Applied Materials, Inc.

Zhaozhao Zhu

Blake Erickson

Michael C Kutney

Keith R Berding

Sidharth Bhatia

Jeffrey Yat Shan Au

Chunlei Zhang

Upendra V Ummethala

Steven Trey Tindel

Prashanth Kumar

Michael D Willwerth

Patrick John Tae

Michael Howells

Soumendra N Barman

Shawn Levesque

Raja Sekhar Jetti

Michelle SanPedro

Jeffrey Ludwig

Varoujan Chakarian

Tsung Feng Wu

Chih Chung Chou

Gregory John Freeman

Sejune Cheon

Ozkan Celik

Sang Hong Kim

Patricia A Schulze

Barry Craver

Arunkumar Ramachandraiah

Mihyun Jang

Chenfei Hu

Nicholas Ryan Pica

Suresh Polali Narayana Rao

Anders Andelman Nottrott

An article, apparatus, and method for detecting an etch rate uniformity in a processing chamber of an electronics processing system is provided. A device is placed in a processing chamber of an electronics processing system. The device includes a first layer deposited on a surface of the device and a second layer deposited on the first layer. The first layer is composed of a first sense material and the second layer is composed of an etch material. During an etch process at the processing chamber, a first amount of time from an initiation of the etch process to a detection of a first indication of completion of etching of the second layer at a first portion of the surface of the device is measured. The etch process etches the second layer of the device based on an initial set of etch parameter settings. A first etch rate of the processing chamber is determined based on the measured first amount of time and a thickness of the second layer. An optimized set of etch parameter settings to be applied at the processing chamber during subsequent etch processes is determined based on the first etch rate of the processing chamber.

In-situ etch rate and etch rate uniformity detection system

A method includes receiving, by a processing device, first sensor data indicating a state of a wall corresponding to a first processing chamber. The first sensor data includes optical spectral data. The method further includes determining, by the processing device, a first value based on the first sensor data. The first value corresponds to a first amount of a product disposed along a surface of the wall at a first time. The method further includes determining, by the processing device, a first update to a first process operation associated with the first processing chamber based on the first value. The method further includes performing, by the processing device, one or more of (i) preparing a notification indicating the first update for presentation on a graphical user interface (GUI), or (ii) causing performance of the first process operation in accordance with the first update.

Process characterization and correction using optical wall process sensor (OWPS)

A method includes receiving, by a processing device, first data from an optical sensor of a processing chamber. The method further includes processing the first data to obtain second data. The second data includes an indication of a condition of a coating on an interior surface of the processing chamber. The method further includes generating an indication of performance of a processing operation of the processing chamber in view of the second data. The method further includes causing performance of a corrective action in view of the indication of performance of the processing chamber.

A method includes receiving spectral data of a substrate and metrology data corresponding to the spectral data of the substrate. The method further includes determining a plurality of feature model configurations for each of a plurality of feature models, each of the plurality of feature model configurations including one or more feature model conditions. The method further includes determining a plurality of feature model combinations, where each feature model combination of the plurality of feature model combinations includes a subset of the plurality of feature model configurations. The method further includes generating a plurality of input datasets, where each input dataset of the plurality of input datasets is generated based on application of the spectral data to a respective feature model combination of the plurality of feature model combinations. The method further includes training a plurality of machine learning models, where each machine learning model is trained to generate an output using an input dataset of the plurality of input datasets and the metrology data. The method further includes selecting a trained machine learning model from the plurality of trained machine learning models satisfying one or more selection criteria.

Machine learning model training

Implementations disclosed describe a system including a light source, an optical sensor, and a processing device. The light source directs, during a first time, a probe light into a processing chamber through a window. The light source ceases, during a second time, directing the probe light into the processing chamber through the window. The optical sensor detects, during the first time, a first intensity of a first light. The first light includes a portion of the probe light reflected from the window and a light transmitted from an environment of the processing chamber through the window. The optical sensor detects, during the second time, a second intensity of a second light. The second light includes the light transmitted from the environment of the processing chamber through the window. The processing device determines, using the first intensity and the second intensity, a transmission coefficient of the window.

