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 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 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 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: 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: 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: 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: 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.