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公开(公告)号：US11586160B2
公开(公告)日：2023-02-21
申请号：US17360652
申请日：2021-06-28
Applicant: APPLIED MATERIALS, INC.
Inventor： Kartik B Shah , Satish Radhakrishnan , Karthik Ramanathan , Karthikeyan Balaraman , Adolph Miller Allen , Xinyuan Chong , Mitrabhanu Sahu , Wenjing Xu , Michael Sterling Jackson , Weize Hu , Feng Chen
Abstract: Methods and systems for reducing substrate particle scratching using machine learning are provided. A machine learning model is trained to predict process recipe settings for a substrate temperature control process to be performed for a current substrate at a manufacturing system. First training data and second training data are generated for the machine learning model. The first training data includes historical data associated with prior process recipe settings for a prior substrate temperature control process performed for a prior substrate at a prior process chamber. The second training data is associated with a historical scratch profile of one or more surfaces of the prior substrate after performance of the prior substrate temperature control process according to the prior process recipe settings. The first training data and the second training data are provided to train the machine learning model to predict which process recipe settings for the substrate temperature control process to be performed for the current substrate correspond to a target scratch profile for one or more surfaces of the current substrate.
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公开(公告)号：US20220245307A1
公开(公告)日：2022-08-04
申请号：US17166965
申请日：2021-02-03
Applicant: Applied Materials, Inc.
Inventor： Prashanth Kothnur , Karthik Ramanathan , Ajit Balakrishna , Kartik Shah , Umesh Kelkar , Vishwas Pandey , Prasoon Shukla , Sushil Arun Samant
Abstract: Embodiments described herein include processes for generating a hybrid model for modeling processes in semiconductor processing equipment. In a particular embodiment, method of creating a hybrid machine learning model comprises identifying a first set of cases spanning a first range of process and/or hardware parameters, and running experiments in a lab for the first set of cases. The method may further comprise compiling experimental outputs from the experiments, and running physics based simulations for the first set of cases. In an embodiment, the method may further comprise compiling model outputs from the simulations, and correlating the model outputs with the experimental outputs with a machine learning algorithm to provide the hybrid machine learning model.
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公开(公告)号：US20240310819A1
公开(公告)日：2024-09-19
申请号：US18679298
申请日：2024-05-30
Applicant: Applied Materials, Inc.
Inventor： Ala Moradian , Elizabeth Neville , Umesh Madhav Kelkar , Mark R. Denome , Prashanth Kothnur , Karthik Ramanathan , Kartik Shah , Orlando Trejo , Sergey Meirovich
IPC: G05B19/418 , G05B13/02 , H01L21/67
CPC classification number: G05B19/41885 , G05B13/027 , G05B2219/32335 , G05B2219/32359 , G05B2219/45031 , H01L21/67276
Abstract: A first selection of a first fabrication process and/or first manufacturing equipment to perform manufacturing operations of the first fabrication process is received. The first selection is input into a digital replica of the first manufacturing equipment, where the digital replica outputs physical conditions of the first fabrication process. Environmental resource usage data indicative of a first environmental resource consumption of the first fabrication process run on the first manufacturing equipment based on the physical conditions of the first fabrication process is determined. A modification to the first fabrication process that reduces the environmental resource consumption of the first fabrication process run on the first manufacturing equipment is determined. Applying the modification to the first fabrication and/or providing the modification for display by a graphical user interface (GUI) is performed.
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公开(公告)号：US10519547B2
公开(公告)日：2019-12-31
申请号：US15000971
申请日：2016-01-19
Applicant: Applied Materials, Inc.
Inventor： Karthik Ramanathan , Kartik Shah , Nyi O. Myo , Schubert S. Chu , Jeffrey Tobin , Errol Antonio C. Sanchez , Palamurali Gajendra
IPC: C23C16/458 , H01L21/687 , H01L21/67
Abstract: Embodiments of the present disclosure generally relate to a susceptor for thermal processing of semiconductor substrates. In one embodiment, the susceptor includes a first rim, an inner region coupled to and surrounded by the first rim, and one or more annular protrusions formed on the inner region. The one or more annular protrusions may be formed on the inner region at a location corresponding to the location where a valley is formed on the substrate, and the one or more annular protrusions help reduce or eliminate the formation of the valley.
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公开(公告)号：US20160068958A1
公开(公告)日：2016-03-10
申请号：US14785009
申请日：2014-04-10
Applicant: Umesh M KELKAR , Kallol BERA , Karthik RAMANATHAN , Garry K KWONG , Joseph YUDOVSKY , Applied Materials, Inc.
Inventor： Umesh M. Kelkar , Kallol Bera , Karthik Ramanathan , Garry K Kwong , Joseph Yudovsky
IPC: C23C16/46 , C23C16/458 , C23C16/455
CPC classification number: C23C16/46 , C23C16/45544 , C23C16/4584 , C23C16/481 , C23C16/52 , H01L21/67115
Abstract: Apparatus and methods for processing a plurality of semiconductor wafers on a susceptor assembly so that the temperature across the susceptor assembly is uniform are described. A plurality of linear lamps are positioned and controlled in zones to provide uniform heating.
