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    Methods and apparatus for maintaining low non-uniformity over target life
    4.
    发明授权
    Methods and apparatus for maintaining low non-uniformity over target life 有权

    公开(公告)号：US10283334B2

    公开(公告)日：2019-05-07

    申请号：US14522066

    申请日：2014-10-23

    Applicant: APPLIED MATERIALS, INC.

    Inventor： William Johanson ,  Fuhong Zhang ,  Adolph Miller Allen ,  Yu Liu

    IPC: C23C14/34 ,  H01J37/34 ,  C23C14/54 ,  H01J37/32 ,  C23C14/50

    Abstract: Embodiments of improved methods and apparatus for maintaining low non-uniformity over the course of the life of a target are provided herein. In some embodiments, a method of processing a substrate in a physical vapor deposition chamber includes: disposing a substrate atop a substrate support having a cover ring that surrounds the substrate support such that an upper surface of the substrate is positioned at a first distance above an upper surface of the cover ring; sputtering a source material from a target disposed opposite the substrate support to deposit a film atop the substrate while maintaining the first distance; and lowering the substrate support with respect to the cover ring and sputtering the source material from the target to deposit films atop subsequent substrates over a life of the target.

    High density TiN RF/DC PVD deposition with stress tuning
    6.
    发明授权
    High density TiN RF/DC PVD deposition with stress tuning 有权
    高密度TiN RF / DC PVD沉积与应力调谐

    公开(公告)号：US09499901B2

    公开(公告)日：2016-11-22

    申请号：US13750318

    申请日：2013-01-25

    Applicant: APPLIED MATERIALS, INC.

    Inventor： Yong Cao ,  Xianmin Tang ,  Adolph Miller Allen ,  Tza-Jing Gung

    IPC: C23C14/34 ,  C23C14/35 ,  C23C14/54 ,  H01J37/34

    CPC classification number: C23C14/345 ,  C23C14/351 ,  C23C14/54 ,  H01J37/34

    Abstract: Methods for depositing a layer on a substrate are provided herein. In some embodiments, a method of depositing a metal-containing layer on a substrate in a physical vapor deposition (PVD) chamber may include applying RF power at a VHF frequency to a target comprising a metal disposed in the PVD chamber above the substrate to form a plasma from a plasma-forming gas; optionally applying DC power to the target; sputtering metal atoms from the target using the plasma while maintaining a first pressure in the PVD chamber sufficient to ionize a predominant portion of the sputtered metal atoms; and controlling the potential on the substrate to be the same polarity as the ionized metal atoms to deposit a metal-containing layer on the substrate.

    Abstract translation: 本文提供了在基板上沉积层的方法。 在一些实施例中，在物理气相沉积（PVD）室中在衬底上沉积含金属层的方法可以包括以VHF频率将RF功率施加到包括设置在衬底上方的PVD室中的金属的靶，以形成 来自等离子体形成气体的等离子体; 可选地向目标施加DC电力; 使用等离子体从靶中溅射金属原子，同时保持PVD室中的第一压力足以离子化主要部分的溅射金属原子; 并且将基板上的电位控制为与电离金属原子相同的极性，以在基板上沉积含金属层。

    Reducing substrate surface scratching using machine learning
    9.
    发明授权
    Reducing substrate surface scratching using machine learning 有权

    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

    IPC: H05B3/68 ,  G05B13/02 ,  G05B13/04

    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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