Abstract: A method includes identifying first parameters of a first processing chamber of a semiconductor fabrication facility. The first parameters include first input parameters and first output parameters. The method further includes identifying second parameters of a second processing chamber of the semiconductor fabrication facility. The second parameters include second input parameters and second output parameters. The method further includes generating, by a processing device based on the first parameters and the second parameters, composite parameters comprising composite input parameters and composite output parameters. Semiconductor fabrication is based on the composite parameters.

Abstract: An electronic device manufacturing system configured identify a machine-learning model trained to generate analytic or predictive data for a first substrate processing domain associated with a type of substrate processing system. The system is further configured to obtain first trace data pertaining to the first domain used to train the machine-learning model. The system is further configured to a transfer model for a second substrate processing domain associated with the type of substrate processing system. The transfer model is generated based on the first trace data pertaining to the first substrate processing domain and second trace data pertaining to the second substrate processing domain. Using the transfer model, at least one of the machine-learning model or current trace data associated with the second substrate processing domain is modified to enable the machine-learning model to generate analytic or predictive data associated with the second substrate processing domain.

Abstract: An electronic device manufacturing system configured to obtain current sensor data associated with a sensor of a substrate manufacturing system and determine a slope value associated with the current sensor data. Responsive to determining that the slope value satisfied a threshold criterion associated with a fault detection limit, at least one of an alert is generated or a corrective action performed.

Abstract: Implementations relate to techniques of monitoring conditions of tools used in device manufacturing systems. The techniques include storing a failure index (FI) model generated using run-time sensor data that was collected during operations of a tool that occurred prior to a low number of failures of the tool or even before any such failures occur. The FI model includes an FI function of the run-time sensor data and FI threshold value(s) associated with conditions of the tool. The techniques further include collecting new run-time sensor data and applying the FI model to the new run-time sensor data to identify one or more conditions associated with the tool. The techniques further include updating the FI model responsive to one or more tool failures.

Abstract: An electronic device manufacturing system configured to receive, by a first computing system, a request for manufacturing process data request. The system further uses a first cryptographic key controlled by a first entity and a second cryptographic key controlled by a second entity, a database management system to retrieve the manufacturing process data from a data store. The system further obtains, using the database management system, the manufacturing process data stored in the data store. The manufacturing process data is encrypted. The system further sends the encrypted manufacturing process data to a second computing system configured to perform one or more anonymization operations on the manufacturing process data.

Abstract: A method includes identifying trace data including a plurality of data points, the trace data being associated with production, via a substrate processing system, of substrates having property values that meet threshold values. The method further includes generating, based on the trace data and a plurality of allowable types of variance, a guardband including an upper limit and a lower limit for fault detection. The method further includes causing, based on the guardband, performance of a corrective action associated with the substrate processing system.

Abstract: An electronic device manufacturing system configured to obtain, by a processor, a plurality of datasets associated with a process recipe, wherein each dataset of the plurality of datasets comprises data generated by a plurality of sensors during a corresponding process run performed using the process recipe. The processor is further configured to determine, using the plurality of data sets associated with the process recipe, a correlation value between two or more sensors of the plurality of sensors. Responsive to the correlation value satisfying a threshold criterion, the processor assigns the two or more sensors to a cluster. During a subsequent process run, the processor generates an anomaly score associated with the cluster and indicative of an anomaly associated with at least one step of the subsequent process run.

Abstract: A method includes identifying trace data including a plurality of data points, the trace data being associated with production, via a substrate processing system, of substrates having property values that meet threshold values. The method further includes generating, based on the trace data and a plurality of allowable types of variance, a guardband including an upper limit and a lower limit for fault detection. The method further includes causing, based on the guardband, performance of a corrective action associated with the substrate processing system.

Abstract: A method includes identifying trace data including a plurality of data points, the trace data being associated with production, via a substrate processing system, of substrates having property values that meet threshold values. The method further includes determining, based on a guardband, guardband violation data points of the plurality of data points of the trace data. The method further includes determining, based on the guardband violation data points, guardband violation shape characterization. Classification of additional guardband violation data points of additional trace data is to be based on the guardband violation shape characterization. Performance of a corrective action associated with the substrate processing system is based on the classification.

Abstract: A method includes identifying trace data including a plurality of data points, the trace data being associated with production, via a substrate processing system, of substrates that have property values that meet threshold values. The method further includes determining, based on the trace data, a dynamic acceptable area outside of guardband limits. The method further includes causing, based on the dynamic acceptable area outside of the guardband limits, performance of a corrective action associated with the substrate processing system.

