Systems and methods for controlling device performance variability during manufacturing of a device on wafers are disclosed. The system includes a process platform, on-board metrology (OBM) tools, and a first server that stores a machine-learning based process control model. The first server combines virtual metrology (VM) data and OBM data to predict a spatial distribution of one or more dimensions of interest on a wafer. The system further comprises an in-line metrology tool, such as SEM, to measure the one or more dimensions of interest on a subset of wafers sampled from each lot. A second server having a machine-learning engine receives from the first server the predicted spatial distribution of the one or more dimensions of interest based on VM and OBM, and also receives SEM metrology data, and updates the process control model periodically (e.g., to account for chamber-to-chamber variability) using machine learning techniques.

A pick and place nozzle performance analytics system streams production data from pick and place machines used in electronic assembly to a cloud platform as torrential data streams, and performs analytics on the production data to track, visualize, and predict performance of individual nozzles in terms of rejects or miss-picks. The analytics system generates a performance vector for each nozzle based on the collected production data, the performance vector tracking both the accumulated rejects and the percentage of rejects as respective dimensions of an x-y plane. The system monitors and analyzes the trajectory of this vector in the x-y plane to predict when performance degradation of the nozzle will reach a critical threshold. In response to predicting that nozzle performance degradation will exceed a threshold at a future time, the system can generate and deliver notifications to appropriate client devices.

An engineering support system that supports engineering of a process control system, includes: a storage; and a processor connected to the storage and that: transforms design drawings into semantic models and outputs the semantic models to the storage; and generates a combined semantic model by combining the semantic models based on a degree of similarity among the semantic models and outputs the combined semantic model to the storage, wherein each of the semantic models is expressed by first information indicating elements included in the design drawings and second information indicating a relationship between the elements.

Provided is a control device for controlling an operation of a substrate processing apparatus that forms a predetermined film on a substrate and operations of a plurality of measurement devices that measure a characteristic of the predetermined film. The control device includes: an individual difference information storing unit that stores individual difference information representing a relationship between information allocated to each of the plurality of measurement devices to specify each measurement device and an individual difference of the measurement device; and a controller that corrects a measurement value of the characteristic of the predetermined film measured by the measurement device based on information specifying the measurement device that has measured the characteristic of the predetermined film and the individual difference information stored in the individual difference information storing unit.

Techniques are described for receiving manufacturing data and events over real time and non-real time interfaces and associating the data with one another. In one example, real time data is received, the real time data associated with a counter value assigned by a precision counter. The received real time data is stored in a storage buffer, and non-real time data is received, where the non-real time data associated with a counter value assigned by the precision counter. In response to receiving the non-real time data, the buffer is searched for real time data having a matching counter value and, in response to identifying stored real time data associated with a matching counter value, the non-real time data is associated with the real time data based on the match. Data packages are generated including related real time and non-real time data associated with matching counter values.

A method includes obtaining a model associated with a model-based controller in an industrial process having multiple actuator arrays and performing temporal tuning of the controller. The temporal tuning includes adjusting one or more parameters of a multivariable filter used to smooth reference trajectories of actuator profiles of the actuator arrays. The temporal tuning could also include obtaining one or more uncertainty specifications for one or more temporal parameters of the model, obtaining one or more overshoot limits for the actuator profiles, identifying a minimum bound for profile trajectory tuning parameters, and identifying one or more of the profile trajectory tuning parameters that minimize one or more measurement settling times without exceeding the one or more overshoot limits. The controller could be configured to use the adjusted parameter(s) during control of the industrial process such that the adjusting of the parameter(s) alters operation of the controller and the industrial process.

There is provided a method for optimizing a manufacturing process of a new part. The method includes executing, by a system configured to drive the manufacturing process, a set of manufacturing functions. Executing these functions include receiving data associated with one or more field parts similar to the new part, and generating, based on the data, a forecast representative of a longevity of the one or more parts. The method further includes generating a digital thread forming a surrogate model for the new part, based on the forecast. Further, the method includes creating the set of manufacturing functions based on the surrogate model and manufacturing the new part according to the set of manufacturing functions.

A method for real-time damage prediction includes obtaining a damage prediction model that mathematically models expected damage to equipment in an industrial process based on a plurality of process parameters. The method also includes obtaining real-time state information for at least one of the plurality of process parameters. The method further includes determining, based on the real-time state information and the damage prediction model, a real-time prediction of damage to at least one component of the equipment in the industrial process. The method may also include obtaining historical data for the plurality of process parameters, and the real-time prediction of damage can be based on the historical data, the real-time state information, and the damage prediction model. The method may further include identifying and adjusting a high limit and a low limit for the at least one of the plurality of process parameters.

According to some embodiments, system and methods are provided, comprising a flow module to receive one or more objectives for a flow in the production of an object; a memory for storing program instructions; a flow processor, coupled to the memory, and in communication with the flow module and operative to execute program instructions to: receive a request from a user for a current state of a flow for the object; retrieve metadata and data from the physical world associated with the object; determine a current phase in the flow for the object; select an analytic model to determine the current state of the flow for the object; instantiate a digital twin for the current phase in the flow for the object; execute the selected analytic for the instantiated digital twin; generate a response to the request including the current state of the flow for the object based on the executed analytic; and display the response on a display. Numerous other aspects are provided.

A pick and place nozzle performance analytics system streams production data from pick and place machines used in electronic assembly to a cloud platform as torrential data streams, and performs analytics on the production data to track, visualize, and predict performance of individual nozzles in terms of rejects or miss-picks. The analytics system generates a performance vector for each nozzle based on the collected production data, the performance vector tracking both the accumulated rejects and the percentage of rejects as respective dimensions of an x-y plane. The system monitors and analyzes the trajectory of this vector in the x-y plane to predict when performance degradation of the nozzle will reach a critical threshold. In response to predicting that nozzle performance degradation will exceed a threshold at a future time, the system can generate and deliver notifications to appropriate client devices.