Systems and methods of optimizing wafer transport and metrology measurements in a fab are provided. Methods comprise deriving and updating dynamic sampling plans that provide wafer-specific measurement sites and conditions, deriving optimized wafer measurement paths for metrology measurements of the wafers that correspond to the dynamic sampling plan, managing FOUP (Front Opening Unified Pod) transport through the fab, transporting wafers to measurement tools while providing the dynamic sampling plans and the wafer measurement paths to the respective measurement tools before or as the FOUPs with the respective wafers are transported thereto, and carrying out metrology and/or inspection measurements of the respective wafers by the respective measurement tools according to the derived wafer measurement paths. Quantum computing resources may be used to solve the corresponding specific optimization problems, to reduce the required time, improve the calculated solutions and improve the fab yield and accuracy of the produced wafers.

Method for controlling a production process for producing components, wherein the components or a manufacturing device used to produce the components has/have at least one first feature which can be captured using metrology; at least having the following steps: a) determining a test plan for capturing the first feature with a first value of a first test frequency, wherein at least one first stability criterion is defined for the first feature; b) producing the components and carrying out the test plan in a parallel manner, wherein the first feature is tested at the first test frequency; c) evaluating the test results; d) changing the first test frequency, wherein the first test frequency is increased if at least one test result for the first feature violates the first stability criterion; wherein the first test frequency is reduced if all test results are in accordance with the first stability criterion.

A method includes accessing feature values representing a historical population of assembly units assembled on an assembly line; and accessing a failure status of the assembly unit at a target test on the assembly line. The method also includes, for each feature: deriving a correlation between values of the feature and failure status at the target test; deriving an effective limit of the feature based on scope of feature values in the historical population of assembly units; and calculating an action score for the feature based on the correlation and a width of the effective limit. The method further includes: selecting a particular feature exhibiting greatest action score; defining a preemptive test for the particular feature upstream of the target test during a next assembly period; and assigning a target limit, narrower than an effective limit of the particular feature, to the preemptive test.

A method for controlling a production process for components, wherein the components or a production device used for producing the components have or has features which are metrologically detectable. The method comprising specifying a test plan for detecting primary feature(s) and secondary feature(s) by tests, wherein the primary feature(s) is/are measured at a first test frequency and the secondary feature(s) is/are measured at a second test frequency, wherein at least one stability criterion is defined for the primary feature(s); producing the components and carrying out the test plan for producing test results in parallel, wherein solely the primary feature(s) is/are tested at the first test frequency; evaluating the determined test results; and, if at least one test result for the primary feature(s) violates the stability criterion, continuing the carrying out of the test plan, wherein at least a secondary feature is tested.

Systems and methods of optimizing wafer transport and metrology measurements in a fab are provided. Methods comprise deriving and updating dynamic sampling plans that provide wafer-specific measurement sites and conditions, deriving optimized wafer measurement paths for metrology measurements of the wafers that correspond to the dynamic sampling plan, managing FOUP (Front Opening Unified Pod) transport through the fab, transporting wafers to measurement tools while providing the dynamic sampling plans and the wafer measurement paths to the respective measurement tools before or as the FOUPs with the respective wafers are transported thereto, and carrying out metrology and/or inspection measurements of the respective wafers by the respective measurement tools according to the derived wafer measurement paths. Quantum computing resources may be used to solve the corresponding specific optimization problems, to reduce the required time, improve the calculated solutions and improve the fab yield and accuracy of the produced wafers.

The present teachings relate to a method for improving a production process for manufacturing a chemical product at an industrial plant comprising at least one equipment and one or more computing units, and the product being manufactured by processing at least one input material, which method comprises: receiving real-time process data from the equipment; determining a subset of the real-time process data; computing at least one state related to the input material and/or the equipment. The present teachings also relate to a system for improving the production process, a use, and a software program.

A system for auto defect screening using adaptive machine learning includes an adaptive model controller, a defect/nuisance library and a module for executing data modeling analytics. The adaptive model controller has a feed-forward path for receiving a plurality of defect candidates in wafer inspection, and a feedback path for receiving defects of interest already screened by one or more existing defect screening models after wafer inspection. The adaptive model controller selects data samples from the received data, interfaces with scanning electron microscope (SEM) review/inspection to acquire corresponding SEM results that validate if each data sample is a real defect or nuisance, and compiles model training and validation data. The module of executing data modeling analytics is adaptively controlled by the adaptive model controller to generate and validate one or more updated defect screening models using the model training and validation data according to a target specification.

A system for auto defect screening using adaptive machine learning includes an adaptive model controller, a defect/nuisance library and a module for executing data modeling analytics. The adaptive model controller has a feed-forward path for receiving a plurality of defect candidates in wafer inspection, and a feedback path for receiving defects of interest already screened by one or more existing defect screening models after wafer inspection. The adaptive model controller selects data samples from the received data, interfaces with scanning electron microscope (SEM) review/inspection to acquire corresponding SEM results that validate if each data sample is a real defect or nuisance, and compiles model training and validation data. The module of executing data modeling analytics is adaptively controlled by the adaptive model controller to generate and validate one or more updated defect screening models using the model training and validation data according to a target specification.

A method for measuring components produced by a production device includes selecting components to be measured from multiple components. The selection is made according to at least one selection parameter. The at least one selection parameter includes a sampling frequency. The method includes determining at least one production parameter. The at least one production parameter includes a production condition. The method includes adapting the sampling frequency based on the production parameter or a change in the production parameter. Adapting includes reducing the sampling frequency in response to one or more production parameters not changing by more than a predetermined amount.

Methods and systems for determining a sample size in a sampling plan are described. A device may receive a selection indicating a type of sampling plan. The device may retrieve interface data associated with the selected type of sampling plan. The interface data, when rendered by the device, may be outputted as a user interface. The device may output the user interface on a display of the device. The user interface may include one or more options associated with the selected type of sampling plan. The device may receive a set of input values of the one or more options through the user interface. The device may determine sample size data based on the set of input values. The sample size data may indicate an optimal number of samples to be used in the selected sampling plan.