Tokyo Electron Limited

Voyan Technology

Utstarcom, Incorporated

Relman, Inc.

Pradeep Pandey

Sanjeev Kaushal

Kenji Sugishima

Sunil C Shah

Mark Alan Erickson

Thorkell Gudmundsson

Wenling Wang

Fujio Suzuki

Koichi Sakamoto

Moyuru Yasuhara

Anthony Dip

Raymond Joe

Gary Tsztoo

Sridhar G Sharma Isukapalli

Neal A Schneider

Matthew D Shaver

David Smith

Sundar Gandhi

An adaptive real time thermal processing system is presented that includes a multivariable controller. The method includes creating a dynamic model of the MLD processing system and incorporating virtual sensors in the dynamic model. The method includes using process recipes comprising intelligent set points, dynamic models, and/or virtual sensors.

Method and apparatus for monolayer deposition (MLD)

A method of monitoring a processing system in real-time using low-pressure based modeling techniques that include processing one or more of wafers in a processing chamber, calculating dynamic estimation errors for the precursor and/or purging process, and determining if the dynamic estimation errors can be associated with pre-existing BIST rules for the process. When the dynamic estimation error cannot be associated with a pre-existing BIST rule, the method includes either modifying the BIST table by creating a new BIST rule for the process, or stopping the process when a new BIST rule cannot be created.

Method for creating a built-in self test (BIST) table for monitoring a monolayer deposition (MLD) system

A method of monitoring a processing system in real-time using low-pressure based modeling techniques that include processing one or more of wafers in a processing chamber; determining a measured dynamic process response for a rate of change for a process parameter; executing a real-time dynamic model to generate a predicted dynamic process response; determining a dynamic estimation error using a difference between the predicted dynamic process response and the expected process response; and comparing the dynamic estimation error to operational limits.

Monitoring a monolayer deposition (MLD) system using a built-in self test (BIST) table

Method for monolayer deposition

A method of determining wafer curvature in real-time is presented. The method includes establishing a first temperature profile for a hotplate surface, where the hotplate surface is divided into a plurality of temperature control zones. The method further includes positioning a wafer at a first height above the hotplate surface and determining a second temperature profile for the hotplate surface. The wafer curvature is then determined by using the second temperature profile. Also, a dynamic model of a processing system is presented and wafer curvature can be incorporated into the dynamic model.

Wafer curvature estimation, monitoring, and compensation

A method of creating and/or modifying a built-in self test (BIST) table for monitoring a thermal processing system in real-time that includes positioning a plurality of wafers in a processing chamber in the thermal processing system; executing a real-time dynamic model to generate a predicted dynamic process response; creating a measured dynamic process response; determining a dynamic estimation error; determining if the determined dynamic estimation error can be associated with a pre-existing BIST rule in the BIST table; creating a new BIST rule when the dynamic estimation error cannot be associated with any pre-existing BIST rule in the BIST table; and stopping the process when a new BIST rule cannot be created.

Built-in self test for a thermal processing system

A method of monitoring a thermal processing system in real-time using a built-in self test (BIST) table to detect, diagnose and/or predict fault conditions and/or degraded performance. The method includes positioning a plurality of wafers in a processing chamber in the thermal processing system, performing a self test process, determining a real-time transient error from a measured transient response and a baseline transient response determined by a BIST rule stored in the BIST table, and comparing the transient error to operational limits and warning limits established by the BIST rule for the self test process.

Monitoring a thermal processing system

A method of monitoring a single-wafer processing system in real-time using low-pressure based modeling techniques that include processing a wafer in a processing chamber; determining a measured dynamic process response for a rate of change for a process parameter; executing a real-time dynamic model to generate a predicted dynamic process response; determining a dynamic estimation error using a difference between the predicted dynamic process response and the expected process response; and comparing the dynamic estimation error to operational limits.

Monitoring a single-wafer processing system

Monitoring a system during low-pressure processes

A method of monitoring a thermal processing system in real-time using a built-in self test (BIST) table that includes positioning a plurality of wafers in a processing chamber in the thermal processing system; executing a real-time dynamic model to generate a predicted dynamic process response for the processing chamber during the processing time; creating a first measured dynamic process response; determining a dynamic estimation error using a difference between the predicted dynamic process response and the measured dynamic process response; and comparing the dynamic estimation error to operational thresholds established by one or more rules in the BIST table.

A system for transmitting real-time data between an asynchronous network () and a synchronous network () is disclosed. A method () may include an ingress path () for transmitting data from an asynchronous system () to a synchronous system (), and an egress path () for transmitting data from a synchronous system () to an asynchronous system (). An ingress path () may include a packet receiver () and write to synchronous system () steps. An egress path () may include read from synchronous system (), packetizer (), and packet transmitter () steps.

Media flow method for transferring real-time data between asynchronous and synchronous networks

Methods for adaptive real time control of a system for thermal processing substrates, such as semiconductor wafers and display panels. Generally, the method includes creating a dynamic model of the thermal processing system, incorporating wafer bow in the dynamic model, coupling a diffusion-amplification model into the dynamic thermal model, creating a multivariable controller, parameterizing the nominal setpoints, creating a process sensitivity matrix, creating intelligent setpoints using an efficient optimization method and process data, and establishing recipes that select appropriate models and setpoints during run-time.

Methods for adaptive real time control of a thermal processing system

An adaptive real time thermal processing system is presented that includes a multivariable controller. Generally, the method includes creating a dynamic model of the thermal processing system; incorporating reticle/mask curvature in the dynamic model; coupling a diffusion-amplification model into the dynamic thermal model; creating a multivariable controller; parameterizing the nominal setpoints into a vector of intelligent setpoints; creating a process sensitivity matrix; creating intelligent setpoints using an efficient optimization method and process data; and establishing recipes that select appropriate models and setpoints during run-time.

Adaptive real time control of a reticle/mask system

A heat treatment apparatus for making a heat treatment while estimating temperatures of objects-to-be-processed that can estimate correct temperatures of the objects-to-be-processed. A reaction tube includes heaters and temperature sensors, and receives a wafer boat. A controller estimates temperatures of wafers and temperatures of the temperature sensors in zones in the reaction tube corresponding to the heaters by using the temperature sensors and electric powers of the heaters. Based on relationships between estimated temperatures of the temperature sensors and really metered temperatures, functions expressing the relationships between the estimated temperatures and the really metered temperatures are given for the respective zones. The functions are substituted by the estimated wafer temperatures to correct the estimated wafer temperatures. Electric powers to be fed to the respective heaters are respectively controlled so that the corrected wafer temperatures are converged to target temperature trajectories.

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San Jose, CA, United States of America

Pradeep Pandey