Applied Materials, Inc.

James Robert Moyne

Jimmy Iskandar

Richard Stafford

Nicholas Ward

Parris C M Hawkins

Jianping Zou

Fei Li

Helen R Armer

Bradley D Schulze

Deepak Sharma

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.

Guardbands in substrate processing systems

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.

Matching process controllers for improved matching of process

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.

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.

Value-independent situation identification and matching

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.

Automatic and adaptive fault detection and classification limits

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.

Methods and systems for applying run-to-run control and virtual metrology to reduce equipment recovery time

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.

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.

Techniques are disclosed for methods and apparatuses for determining when to perform or trigger events. The technique comprises determining a first cost of false positives and a second cost of missed true positives. A Receive Operating Characteristic (ROC) of a prediction model is determined for the occurrence of one or more events. The operational area on the ROC is determined based on the first costs and second costs. A threshold is determined from the ROC and is applied to a detection or prediction function. An event is triggered based on the threshold.

Method and apparatus for determining when to perform or trigger events in semi-conductor processing equipment

Described herein are methods, apparatuses, and systems for determining adaptive predictive algorithms for virtual metrology. In some embodiments, a computer implemented method identifies a plurality of predictive algorithms. The method determines when to use one or more of the plurality of predictive algorithms to predict one or more virtual metrology variables in a manufacturing facility.

Methods and apparatuses for utilizing adaptive predictive algorithms and determining when to use the adaptive predictive algorithms for virtual metrology

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.

Portable, adaptable equipment health user interface

Techniques are disclosed for methods and apparatuses for forming for determining when to perform maintenance events. The technique comprises determining a first cost of false positives and a second cost of missed true positives. A Receive Operating Characteristic (ROC) of a prediction model is determined for the occurrence of an event. A survival function and prediction horizon is generated from the prediction model for the occurrence of an event. The operational area on the ROC is determined based on the first costs and second costs. A threshold is determined from the ROC and is applied to the survival function and prediction horizon. A maintenance event is triggered based on the threshold.

Method and apparatus for optimizing profit in predictive systems

A computer system iteratively executes a decision tree-based prediction model using a set of input variables. The iterations create corresponding rankings of the input variables. The computer system generates overall variables contribution data using the rankings of the input variables and identifies key input variables based on the overall variables contribution data.

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Canton, MI, United States of America

James Robert Moyne