Applied Materials, Inc.

Reachable, Inc.

Richard Stafford

James Robert Moyne

Nicholas Ward

David Everton Norman

Timothy J Sheehan

Paul Samuel Stevens, Jr

Christian Michael Bates

A method and system for the use of prediction data in monitoring actual production targets is described herein. In one embodiment, a process is provided to receive data from a plurality of source systems in a manufacturing facility and generate a prediction pertaining to a future state of the manufacturing facility based on the data received from the plurality of source systems. A recent state of the manufacturing facility is determined based on the data received from the plurality of source systems and a comparison between the recent state and the prediction is facilitated.

Use of prediction data in monitoring actual production targets

A yield prediction is received by a strategy engine. The strategy engine compares the end-of-line yield prediction to a plurality of rules. The strategy engine then instructs a component of an equipment engineering system to perform an action included in a rule that corresponds to the end-of-line yield prediction.

Yield prediction feedback for controlling an equipment engineering system

First acquired data that represents past values of one or more parameters is displayed in a user interface through which a user can monitor, control and predict system operations. Second acquired data that represents present values of the one or more parameters is displayed in the user interface. Virtual data that represents predicted future values of the one or more parameters is displayed in the user interface, wherein the first acquired data, the second acquired data and the virtual data are presented with a unified visual appearance such that a relationship between the past values, present values and predicted future values is visually indicated.

User interface with visualization of real and virtual data

A yield prediction is received by a run-to-run controller that includes an intra-process run-to-run control module that specifies process performance targets, wherein the yield prediction is associated with at least one of a manufacturing tool, a product or a process. The run-to-run control module adjusts first parameters associated with intra-process run-to-run control based on the yield prediction, wherein the first parameters include processing parameters of a process recipe.

A computer-implemented method for determining a connection ranking value between entities includes receiving private information from a user, and retrieving public information from publicly available sources. The method also includes matching the public information with the private information, generating a graph database with the public and private information, establishing at least one search criteria, and generating a connection ranking value between entities in the graph database based on the search criteria.

Systems and methods for ranking user defined targets in a universal graph database

Data of at least one of past values and present values of a system is consolidated from a plurality of sources. Virtual data of future values of the system is generated by applying the acquired data to a predictive model. Additional acquired data is received. The virtual data is dynamically updated by applying the additional acquired data to the predictive model.

Dynamically updated predictive model

A yield prediction is received by a scheduling and dispatch controller, wherein the yield prediction is associated with a manufacturing tool and a product. A weighting of the manufacturing tool is adjusted in a routing algorithm based on the yield prediction. A cost-benefit analysis is computed that identifies a cost and a benefit of manufacturing future products on the manufacturing tool. A determination is made regarding whether to process a future product on the manufacturing tool based on the cost-benefit analysis.

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Bountiful, UT, United States of America

Richard Stafford