Transmission corrected plasma emission using in-situ optical reflectometry

An apparatus includes a base component and a plurality of collimators housed within the base component. Each collimator of the plurality of collimators corresponds to a respective location of a plurality of locations of a wafer in an etch chamber. The plurality of locations includes a center location of the wafer, a plurality of inner ring locations along an inner ring of the wafer associated with a first set of radially symmetric optical emission spectroscopy (OES) signal sampling paths, and a plurality of outer ring locations along an outer ring of the wafer associated with a second set of radially symmetric OES signal sampling paths.

Spatial optical emission spectroscopy for etch uniformity

A method includes processing a substrate in a process chamber according to a recipe, wherein the substrate comprises at least one of a film or a feature after the processing. The method further includes generating a profile map of the first substrate. The method further includes processing data from the profile map using a first model, wherein the first model outputs at least one of an estimated mesa condition of a substrate support for the process chamber, an estimated lift pin location condition of the substrate support an estimated seal band condition of the substrate support, or an estimated process kit ring condition for a process kit ring for the process chamber. The method further includes outputting a notice as a result of the processing.

Estimation of chamber component conditions using substrate measurements

A method for a substrate measurement subsystem is provided. An indication is received that a substrate being processed at a manufacturing system has been loaded into a substrate measurement subsystem. First positional data of the substrate within the substrate measurement subsystem is determined. One or more portions of the substrate to be measured by one or more sensing components of the substrate measurement subsystem are determined based on the first positional data of the substrate and a process recipe for the substrate. Measurements of each of the determined portions of the substrate are obtained by one or more sensing components of the substrate measurement subsystem. The obtained measurements of each of the determined portions of the substrate are transmitted to a system controller.

Substrate measurement subsystem

A substrate processing system includes a process chamber, one or more robot, a substrate measurement system, and a computing device. The process chamber may process a substrate that will comprise a film and/or feature after the processing. The one or more robot, to move the substrate from the process chamber to a substrate measurement system. The substrate measurement system may measure the film and/or feature on the substrate and generate a profile map of the film and/or feature. The computing device may process data from the profile map using a first trained machine learning model, wherein the first trained machine learning model outputs a first chamber component condition estimation for a first chamber component of the process chamber. The computing device may then determine whether to perform maintenance on the first chamber component of the process chamber based at least in part on the first chamber component condition estimation.

Chamber component condition estimation using substrate measurements

A process recipe associated with a substrate at a manufacturing system is identified. A first set of measurements for the substrate is obtained from a substrate measurement subsystem. A second set of measurements for the substrate is obtained from one or more sensors of a chamber of the manufacturing system. A determination is made based on the obtained first set of measurements and the obtained second set of measurements of whether to modify the process recipe by at least one of modifying an operation of the process recipe or generating an instruction to prevent completion of execution of one or more operations of the process recipe.

Integrated substrate measurement system to improve manufacturing process performance

An apparatus includes a substrate holder, a first actuator to rotate the substrate holder, a second actuator to move the substrate holder linearly, a first sensor to generate one or more first measurements or images of the substrate, a second sensor to generate one or more second measurements of target positions on the substrate, and a processing device. The processing device estimates a position of the substrate on the substrate holder and causes the first actuator to rotate the substrate holder about a first axis. The rotation causes an offset between a field of view of the second sensor and a target position on the substrate due to the substrate not being centered on the substrate holder. The processing device causes the second actuator to move the substrate holder linearly along a second axis to correct the offset. The processing device determines a profile across a surface of the substrate based on the one or more second measurements of the target positions.

Integrated substrate measurement system

Portion of a display panel with a graphical user interface

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Milpitas, CA, United States of America

Zhaozhao Zhu