A system includes a memory and a processing device, operatively coupled to the memory, to perform operations including receiving, as input to a trained machine learning model for identifying defect impact with respect to at least one type defect type, data associated with a process related to electronic device manufacturing. The data associated with the process comprises at least one of: an input set of recipe settings for processing a component, a set of desired characteristics to be achieved by processing the component, or a set of constraints specifying an allowable range for each setting of the set of recipe settings. The operations further include obtaining an output by applying the data associated with the process to the trained machine learning model. The output is representative of the defect impact with respect to the at least one defect type.

A method includes receiving a set of feature models, each feature model of the set of feature models corresponding to a respective feature associated with processing of a component, receiving a set of target properties for processing the component, where the set of target properties includes, for each feature, a respective target, determining, based on the set of feature models, one or more sets of predicted processing parameters in view of the set of target properties, generating one or more candidate process recipes each corresponding to a respective one of the one or more sets of predicted processing parameters, where the one or more candidate process recipes each correspond to a set of predicted properties including, for each feature, a respective predicted property value resulting from component processing, and selecting, from the one or more candidate process recipes, a process recipe for processing the component.

Real-time intervention of an industrial process can include searching for a batch of candidate configurations for use by the industrial process, the batch of candidate configurations searched for by performing a batch Bayesian optimization (BBO). The batch of candidate configurations is transmitted to the industrial process to use in running the industrial process. A result of the run is received from the industrial process. Using the result in the BBO, a next batch of candidate configurations is searched. Whether a stopping criterion is met is determined, based on the next batch of candidate configurations and by applying a function to a BBO acquisition score. Responsive to determining that the stopping criterion is met, searching for the next batch of candidates is terminated.

A system and method for implementing a control process within a process control system and resolving inconsistencies during execution of the control process includes loading the logical structure of the control process, loading a plurality of instantiation objects or processes when the control process is instantiated, using the instantiation objects to instantiate a procedural element of the control process as the control process calls for the procedural element during execution, executing the procedural element as part of the control process, and deconstructing the procedural element as execution of the procedural element is completed during execution of the control process. Resolution of inconsistencies includes executing a first model of an entity in a controller, executing a second model of the entity in an execution engine, detecting a difference between the models, generating a prompt and receiving an operation instruction to continue the process or abort the process.

A substrate processing apparatus includes an operating unit for transmitting apparatus data to a memory, the apparatus data being required while a recipe for processing a substrate is executed; and a data matching unit for comparing the apparatus data stored in the memory. When an error occurs in the substrate processing apparatus, the operating unit transmits data representing the error to the data matching unit. The data matching unit includes: a selection unit for selecting first apparatus data which was acquired when the recipe was executed without an occurrence of the error, and stored in the memory; an acquisition unit for acquiring first and second apparatus data from the memory, the first apparatus data being acquired when an error did not occur and the second apparatus data being acquired when an error occurred; and a calculation unit for comparing the first and second apparatus data and calculating a difference therebetween.

A method and system for computerized coordination of multiple operations to be performed by components of machines are provided. The computer system includes a memory device for storing data and a computer-controlled machine that includes a processor in communication with the memory device wherein the processor is programmed to read a recipe file from the memory device, the recipe file including operating parameter values for controlling the operation of the machine, extract a name of a meta-recipe file from the recipe file, the meta-recipe file including a first portion including parameter properties of operating parameter values used by the meta-recipe file, receive values for the meta-recipe having the parameter properties specified in the first portion, and operate the machine using code from a second portion of the meta-recipe and the received values.

Real-time intervention of an industrial process can include searching for a batch of candidate configurations for use by the industrial process, the batch of candidate configurations searched for by performing a batch Bayesian optimization (BBO). The batch of candidate configurations is transmitted to the industrial process to use in running the industrial process. A result of the run is received from the industrial process. Using the result in the BBO, a next batch of candidate configurations is searched. Whether a stopping criterion is met is determined, based on the next batch of candidate configurations and by applying a function to a BBO acquisition score. Responsive to determining that the stopping criterion is met, searching for the next batch of candidates is terminated.

Embodiments disclosed herein include methods of processing substrates in a tool. In an embodiment, the method of processing the substrate in the tool, comprises receiving an augmented recipe with a machine learning (ML) and/or an artificial intelligence (AI) module. In an embodiment, the augmented recipe comprises, a recipe for processing the substrate in the tool, and a matrix identifier that corresponds to one or more substrate properties. In an embodiment the method further comprises using the ML and/or AI module to retrieve a data set from a database, where the data set is associated with the matrix identifier, and using the ML and/or AI module to modify the augmented recipe to form a modified recipe, where the modification is dependent on the data set.

A method and system are provided for distributing master recipes for process control systems comprising storing a single master recipe on a server having an enterprise database and communicating said single master recipe to each of a plurality of manufacturing units. Control recipes are seamlessly created using common master recipes across multiple process plants to deliver consistent product quality with enough flexibility and interoperability in accordance with ISA S88 batch standards.

A substrate processing apparatus includes an operating unit for transmitting apparatus data to a memory, the apparatus data being required while a recipe for processing a substrate is executed; and a data matching unit for comparing the apparatus data stored in the memory. When an error occurs in the substrate processing apparatus, the operating unit transmits data representing the error to the data matching unit. The data matching unit includes: a selection unit for selecting first apparatus data which was acquired when the recipe was executed without an occurrence of the error, and stored in the memory; an acquisition unit for acquiring first and second apparatus data from the memory, the first apparatus data being acquired when an error did not occur and the second apparatus data being acquired when an error occurred; and a calculation unit for comparing the first and second apparatus data and calculating a difference therebetween.