The program code, when executed by a processor, causes the processor to input fabrication data including a plurality of parameters associated with a semiconductor fabricating process to a framework to generate a first class for analyzing the fabrication data, to extract a first parameter targeted for analysis and a second parameter associated with the first parameter from the plurality of parameters and generate a second class for analyzing the first parameter as a sub class of the first class, to modify the first parameter and the second parameter into a data structure having a format appropriate to store in the second class, so as to be stored in the second class, to perform data analysis on the first parameter and the second parameter, to transform the first parameter and the second parameter into corresponding tensor data, and to input the tensor data to the machine learning model.

Systems and methods in automated systems for reconciling conflicting measurements, optimizing operations, and, when a system operator overrides system parameters, reducing errors.

Provided are a system, method, and computer program product for optimizing a manufacturing process. The method includes receiving manufacturing data associated with a manufacturing process for manufacturing a product. The manufacturing data may include data from a plurality of data sources associated with a plurality of stages of the manufacturing process, and the manufacturing data may include values for a plurality of parameters including at least one process parameter value and at least one quality parameter value. The method includes generating a time-sequenced data structure including the manufacturing data and transforming the time-sequenced data structure to a positionally-dimensioned data structure based on timing data associated with the plurality of stages. The method includes determining a new value for the at least one process parameter value based on the positionally-dimensioned data structure and at least one algorithm and optimizing the manufacturing process based on the new value.

A system and method include dividing, by a processor of a manufacturing execution system (MES), a time axis associated with a time window into a plurality of time slots, assigning an integer value to an integer variable indexed by a slot identifier, a machine identifier, and a wafer lot identifier, specifying one or more constraints based on the integer variable, wherein the one or more constraints comprise a wafer quantity constraint and a Q-time constraint, executing an optimization solver under the one or more constraints to determine a time and a quantity of wafer lots to be provided to each machine associated with the time window, and issuing a request to a controller to cause provision of the quantity of wafer lots to each machine associated with each step in the time window.

The production management support system analyzes, on the basis of one or more attentional perspectives on a plurality of different models of products, past record information that includes information as a past record which shows, for each product loaded in a production system in which the plurality of different models of products are loaded and a sequential order of two or more of a plurality of steps is different depending on the model, an execution time of each of the steps and that serves as a basis for a production management chart showing a production situation, to detect, among display objects displayed in the production management chart, a display object satisfying one or more requirements associated with the one or more attentional perspectives. The system performs accentuated display on at least one of the detected display objects.

A method for automatically determining an optimized process configuration of a process for manufacturing or processing products that can be executed using a technical system and can be configured using a number of different process configuration parameters comprises: determining a process configuration of the process that is optimized with regard to a defined metric and is defined by respective target values of process configuration parameters using an optimization method that is adapted to the process and is at least partially based on machine learning, using input data that include production data and features that are given by historical process configuration data and status data of the system or process or are derived therefrom; and outputting target process configuration data representing the determined optimized process configuration by means of the target values of the process configuration parameters.

A method for optimizing a sequence of processes for manufacturing of product units, includes: associating measurement results of performance parameters (e.g., fingerprints) with the recorded process characteristics (e.g., context); obtaining a characteristic (e.g., context) of a previous process (e.g. deposition) in the sequence already performed on a product unit; obtaining a characteristic (e.g., context) of a subsequent process (e.g., exposure) in the sequence to be performed on the product unit; determining a predicted performance parameter (e.g., fingerprint) of the product unit associated with the sequence of previous and subsequent processes by using the obtained characteristics to retrieve measurement results of the performance parameters (e.g., fingerprints) corresponding to the recorded characteristics; and determining corrections to be applied to future processes (e.g. exposure, etch) in the sequence to be performed on the product unit, based on the determined predicted performance parameter.

A controller is provided for building equipment including a plurality of devices that operate in parallel to affect an environmental condition of a building. The controller includes one or more processing circuits including one or more processors and memory. The memory store instructions that, when executed by the one or more processors, cause the one or more processors to perform operations. The operations include lowering an upper bound or raising a lower bound of one or more constraints based on a minimum off schedule or a minimum on schedule for the building equipment, performing an optimization of an objective function subject to the one or more constraints to generate control decisions for the building equipment, and operating the building equipment in accordance with the control decisions to affect the environmental condition of the building.

A control device, a system and methods for optimizing and providing movement profiles, wherein the movement profiles serve for determining the movement of tools of a press and the movement of a receiving element for a workpiece of a transfer system, where transfer movement profiles are synchronized with one another via press movement profiles, the synchronization of the transfer movement profiles particularly occurs by chronologically shifting synchronization points via boundary conditions such that an offset of the press movement profiles can be determined via the synchronization, so that the workpiece can be processed as quickly as possible through the system.

The program code, when executed by a processor, causes the processor to input fabrication data including a plurality of parameters associated with a semiconductor fabricating process to a framework to generate a first class for analyzing the fabrication data, to extract a first parameter targeted for analysis and a second parameter associated with the first parameter from the plurality of parameters and generate a second class for analyzing the first parameter as a sub class of the first class, to modify the first parameter and the second parameter into a data structure having a format appropriate to store in the second class, so as to be stored in the second class, to perform data analysis on the first parameter and the second parameter, to transform the first parameter and the second parameter into corresponding tensor data, and to input the tensor data to the machine learning model.