Applied Materials, Inc.

Brooks Automation Gmbh

Bradley D Schulze

Jimmy Iskandar

Michael D Armacost

Suketu Arun Parikh

James Robert Moyne

Chong Jiang

Konstantinos Tsakalis

Kevin D Stoddard

Kommisetti Subrahmanyam

Jigar Bhadriklal Patel

Haw-Jyue Luo

Methods and systems for automated monitoring of sensors associated with a tool used in a manufacturing process are described. Sensor health may be evaluated based on the sensor's actual responses to set point changes. Rather than interrupt operation of the tool to determine whether one or more sensors are behaving in a predictable manner over an applicable range of operating conditions, pairs of set point values and sensor responses may be collected during the manufacturing process and stored in a time-indexed manner. A virtual model may be created for each sensor using selected ones of the indexed pairs and represented as points in an orthogonal coordinate system to identify a predictable operating region corresponding to the operating range. If a sensor response to a set point change, reflected in a stored, time indexed pair, is non-linear or offset relative to the predictable operating region, an alarm may be generated.

System and method for monitoring sensor linearity as part of a production process

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.

Apparatus for cost-effective conversion of unsupervised fault detection (FD) system to supervised FD system

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.

Topography prediction using system state information

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

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.

Apparatus and method for integrating manual and automated techniques for automated correlation in data mining

A method for monitoring and assessing operation of a semiconductor fabrication facility comprises the steps of connecting a monitoring and assessment system to a system bus which is connected directly or indirectly to a manufacturing execution system and a plurality of semiconductor fabrication tools. Through a user interface, the state models can be configured for the semiconductor fabrication tools where each state model is based upon a set of defined triggers for each tool. During monitoring various messages are transmitted on the system bus between the semiconductor fabrication tools and the manufacturing execution system and the monitoring and assessment system, and appropriate triggers are generated based upon the messages where the triggers are selected from a set of defined triggers. During operation, the state models are updated for each tool affected by one of the triggers and transitions within the state models are recorded in a tracking database.

System and method for automated monitoring and assessment of fabrication facility

A automated run-to-run controller for controlling manufacturing processes comprises set of processing tools, a set of metrology tools for taking metrology measurements from the processing tools, and a supervising station for managing and controlling the processing tools. The supervising station comprises an interface for receiving metrology data from the metrology tools and a number of variable parameter tables, one for each of the processing tools, collectively associated with a manufacturing process recipe. The supervising station also includes one or more internal models which relate received metrology data to one or more variables for a processing tool, and which can modify variables stored in the variable parameter table to control the process tools using feedback and/or feed-forward control algorithms. Feed-forward control algorithms may, in certain embodiments, be used to adjust process targets for closed loop feedback control. The supervising station may have a user interface by which different feedback or feed-forward model formats (single or multi-variate) may be interactively selected based upon experimental or predicted behavior of the system, and may also permit users to utilize their own models for run-to-run control.

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Phoenix, AZ, United States of America

Bradley D Schulze