Applied Materials, Inc.

Samsung Display Co., Ltd.

Jeong Jin Hong

Sejune Cheon

Sang Hong Kim

Xiaoqun Zou

Mikyung Shim

Jinkyeong Lee

Mihyun Jang

Jong Sung Bae

Sidharth Bhatia

Dong Hyeon Lee

Zhaozhao Zhu

Jusuck Lee

Seong Min Cho

Chan Young Park

Joshua Maher

Mi Hyun Jang

Hee Soon Jeong

Eun Sil Han

Jin Kyeong Lee

Anders Andelman Nottrott

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 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

A method includes receiving trace sensor data associated with a first manufacturing process of a manufacturing chamber. The method further includes processing the trace sensor data by a processing device to generate summary data associated with the trace sensor data. Generating the summary data includes identifying a steady state and transient portion of the trace sensor data and generating a first and second portion of summary data based on the steady state and transient portions. The method further includes generating a quality index score based on the summary data. The method further includes providing an alert to a user based on the quality index score. The alert includes an indication that the manufacturing chamber performance does not meet a first threshold.

Diagnostic tool to tool matching methods for manufacturing equipment

A method includes receiving trace sensor data associated with a first manufacturing process of a processing chamber. The method further includes processing the trace sensor data using one or more trained machine learning models that generate a representation of the trace sensor data, and then generate reconstructed sensor data based on the representation of the trace sensor data. The method further includes comparing the trace sensor data to the reconstructed sensor data. The method further includes determining one or more differences between the reconstructed sensor data and the trace sensor data. The method further includes determining whether to recommend a corrective action associated with the processing chamber based on the one or more differences between the trace sensor data and the reconstructed sensor data.

Diagnostic tool to tool matching and full-trace drill-down analysis methods for manufacturing equipment

A method includes receiving first data associated with measurements taken by a sensor during a first manufacturing procedure of a manufacturing chamber. The method further includes receiving second data. The second data includes reference data associated with the first data. The method further includes providing the first and second data to a comparison model. The method further includes receiving a similarity score from the comparison model, associated with the first and second data. The method further includes performance of a corrective action in view of the similarity score.

Diagnostic tool to tool matching and comparative drill-down analysis methods for manufacturing equipment

The subject matter of this specification can be implemented in, among other things, methods, systems, computer-readable storage medium. A method can include receiving (i) sensor data indicating a first state of an environment of a processing chamber processing a substrate subsequent to a chamber recovery procedure, and (ii) substrate process data indicating a set of process parameter values associated with performing a substrate processing procedure by the processing chamber having the environment in a second state prior to the chamber recovery procedure. The method further includes processing the sensor data and the substrate process data using one or more machine learning models to determine one or more outputs. The one or more outputs include an update to at least one of the set of process parameter values. The update is associated with performing the substrate processing procedure by the processing chamber having the environment in the first state.

Process control knob estimation

The subject matter of this specification can be implemented in, among other things, methods, systems, computer-readable storage medium. A method can include a processing device receiving sensor data indicating a state of an environment of a processing chamber processing a series of substrates according to a substrate processing procedure. The series of substrates may be processed subsequent to a preventive maintenance procedure. The processing device may determine a first set of values based on the sensor data. The set of values may indicate a likelihood an associated substrate comprises a first process result that meets a threshold condition of the substrate processing procedure. The processing device may further predict a first test result based on the first set of values. The first test result may indicate a likelihood a first substrate processed subsequent to processing the series of substrates comprises a second process result that meets the threshold condition.

Post preventative maintenance chamber condition monitoring and simulation

A method includes receiving trace sensor data associated with a first manufacturing process of a manufacturing chamber. The method further includes processing the trace sensor data by a processing device to generate summary data associated with the trace sensor data. The method further includes generating a quality index score based on the summary data. The method further includes providing an alert to a user based on the quality index score. The alert includes an indication that the manufacturing chamber performance does not meet a first threshold.

A display device according to an exemplary embodiment of the present invention includes: a display panel including a plurality of pixels respectively displaying a first color, a second color, and a third color; and a signal controller controlling the display panel by processing an input image signal, wherein, in a color weakness mode, at least one color among the first to third colors is displayed with a higher gray than an input gray of the input image signal, and the other colors are displayed with the same gray as the input gray of the input image signal.

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Yongin, South Korea

Jeong Jin Hong