Applied Materials, Inc.

Sony Group Corporation

Sony Corporation

Anubhav Srivastava

Kirankumar Neelasandra Savandaiah

Thomas B Brezoczky

Bhaskar Prasad

Nitin Bharadwaj Satyavolu

Venkatesh Dharamukkala

Srinivasa Rao Yedla

Govinda Raj

Kaushik Rao

Lakshmikanth Krishnamurthy Shirahatti

Sankaran Kesavan

Parag Vaishnava

Mahima Rao

Teli Pooja

Sankaran Kesavan Namboodiri

Santhosh Kumar Pillappa

Gopinath Ramananda

Embodiments described herein relate to methods and apparatus for detecting and/or monitoring, e.g., abnormalities in wafer transfer and handling. In an embodiment, a method for wafer dechucking verification is provided. The method includes initiating a wafer transfer operation to transfer a wafer between components of a semiconductor processing system, the semiconductor processing system comprising a motor coupled to a lift pin, the motor configured to adjust a height of the lift pin above a pedestal, the lift pin for raising or lowering the wafer. The method further includes measuring one or more first parameters during the wafer transfer operation, comparing the one or more first parameters to one or more first pre-determined parameter ranges, and changing a force applied to the lift pin based on the one or more first parameters. Apparatus for wafer dechucking verification and non-transitory computer-readable mediums storing instructions for wafer dechucking verification are also provided

Methods and apparatus for wafer detection

An electronic device and method for intelligent audio processing is provided. The electronic device receives a first user input for selection of a source audio signal. The electronic device receives a second user input for selection of a first operation to be performed on the source audio signal. The electronic device receives a third user input for selection of at least a first audio signal. The electronic device applies a first machine learning (ML) model on the source audio signal. The electronic device further extracts at least the first audio signal from the source audio signal based on the application of the first ML model on the source audio signal. The electronic device further applies a second ML model on the extracted at least first audio signal from the plurality of audio signals based on the first operation. The electronic device further generates output information as per the first operation.

Intelligent audio processing

Embodiments of the present disclosure generally relate to lift pins and to apparatus for controlling lift pin movement. In an embodiment, an apparatus for positioning a substrate in a chamber is provided. The apparatus includes a chamber component, a lift pin having a top surface for supporting the substrate and a lift pin shaft and a stopper. The apparatus further includes a compressible element positioned between the chamber component and the stopper, the compressible element further positioned around the lift pin shaft, the lift pin being moveable relative to a substrate transfer plane by movement of a substrate support in contact with the compressible element.

Apparatus for controlling lift pin movement

The present disclosure generally relates to a substrate support that includes a body having a substrate receiving surface, the body comprising a dielectric material. The body also includes a first foil embedded in the body below the substrate receiving surface. The body also includes an electrically conductive mesh embedded in the body below the first foil. The body also includes a center tap structure formed in a bottom surface of the body that is in electrical communication with the mesh.

Substrate support designs for a deposition chamber

An electronic device and method for personalized fitness activity training using augmented-reality based avatar are provided. The electronic device receives a first set of images of a first user. The first set of images is captured for a duration in which the first user is engaged in a first fitness activity. The electronic device generates an augmented-reality display that includes a first avatar and an image of the first user based on the first set of images. The electronic device further controls a display device to render the generated augmented-reality display. The electronic device further determines posture information of the first user based on the first set of images. The electronic device determines real-time feedback based on application of a first neural network model on the determined posture information. The electronic device controls the first avatar to output the determined real-time feedback in the augmented-reality display.

Personalized fitness activity training using augmented-reality based avatar

An information processing device and method for prediction of a film's success-quotient is provided. The information processing device receives a film script for a film for production, identifies a plurality of scenes in the film script based on a textual analysis of the film script, and computes a first score for each scene of the film script based on genre information for plurality of scenes. The information processing device further identifies a list of characters in the film script, selects an actor for roleplay of each character of the list of characters and computes a second score that indicates a suitability of the selected actor for the roleplay. The second score is computed based on a machine learning model that is trained on datapoints of a historical film database. The information processing device predicts the success-quotient for the film based on the computed first score and the computed second score.

Prediction of film success-quotient

Embodiments of the present disclosure generally relate to methods and apparatus for processing substrates. More specifically, embodiments of the present disclosure relate to transfer apparatus and substrate-supporting members. In an embodiment, an apparatus for transferring a substrate is provided. The apparatus includes a hub and a plurality of transfer arms extending from the hub. The apparatus further includes a plurality of substrate-supporting members, wherein each of the transfer arms has a first end coupled to the hub and a second end coupled to a respective one of the plurality of substrate-supporting members. The apparatus further includes a first electrical interface connection for electrostatically chucking a substrate and located at a first position on each substrate-supporting member, and a second electrical interface connection for electrostatically chucking the substrate and located at a second position on each substrate-supporting member. Substrate processing modules are also described.

Transfer apparatus and substrate-supporting member

A calibration system for media content comprises a memory, a media device, a plurality of different types of sensors, and a control circuitry. The memory is configured to store a media item and expected-emotions-tagging metadata for the media item. The control circuitry is configured to compute a reaction delay for a first emotional response of the audience captured by the plurality of different types of sensors for a first scene of the media item with respect to a position of the first marker. The first scene of the media item spans a first time slot that corresponds to the first scene in the media item. The control circuitry is configured to calibrate the position of the first marker to associate a first set of frames of the first scene with the first emotional response, based on at least the computed reaction delay.

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Bengaluru, India

Anubhav Srivastava