Applied Materials, Inc.

Sidharth Bhatia

Ganesh Balasubramanian

Bhaskar Kumar

Vivek Bharat Shah

Anup Kumar Singh

Zhaozhao Zhu

Jeffrey Yat Shan Au

Heng-Cheng Pai

Pramod Nambiar

Dermot Cantwell

Michael Howells

Jie Feng

Shawn Levesque

Raja Sekhar Jetti

Shankar Venkataraman

Prashanth Kothnur

Edward P Hammond

Paul Edward Gee

Anjana M Patel

Garrett H Sin

Changgong Wang

Varoujan Chakarian

Irfan Jamil

Garrett Ho-Yee Sin

Kartik Ramaswamy

John C Forster

Deenesh Padhi

Shahid Rauf

Paul Brillhart

Jie Yu

Abdul Aziz Khaja

Hang Yu

Patrick John Tae

Sanjay G Kamath

Song-Moon Suh

Blake Erickson

Hiroshi Hamana

Tao Zhang

Hidehiro Kojiri

Ilker Durukan

Serghei Malkov

Shawyon Jafari

Vinayak Veer Vats

Joshua Maher

Zhili Zuo

Yue Guo

Katty Marie Lydia Gamon Guyomard

Rong Gang Zheng

Chenfei Hu

Nicholas Ryan Pica

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

Embodiments of the present disclosure generally relate to apparatuses for reducing particle contamination on substrates in a plasma processing chamber. In one or more embodiments, an edge ring is provided and includes a top surface, a bottom surface opposite the top surface and extending radially outward, an outer vertical wall extending between and connected to the top surface and the bottom surface, an inner vertical wall opposite the outer vertical wall, an inner lip extending radially inward from the inner vertical wall, and an inner step disposed between and connected to the inner wall and the bottom surface. During processing, the edge ring shifts the high plasma density zone away from the edge area of the substrate to avoid depositing particles on the substrate when the plasma is de-energized.

Plasma density control on substrate edge

Methods, systems, and non-transitory computer readable medium are described for prescriptive analytics in highly collinear response space. A method includes receiving film property data associated with manufacturing parameters of manufacturing equipment. The method further includes determining that the film property data is correlated and is different from target data. The method further includes selecting a set of data points of the film property data that are orthogonal to the target data. The method further includes performing feature extraction on the set of data points. The method further includes determining, based on the feature extraction, updates to one or more of the manufacturing parameters to meet the target data.

A process development visualization tool generates a first visualization of a parameter associated with a manufacturing process, and provides a GUI control element associated with a process variable of the manufacturing process, wherein the GUI control element has a first setting associated with a first value for the process variable. The process development tool receives a user input to adjust the GUI control element from the first setting to a second setting, determines a second value for the process variable based on the second setting, and determines a second set of values for the parameter that are associated with the second value for the process variable. The process development tool then generates a second visualization of the parameter, wherein the second visualization represents the second set of values for the parameter that are associated with the second value for the process variable.

Process development visualization tool

Growing community of inventors

Santa Cruz, CA, United States of America

Sidharth Bhatia