Applied Materials, Inc.

Sidharth Bhatia

Ganesh Balasubramanian

Bhaskar Kumar

Vivek Bharat Shah

Anup Kumar Singh

Zhaozhao Zhu

Michael Howells

Jeffrey Yat Shan Au

Heng-Cheng Pai

Pramod Nambiar

Dermot Cantwell

Jie Feng

Shawn Levesque

Raja Sekhar Jetti

Shankar Venkataraman

Prashanth Kothnur

Edward P Hammond

Thomas Li

Paul Edward Gee

Anjana M Patel

Changgong Wang

Garrett H Sin

Joshua Maher

Varoujan Chakarian

Irfan Jamil

Garrett Ho-Yee Sin

Mimi-Diemmy Dao

Kartik Ramaswamy

John C Forster

Deenesh Padhi

Shahid Rauf

Paul Brillhart

Jie Yu

Abdul Aziz Khaja

Upendra V Ummethala

Steven E Babayan

Roger Alan Lindley

Hang Yu

Song-Moon Suh

Sanjay G Kamath

Blake Erickson

Patrick John Tae

Hiroshi Hamana

Tao Zhang

Hidehiro Kojiri

Ilker Durukan

Jeong Jin Hong

Sejune Cheon

Shawyon Jafari

Vinayak Veer Vats

Serghei Malkov

Mi Hyun Jang

Zhili Zuo

Yue Guo

Katty Marie Lydia Gamon Guyomard

Rong Gang Zheng

Chenfei Hu

Nicholas Ryan Pica

Roger Lindley

Upendra Ummethala

Steven Babayan

Embodiments disclosed herein include a method for use with a semiconductor processing tool. In an embodiment, the method comprises configuring the semiconductor processing tool, running a benchmark test on the semiconductor processing tool, providing hardware operating window (HOW) analytics, generating a design of experiment (DoE) using the HOW analytics, implementing process optimization, and releasing an iteration of the process recipe. In an embodiment, the method further comprises margin testing the iteration of the process recipe.

Physically-informed multi-system hardware operating windows

A method includes providing, as input to a first trained machine learning model, trace data associated with one or more substrate processing procedures. The input further includes equipment constants associated with the one or more substrate processing procedures. The input further includes trace data of a first processing chamber. The input further includes equipment constants of the first processing chamber. The method further includes obtaining, as output from the first trained machine learning model, a recommended update to a first equipment constant of the first processing chamber. The method further includes updated the first equipment constant of the first processing chamber responsive to obtaining the output from the first trained machine learning model.

Determining equipment constant updates by machine learning

A method includes receiving, by a processing device, data indicative of performance of a plurality of process chambers. The method further includes providing the data indicative of performance of the plurality of process chambers to a model. The method further includes receiving as output from the model a first recommended equipment constant update associated with a first process chamber of the plurality of process chambers and a second recommended equipment constant update associated with a second process chamber of the plurality of process chambers. The method further includes updating a first equipment constant of the first process chamber and a second equipment constant of the second process chamber in view of the first recommended equipment constant update and the second recommended equipment constant update.

Chamber matching by equipment constant updates

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 identifying sets of sensor data associated with wafers processed via wafer processing equipment and identifying sets of metrology data associated with the wafers processed via the wafer processing equipment. The method further includes generating sets of aggregated sensor-metrology data, each of the sets of aggregated sensor-metrology data including a respective set of sensor data and a respective set of metrology data. The method further includes causing, based on the sets of aggregated sensor-metrology data, performance of a corrective action associated with the wafer processing equipment.

Sensor metrology data integration

Portion of a display panel with a graphical user interface

Embodiments disclosed herein include a dynamic load simulator. In an embodiment, the dynamic load simulator comprises an impedance load, a reverse match network, and a smart RF controller. In an embodiment, the smart RF controller comprises a dynamic load generator, and a reverse match controller.

Smart dynamic load simulator for RF power delivery control system

A method includes: receiving film property data associated with manufacturing parameters of manufacturing equipment; determining that the film property data is correlated and is different from target data; selecting, by a processing device, a set of data points of the film property data that are orthogonal to the target data; performing, by the processing device, feature extraction on the set of data points; and determining, based on the feature extraction, updates to one or more of the manufacturing parameters to meet the target data.

Prescriptive analytics in highly collinear response space

A method includes receiving part data associated with a corresponding part of substrate processing equipment, sensor data associated with one or more corresponding substrate processing operations performed by the substrate processing equipment to produce one or more corresponding substrates, and metrology data associated with the one or more corresponding substrates produced by the one or more corresponding substrate processing operations performed by the substrate processing equipment that includes the corresponding part. The method further includes generating sets of aggregated part-sensor-metrology data including a corresponding set of part data, a corresponding set of sensor data, and a corresponding set of metrology data. The method further includes causing analysis of the sets of aggregated part-sensor-metrology data to generate one or more outputs to perform a corrective action associated with the corresponding part of the substrate processing equipment.

Part, sensor, and metrology data integration

Disclosed herein is technology for performing predictive modeling to identify inputs for a manufacturing process. An example method may include receiving expected output data for a manufacturing process, wherein the expected output data defines an attribute of an output of the manufacturing process; accessing a plurality of machine learning models that model the manufacturing process; determining, using a first machine learning model, input data for the manufacturing process based on the expected output data for the manufacturing process, wherein the input data comprises a value for a first input and a value for a second input; combining the input data determined using the first machine learning model with input data determined using the second machine learning model to produce a set of inputs for the manufacturing process, wherein the set of inputs comprises candidate values for the first input and candidate values for the second input.

Predictive modeling of a manufacturing process using a set of trained inverted models

Methods, systems, and non-transitory computer readable medium are described for sensor metrology data integration. A method includes receiving sets of sensor data and sets of metrology data. Each set of sensor data includes corresponding sensor values associated with producing corresponding product by manufacturing equipment and a corresponding sensor data identifier. Each set of metrology data includes corresponding metrology values associated with the corresponding product manufactured by the manufacturing equipment and a corresponding metrology data identifier. The method further includes determining common portions between each corresponding sensor data identifier and each corresponding metrology data identifier. The method further includes, for each of the sensor-metrology matches, generating a corresponding set of aggregated sensor-metrology data and storing the sets of aggregated sensor-metrology data to train a machine learning model. The trained machine learning model is capable of generating one or more outputs for performing a corrective action associated with the manufacturing equipment.

Embodiments of the present disclosure relate to a method and an apparatus for monitoring plasma behavior inside a plasma processing chamber. In one example, a method for monitoring plasma behavior includes acquiring at least one image of a plasma, and determining a plasma parameter based on the at least one image.

Plasma parameters and skew characterization by high speed imaging

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Santa Cruz, CA, United States of America

Sidharth Bhatia