Embodiments of the present disclosure provide systems and methods for controlling a manufacturing process in a manner that protects sensitive information from misuse by different entities involved in the manufacturing process. According to the present disclosure, a blueprint providing information regarding subcomponents of a product to be manufactured may be provided to a synthesizer device. The synthesizer device may engage in two-party computation with IP providers to generate a set of machine commands, which may be encrypted, and then provide a message including the set of machine commands to a manufacturer device. The manufacturer device may obtain authorization from the IP provider(s) based on the message, where the authorization may enable the manufacturer device to configure a manufacturing process in accordance with the set of machine commands to manufacture the subcomponents of the product.

Methods, apparatus, systems and articles of manufacture are disclosed to improve boundary excursion detection. An example apparatus to improve boundary excursion detection includes a metadata extractor to parse a first control stream to extract embedded metadata, a metadata label resolver to classify a boundary term of the extracted embedded metadata, a candidate stream selector to identify candidate second control streams that include a boundary term that matches the classified boundary term of the first control stream, and a boundary vector calculator to improve boundary excursion detection by calculating a boundary vector factor based on respective ones of the candidate second control streams that include the classified boundary term.

A method for managing a life cycle of a complex engineering facility, comprising several steps. The steps include forming a facility structure of the facility; selecting constituent elements of the facility structure and the relationships between the constituent elements and a location of each of the constituent elements in a decomposition structure of the facility; forming a linked array of requirements related to the facility and to processes of implementation of the requirement for the facility; planning and accounting for the requirements in accordance with the structural decomposition of the facility, the requirements being assigned a certain status; and forming databases intended for storing an associated array of information about the constituent elements of the facility, the associated array of information comprising at least a plurality of documents related to design data and/or to supply and procurement data and/or to pre-commissioning data and/or operation data and/or facility configuration data.

To provide more test data during the manufacture of non-volatile memories and other integrated circuits, machine learning is used to generate virtual test values. Virtual test results are interpolated for one set of tests for devices on which the test is not performed based on correlations with other sets of tests. In one example, machine learning determines a correlation study between bad block values determined at die sort and photo-limited yield (PLY) values determined inline during processing. The correlation can be applied to interpolate virtual inline PLY data for all of the memory dies, allowing for more rapid feedback on the processing parameters for manufacturing the memory dies and making the manufacturing process more efficient and accurate. In another set of embodiments, the machine learning is used to extrapolate limited metrology (e.g., critical dimension) test data to all of the memory die through interpolated virtual metrology data values.

A virtual simulation manufacturing platform based on automatic control is provided. The platform comprises an integrated control system, a process treatment system, a virtual simulation system and a signal feedback system, wherein a data end of the integrated control system is connected to a data end of the process treatment system, the data end of the process treatment system is connected to a data end of the virtual simulation system, the data end of the virtual simulation system is connected to a data end of the signal feedback system. A physical workshop during the actual production process is connected to a virtual workshop on a computer by means of a digital twinning technology, and related information such as related process parameters and device parameters are completely displayed in the virtual simulation system in the virtual simulation manufacturing platform based on automatic control.

A method of defining a manufacturing operation for a workstation includes providing a selected manufacturing operation record from among a plurality of manufacturing operation records for a selected manufacturing operation to be executed in the workstation. The method further includes extracting, by a process allocation system, process element data for a plurality of process elements associated with the selected manufacturing operation record. The process element data includes a textual description of the respective process element and a process time. The method further includes determining, by the process allocation system, a predicted operation time for the selected manufacturing operation based on the process element data and a time prediction model, where the time prediction model is a trained model recognizing sequential patterns among the plurality of process elements of the selected manufacturing operation.

An engineering system for orchestration of an industrial plant includes: a modular plant to be orchestrated including at least one processor from a topology having: a process orchestration layer, and a plurality of modules. A portion of the plurality of modules are formed as at least one combined module. Each combined module of the at least one combined module has at least two different modules of the portion of the plurality of modules. The process orchestration layer controls the plurality of modules. The control by the process orchestration layer includes in-direct control of the portion of the plurality of modules via control of the at least one combined module.

An Industrial Internet of Things (IIoT) agent module or device preferably used as or in place of a Supervisory Control and Data Acquisition (SCADA) data node system and/or conventional SCADA system, which is operatively coupled and in communication with an IIoT cloud platform, so as to perform control and data acquisition operations and exchange data and commands automatically or in response to Inputs from the IIoT cloud platform; wherein all production details/settings/parameters, including process logic, control methodology, product recipe, and data point setup, can be dynamically changed based on decision/input/command of the IIoT cloud platform, such that the IIoT cloud platform might completely “re-configure/re-program” a software portion of the IIoT agent module or device governing its working behavior or characteristics by sending a reconfiguration/reprogramming information.

A data acquisition system for dielectric analysis measurements, including a sensor interface configured to connect to one or more sensors located within an active machining zone of an industrial manufacturing machine, a module processor coupled to the sensor interface and configured to receive measurement values from one or more sensors connected to the sensor interface, a cloud interface coupled to the module processor, and a machine interface coupled to the module processor. The measurement values indicate physical properties of workpieces processed in the active machining zone of an industrial manufacturing machine. The cloud interface is configured to connect to cloud-based resources, and the machine interface is configured to connect to a controller of the industrial manufacturing machine. The module processor is configured to transmit the received measurement values from the one or more dielectric sensors to cloud-based resources via the cloud interface and to transmit manufacturing control signals to the controller of the industrial manufacturing machine via the machine interface, the manufacturing control signals being based on parameters received from cloud-based resources via the cloud interface.

Various embodiments of the present technology generally relate to solutions for improving industrial automation programming and data science capabilities with machine learning. More specifically, embodiments include systems and methods for implementing machine learning engines within industrial programming and data science environments to improve performance, increase productivity, and add functionality. In an embodiment, a system comprises a machine learning-based analysis engine configured to perform an analysis of operational data from an industrial automation environment. The analysis engine is further configured to perform an analysis of control logic and identify, based on the analysis of the operational data and the analysis of the control logic, a variable that is in the control logic but is not used in the operational data. The system further comprises a notification component configured to surface a notification that the variable is in the control logic but is not used in the operational data.