Abstract translation: 描述了在基座组件上处理多个半导体晶片的装置和方法，使得基座组件上的温度是均匀的。 多个线性灯被定位和控制在区域中以提供均匀的加热。
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Patent Agency Ranking
公开(公告)号：US20250155883A1
公开(公告)日：2025-05-15
申请号：US18939065
申请日：2024-11-06
Applicant: Applied Materials, Inc.
Inventor： Prashanth Kothnur , Ala Moradian , Umesh Madhav Kelkar , Phillip Stout , Badri Ramamurthi , Karthik Ramanathan , Ananth Bhoj , Dalong Zhao
IPC: G05B19/418
Abstract: In one aspect of the present disclosure, a method includes obtaining, by a processing device, input data indicative of a first set of process parameters. The method further includes providing the input data to a first process model. The method further includes obtaining, from the first process model, first predictive output indicative of performance of a first process operation in accordance with the first set of process parameters. The method further includes providing the first predictive output to a second process model. The method further includes obtaining, from the second process model, second predictive output indicative of performance of a second process operation, different than the first process operation or a repetition of the first process operation, in accordance with the first set of process parameters. The method further includes performing a corrective action in view of the second predictive output.
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公开(公告)号：US12001197B2
公开(公告)日：2024-06-04
申请号：US17230897
申请日：2021-04-14
Applicant: Applied Materials, Inc.
Inventor： Ala Moradian , Elizabeth Neville , Umesh Madhav Kelkar , Mark R. Denome , Prashanth Kothnur , Karthik Ramanathan , Kartik Shah , Orlando Trejo , Sergey Meirovich
IPC: H01L21/67 , G05B13/02 , G05B19/418 , G05B19/41
CPC classification number: G05B19/41885 , G05B13/027 , G05B2219/32335 , G05B2219/32359 , G05B2219/45031 , H01L21/67276
Abstract: A method including receiving, by a processing device, a first selection of at least one of a first fabrication process or first manufacturing equipment to perform manufacturing operations of the first fabrication process. The method can further include inputting the first selection into a digital replica of the first manufacturing equipment wherein the digital replica outputs physical conditions of the first fabrication process. The method may further include determining environmental resource usage data indicative of a first environmental resource consumption of the first fabrication process run on the first manufacturing equipment based on the physical conditions of the first fabrication process. The processing device may further determine a modification to the first fabrication process that reduces the environmental resource consumption of the first fabrication process run on the first manufacturing equipment. The method can further include performing at least one of applying the modification to the first fabrication.
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公开(公告)号：US11835927B2
公开(公告)日：2023-12-05
申请号：US18068469
申请日：2022-12-19
Applicant: APPLIED MATERIALS, INC.
Inventor： Kartik B Shah , Satish Radhakrishnan , Karthik Ramanathan , Karthikeyan Balaraman , Adolph Miller Allen , Xinyuan Chong , Mitrabhanu Sahu , Wenjing Xu , Michael Sterling Jackson , Weize Hu , Feng Chen
CPC classification number: G05B13/0265 , G05B13/048
Abstract: Process recipe data associated a process to be performed for a substrate at a process chamber is provided as input to a trained machine learning model. A set of process recipe settings for the process that minimizes scratching on one or more surfaces of the substrate is determined based on one or more outputs of the machine learning model. The process is performed for the substrate at the process chamber in accordance with the determined set of process recipe settings.
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公开(公告)号：US20220413452A1
公开(公告)日：2022-12-29
申请号：US17360652
申请日：2021-06-28
Applicant: APPLIED MATERIALS, INC.
Inventor： Kartik B. Shah , Satish Radhakrishnan , Karthik Ramanathan , Karthikeyan Balaraman , Adolph Miller Allen , Xinyuan Chong , Mitrabhanu Sahu , Wenjing Xu , Michael Sterling Jackson , Weize Hu , Feng Chen
Abstract: Methods and systems for reducing substrate particle scratching using machine learning are provided. A machine learning model is trained to predict process recipe settings for a substrate temperature control process to be performed for a current substrate at a manufacturing system. First training data and second training data are generated for the machine learning model. The first training data includes historical data associated with prior process recipe settings for a prior substrate temperature control process performed for a prior substrate at a prior process chamber. The second training data is associated with a historical scratch profile of one or more surfaces of the prior substrate after performance of the prior substrate temperature control process according to the prior process recipe settings. The first training data and the second training data are provided to train the machine learning model to predict which process recipe settings for the substrate temperature control process to be performed for the current substrate correspond to a target scratch profile for one or more surfaces of the current substrate.
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公开(公告)号：US20220092241A1
公开(公告)日：2022-03-24
申请号：US16948522
申请日：2020-09-22
Applicant: Applied Materials, Inc.
Inventor： Ala Moradian , James Omer L'Heureux , Shuran Sheng , Rohit Mahakali , Karthik Ramanathan , Lin Zhang , Umesh Madhav Kelkar , Gopalakrishna B. Prabhu , Zheng Yuan , Jeonghoon Oh
Abstract: A method includes receiving measurement data from multiple sensors positioned along a delivery line that delivers a liquid as a gas to one of a gas panel or a processing chamber; simulating, using a computer-generated model, one or more process parameters associated with the delivery line and a plurality of heater jackets positioned around the delivery line; comparing the measurement data with values of the one or more process parameters; and determining, based on at least a threshold deviation between the measurement data and the values of the one or more process parameters, that a fault exists that is associated with maintaining temperature within the delivery line consistent with a gaseous state of the liquid.
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