Patents by Inventor Yifeng TAO

Yifeng TAO has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Deep learning-based root cause analysis of process cycle images
    Patent number: 12272050
    Abstract: The technology disclosed relates to training a convolutional neural network (CNN) to identify and classify images of sections of an image generating chip resulting in process cycle failures. The technology disclosed includes creating a training data set of images of dimensions M×N using labeled images of sections of image generating chip of dimensions J×K. The technology disclosed can fill the M×N frames using horizontal and vertical reflections along edges of J×K labeled images positioned in M×N frames. A pretrained CNN is further trained using the training data set. Trained CNN can classify a section image as normal or depicting failure. The technology disclosed can train a root cause CNN to classify process cycle images of sections causing process cycle failure. The trained CNN can classify a section image by root cause of process failure among a plurality of failure categories.
    Type: Grant
    Filed: January 28, 2022
    Date of Patent: April 8, 2025
    Assignee: Illumina, Inc.
    Inventors: Kimberly Jean Gietzen, Jingtao Liu, Yifeng Tao
  • DEEP LEARNING-BASED ROOT CAUSE ANALYSIS OF PROCESS CYCLE IMAGES
    Publication number: 20220245801
    Abstract: The technology disclosed relates to training a convolutional neural network (CNN) to identify and classify images of sections of an image generating chip resulting in process cycle failures. The technology disclosed includes creating a training data set of images of dimensions M×N using labeled images of sections of image generating chip of dimensions J×K. The technology disclosed can fill the M×N frames using horizontal and vertical reflections along edges of J×K labeled images positioned in M×N frames. A pretrained CNN is further trained using the training data set. Trained CNN can classify a section image as normal or depicting failure. The technology disclosed can train a root cause CNN to classify process cycle images of sections causing process cycle failure. The trained CNN can classify a section image by root cause of process failure among a plurality of failure categories.
    Type: Application
    Filed: January 28, 2022
    Publication date: August 4, 2022
    Applicant: Illumina, Inc.
    Inventors: Kimberly Jean GIETZEN, Jingtao LIU, Yifeng TAO