公开(公告)号：US20220238300A1

公开(公告)日：2022-07-28

申请号：US17158811

申请日：2021-01-26

Applicant: APPLIED MATERIALS, INC.

Inventor： Arvind Shankar Raman ,  Harikrishnan Rajagopal ,  John Forster

IPC: H01J37/244 ,  H01L21/67 ,  H01L21/683 ,  H01J37/32 ,  G01R21/133 ,  G01R27/26 ,  H04Q9/00 ,  G06N20/00

Abstract: Methods and systems for rating a current substrate support assembly based on impedance circuit electron flow are provided. Data associated with an amount of radio frequency (RF) power flowed through an electrical component of a current substrate support assembly during a current testing process performed for the current substrate support assembly is provided as input to a trained machine learning model. One or more outputs of the trained machine learning model are obtained. A measurement value for an electron flow across an impedance circuit of the current substrate support assembly is extracted from the one or more outputs. In response to a determination that the extracted measurement value for the electron flow satisfies an electron flow criterion, a first quality rating is assigned to the current substrate support assembly.

公开(公告)号：US20240248466A1

公开(公告)日：2024-07-25

申请号：US18158370

申请日：2023-01-23

Applicant: Applied Materials, Inc.

Inventor： Arvind Shankar Raman ,  Harikrishnan Rajagopal ,  Minal Balkrishna Shettigar ,  Vishwath Ram Amarnath ,  Yi Qi

IPC: G05B23/02

CPC classification number: G05B23/024 ,  G05B23/0283

Abstract: Methods and systems for process chamber qualification for maintenance process endpoint detection are provided. Data collected by one or more sensors of a process chamber of a manufacturing system is identified. The identified data is collected during performance of initial maintenance operation(s) of a maintenance process. A current state of the process chamber is determined, based on the identified data, after the performance of the initial maintenance operation(s) based on the identified data. In response to a determination that the current state does not satisfy one or more chamber maintenance criteria, a set of subsequent maintenance operations to be performed to cause the current state of the process chamber to satisfy the criteria is identified. Performance of the set of subsequent maintenance operations is initiated at the process chamber.

公开(公告)号：US12205791B2

公开(公告)日：2025-01-21

申请号：US17158811

申请日：2021-01-26

Applicant: APPLIED MATERIALS, INC.

Inventor： Arvind Shankar Raman ,  Harikrishnan Rajagopal ,  John Forster

IPC: H01J37/244 ,  G01R21/133 ,  G01R27/26 ,  G06N20/00 ,  H01J37/32 ,  H01L21/67 ,  H01L21/683 ,  H04Q9/00

Abstract: Methods and systems for rating a current substrate support assembly based on impedance circuit electron flow are provided. Data associated with an amount of radio frequency (RF) power flowed through an electrical component of a current substrate support assembly during a current testing process performed for the current substrate support assembly is provided as input to a trained machine learning model. One or more outputs of the trained machine learning model are obtained. A measurement value for an electron flow across an impedance circuit of the current substrate support assembly is extracted from the one or more outputs. In response to a determination that the extracted measurement value for the electron flow satisfies an electron flow criterion, a first quality rating is assigned to the current substrate support assembly.

公开(公告)号：US20240427324A1

公开(公告)日：2024-12-26

申请号：US18341650

申请日：2023-06-26

Applicant: Applied Materials, Inc.

Inventor： Jimmy Iskandar ,  Fei LI ,  Arvind Shankar Raman ,  James Robert Moyne ,  Eda Tuncel ,  Michael Armacost

IPC: G05B23/02

Abstract: Implementations relate to techniques of monitoring conditions of tools used in device manufacturing systems. The techniques include storing a failure index (FI) model generated using run-time sensor data that was collected during operations of a tool that occurred prior to a low number of failures of the tool or even before any such failures occur. The FI model includes an FI function of the run-time sensor data and FI threshold value(s) associated with conditions of the tool. The techniques further include collecting new run-time sensor data and applying the FI model to the new run-time sensor data to identify one or more conditions associated with the tool. The techniques further include updating the FI model responsive to one or more tool failures.