Abstract: Described herein are methods and systems for providing a user interface to indicate health of a tool in a manufacturing facility. A method may include receiving, via a user interface, user selection of fault detection data pertaining to a tool in a manufacturing facility, obtaining health abnormality indicators of the tool using the fault detection data, and presenting the health abnormality indicators of the tool in the user interface.

Abstract: Embodiments provide techniques for compressing sensor data collected within a manufacturing environment. One embodiment monitors a plurality of runs of a recipe for fabricating one or more semiconductor devices within a manufacturing environment to collect runtime data from a plurality of sensors within the manufacturing environment. The collected runtime data is compressed by generating, for each of the plurality of sensors and for each of the plurality of runs, a respective representation of the corresponding runtime data that describes a shape of the corresponding runtime data and a magnitude of the corresponding runtime data. A query specifying one or more runtime data attributes is received and executed against the compressed runtime data to generate query results, by comparing the one or more runtime data attributes to at least one of the generated representations of runtime data.

Abstract: Techniques are provided for classifying runs of a recipe within a manufacturing environment. Embodiments monitor a plurality of runs of a recipe to collect runtime data from a plurality of sensors within a manufacturing environment. Qualitative data describing each semiconductor devices produced by the plurality of runs is determined. Embodiments characterize each run into a respective group, based on an analysis of the qualitative data, and generate a data model based on the collected runtime data. A multivariate analysis of additional runtime data collected during at least one subsequent run of the recipe is performed to classify the at least one subsequent run into a first group. Upon classifying the at least one subsequent run, embodiments output for display an interface depicting a ranking sensor types based on the additional runtime data and the description of relative importance of each sensor type for the first group within the data model.

Abstract: A method is provided for determining one or more causes for variability in data. The method includes selecting a first range of a multivariate model output data on a user interface and employing a computing system, operatively coupled to the user interface, to determine one or more process data causing a variability of the multivariate model output data in the first range when compared to a second range of the multivariate model output data. At least some of the process data includes data derived from a physical measurement of a process variable.

Abstract: Described herein are methods and systems for chamber matching in a manufacturing facility. A method may include receiving a first chamber recipe advice for a first chamber and a second chamber recipe advice for a second chamber. The chamber recipe advices describe a set of tunable inputs and a set of outputs for a process. The method may further include adjusting at least one of the set of first chamber input parameters or the set of second chamber input parameters and at least one of the set of first chamber output parameters or the set of second chamber output parameters to substantially match the first and second chamber recipe advices.

Abstract: Described herein are methods and systems for chamber matching in a manufacturing facility. A method may include receiving a first chamber recipe advice for a first chamber and a second chamber recipe advice for a second chamber. The chamber recipe advices describe a set of tunable inputs and a set of outputs for a process. The method may further include adjusting at least one of the set of first chamber input parameters or the set of second chamber input parameters and at least one of the set of first chamber output parameters or the set of second chamber output parameters to substantially match the first and second chamber recipe advices.

Abstract: Embodiments presented herein provide techniques for predicting the topography of a product produced from a manufacturing process. One embodiment includes generating a plurality of prediction models. Each of the plurality of prediction models corresponds to a respective one of a plurality of positional coordinates of a product produced from a manufacturing process. The method also includes receiving a set of user-specified input parameters to apply to the manufacturing control process. The method further includes generating a graphical representation of a topography map for the product for the user-specified of input parameters based on the plurality of prediction models.

Abstract: Described herein are methods, apparatuses, and systems for reducing equipment repair time. In one embodiment, a computer implemented method includes collecting, with a system, data including test substrate data or other metrology data and fault detection data for maintenance recovery of at least one manufacturing tool in a manufacturing facility and determining, with the system, a relationship between tool parameter settings for the at least one manufacturing tool and at least some collected data including the test substrate data. The method further includes utilizing zero or more virtual metrology predictive algorithms and at least some collected data to obtain a metrology prediction and applying multivariate run-to-run (R2R) control modeling to obtain a state estimation including a current operating region of the at least one manufacturing tool based on the test substrate data and obtain at least one tool parameter adjustment for at least one target parameter for the at least one manufacturing tool.

Abstract: Embodiments disclosed herein include methods for reducing or eliminating the impact of tuning disturbances during prediction of lamp failure. In one embodiment, the method comprises monitoring data of a lamp module for a process chamber using one or more physical sensors disposed at different locations within the lamp module, creating virtual sensors based on monitoring data of the lamp module, and providing a prediction model for the lamp module using the virtual sensors as inputs.

Abstract: A method is provided for determining two or more context types having an associated fault to be modeled by the same multivariate model. The method includes selecting a fault and selecting two or more context types associated with the fault. The method further includes accessing data stored for the selected context types. The method further includes generating rankings of process data tags for each selected context type. Each ranking includes process data tags ranked according to relative contributions of each process data tag in the ranking to the fault. The method further includes classifying the context types into one or more classes based on the process data tags included in each ranking. The one or more classes include a first class of the context types. The method further includes deploying a multivariate model operable to monitor processing equipment for the selected fault for the first class of context types.

Abstract: A method is provided for determining one or more causes for variability in data. The method includes selecting a first range of a multivariate model output data on a user interface and employing a computing system, operatively coupled to the user interface, to determine one or more process data causing a variability of the multivariate model output data in the first range when compared to a second range of the multivariate model output data. At least some of the process data includes data derived from a physical measurement of a process variable.

Abstract: Described herein are methods and systems for chamber matching in a manufacturing facility. A method may include receiving a first chamber recipe advice for a first chamber and a second chamber recipe advice for a second chamber. The chamber recipe advices describe a set of tunable inputs and a set of outputs for a process. The method may further include adjusting at least one of the set of first chamber input parameters or the set of second chamber input parameters and at least one of the set of first chamber output parameters or the set of second chamber output parameters to substantially match the first and second chamber recipe advices.

Abstract: Described herein are methods and systems for providing a user interface to indicate health of a tool in a manufacturing facility. A method may include receiving, via a user interface, user selection of fault detection data pertaining to a tool in a manufacturing facility, obtaining health abnormality indicators of the tool using the fault detection data, and presenting the health abnormality indicators of the tool in the user interface.

Abstract: A system and method of determining shape and position corrections of a beam such as a particle or other beam used in a system such as a particle beam lithography. The method of providing corrected deflector voltages may include determining a voltage step value by subtracting a previous deflector voltage value with a current deflector voltage value; determining a plurality of correction values using the voltage step value and an exposure time for the current deflector voltage value; selecting a current voltage correction value from the plurality of correction values using the current deflector voltage value; and calculating a corrected deflector voltage value by adding the current voltage correction value to the current deflector voltage value.

Abstract: A system and method of determining shape and position corrections of a beam such as a particle or other beam used in a system such as a particle beam lithography. The method of providing corrected deflector voltages may include determining a voltage step value by subtracting a previous deflector voltage value with a current deflector voltage value; determining a plurality of correction values using the voltage step value and an exposure time for the current deflector voltage value; selecting a current voltage correction value from the plurality of correction values using the current deflector voltage value; and calculating a corrected deflector voltage value by adding the current voltage correction value to the current deflector voltage value.