Abstract: A method includes receiving one or more fingerprint dimensions to be used to generate a fingerprint. The method further includes receiving trace data associated with a manufacturing process. The method further includes applying the one or more fingerprint dimensions to the trace data to generate at least one feature. The method further includes generating the fingerprint based on the at least one feature. The method further includes causing, based on the fingerprint, performance of a corrective action associated with one or more manufacturing processes.

Abstract: Implementations disclosed describe systems and techniques to detect anomalies in a manufacturing operation. The techniques include generating, using a plurality of outlier detection models, a plurality of outlier scores. The outlier scores are representative of a degree of presence, in a plurality of sensor statistics, of an anomaly associated with the manufacturing operation. Individual outlier scores are generated using a respective one of the plurality of outlier detection models. The techniques further include determining, using the outlier scores, a likelihood of the anomaly associated with the manufacturing operation.

Abstract: Implementations disclosed describe a method and a system to perform the method of obtaining a reduced representation of a plurality of sensor statistics representative of data collected by a plurality of sensors associated with a device manufacturing system performing a manufacturing operation. The method further includes generating, using a plurality of outlier detection models, a plurality of outlier scores, each of the plurality of outlier scores generated based on the reduced representation of the plurality of sensor statistics using a respective one of the plurality of outlier detection models. The method further includes processing the plurality of outlier scores using a detector neural network to generate an anomaly score indicative of a likelihood of an anomaly associated with the manufacturing operation.

Abstract: A method includes receiving one or more fingerprint dimensions to be used to generate a fingerprint. The method further includes receiving trace data associated with a manufacturing process. The method further includes applying the one or more fingerprint dimensions to the trace data to generate at least one feature. The method further includes generating the fingerprint based on the at least one feature. The method further includes causing, based on the fingerprint, performance of a corrective action associated with one or more manufacturing processes.

Abstract: A method includes receiving, from sensors, current trace data including current sensor values associated with producing products. The method further includes processing the current trace data to identify features of the current trace data and providing the features of the current trace data as input to a trained machine learning model that uses a hyperplane limit for product classification. The method further includes obtaining, from the trained machine learning model, outputs indicative of predictive data associated with the hyperplane limit and processing the predictive data and the hyperplane limit to determine: first products associated with a first product classification and second products associated with a second product classification based exclusively on the subset of the plurality of features; and third products associated with the first product classification or the second product classification based on an additional feature not within the subset.

Abstract: Implementations disclosed describe a method and a system to perform the method of obtaining a reduced representation of a plurality of sensor statistics representative of data collected by a plurality of sensors associated with a device manufacturing system performing a manufacturing operation. The method further includes generating, using a plurality of outlier detection models, a plurality of outlier scores, each of the plurality of outlier scores generated based on the reduced representation of the plurality of sensor statistics using a respective one of the plurality of outlier detection models. The method further includes processing the plurality of outlier scores using a detector neural network to generate an anomaly score indicative of a likelihood of an anomaly associated with the manufacturing operation.

Abstract: Described herein are methods, apparatuses, and systems for reducing equipment repair time. In one embodiment, a computer implemented method includes collecting 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 a relationship between tool parameter settings for the manufacturing tool and the test substrate data. The method further includes utilizing 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. Applying multivariate run-to-run (R2R) control modeling to obtain tool parameter adjustments for at least one manufacturing tool to reduce maintenance recovery time and to reduce requalification time.

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: Described herein are methods, apparatuses, and systems for reducing equipment repair time. Disclosed methods include collecting 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. Disclosed methods include determining 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 disclosure includes utilizing 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 tool parameter adjustment for at least one target parameter for the at least one manufacturing tool. The disclosure further includes applying the R2R control modeling to obtain tool parameter adjustments for at least one manufacturing tool.

Abstract: A method includes identifying first parameters of a first processing chamber of a semiconductor fabrication facility. The first parameters include first input parameters and first output parameters. The method further includes identifying second parameters of a second processing chamber of the semiconductor fabrication facility. The second parameters include second input parameters and second output parameters. The method further includes generating, by a processing device based on the first parameters and the second parameters, composite parameters comprising composite input parameters and composite output parameters. Semiconductor fabrication is based on the composite parameters.