uses assets integrated into measurement points. The method includes reading TAG information using an edge device, wherein the TAG information is provided in a character chain data type and represents the hierarchical structure of the respective asset. The method also includes transmitting the TAG information to the cloud-based service platform, and parsing the TAG information using an application, wherein a logic is used for the parsing process, and the name of the asset and the name of the measurement point in which the respective asset is integrated are extracted from the TAG information. A structure plan of the system is generated using the application having all of the system measurement points extracted from the TAG information together with all of the assets which are assigned to the measurement points and are extracted from the TAG information.

A computer-implemented method builds and uses a reduced-order model of a semiconductor manufacturing chamber. The method generates a design-of-experiments matrix that defines combinations of chamber parameters. It executes physics-based simulations to obtain training data for the combinations. The training data includes physical quantities and derived values. The method trains a machine-learning algorithm using the training data to create the reduced-order model. The reduced order model maps the chamber parameters to predicted performance metrics. The method receives a chamber configuration as input to the reduced-order model. It generates predicted performance metrics for the configuration. The method controls at least one process recipe of the chamber based on the predicted metrics. The method presents results on a display. The method stores an updated process recipe definition in a hardware-based memory. The method transmits the updated process recipe definition to a controller of the chamber for execution.