Patents by Inventor Bambi L. DeLaRosa

Bambi L. DeLaRosa 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).

  • Wafer-scale memory techniques
    Patent number: 11621257
    Abstract: Techniques for wafer-scale memory device and systems are provided. In an example, a wafer-scale memory device can include a large single substrate, multiple memory circuit areas including dynamic random-access memory (DRAM), the multiple memory circuit areas integrated with the substrate and configured to form an array on the substrate, and multiple streets separating the memory circuit areas. The streets can accommodate attaching the substrate to a wafer-scale processor. In certain examples, the large, single substrate can have a major surface area of more than 20,000 square millimeters (mm2).
    Type: Grant
    Filed: January 29, 2021
    Date of Patent: April 4, 2023
    Assignee: Micron Technology, Inc.
    Inventors: Brent Keeth, Bambi L. DeLaRosa, Eiichi Nakano
  • BUILDING UNITS FOR MACHINE LEARNING MODELS FOR DENOISING IMAGES AND SYSTEMS AND METHODS FOR USING SAME
    Publication number: 20220309618
    Abstract: In some examples, a machine learning model may be trained to denoise an image. In some examples, the machine learning model may identify noise in an image of a sequence based at least in part, on at least one other image of the sequence. In some examples, the machine learning model may include a recurrent neural network. In some examples, the machine learning model may have a modular architecture including one or more building units. In some examples, the machine learning model may have a multi-branch architecture. In some examples, the noise may be identified and removed from the image by an iterative process.
    Type: Application
    Filed: August 18, 2021
    Publication date: September 29, 2022
    Applicant: MICRON TECHNOLOGY, INC.
    Inventors: Bambi L. DeLaRosa, Katya Giannios, Abhishek Chaurasia
  • BUILDING UNITS FOR MACHINE LEARNING MODELS FOR DENOISING IMAGES AND SYSTEMS AND METHODS FOR USING SAME
    Publication number: 20220300789
    Abstract: In some examples, a machine learning model may be trained to denoise an image. In some examples, the machine learning model may identify noise in an image of a sequence based at least in part, on at least one other image of the sequence. In some examples, the machine learning model may include a recurrent neural network. In some examples, the machine learning model may have a modular architecture including one or more building units. In some examples, the machine learning model may have a multi-branch architecture. In some examples, the noise may be identified and removed from the image by an iterative process.
    Type: Application
    Filed: August 18, 2021
    Publication date: September 22, 2022
    Applicant: MICRON TECHNOLOGY, INC.
    Inventors: Bambi L DeLaRosa, Katya Giannios, Abhishek Chaurasia
  • SYSTEMS AND METHODS FOR TRAINING MACHINE LEARNING MODELS FOR DENOISING IMAGES
    Publication number: 20220300791
    Abstract: In some examples, a machine learning model may be trained to denoise an image. In some examples, the machine learning model may identify noise in an image of a sequence based at least in part, on at least one other image of the sequence. In some examples, the machine learning model may include a recurrent neural network. In some examples, the machine learning model may have a modular architecture including one or more building units. In some examples, the machine learning model may have a multi-branch architecture. In some examples, the noise may be identified and removed from the image by an iterative process.
    Type: Application
    Filed: August 18, 2021
    Publication date: September 22, 2022
    Applicant: MICRON TECHNOLOGY, INC.
    Inventors: Bambi L. DeLaRosa, Katya Giannios, Abhishek Chaurasia
  • MODULAR MACHINE LEARNING MODELS FOR DENOISING IMAGES AND SYSTEMS AND METHODS FOR USING SAME
    Publication number: 20220301112
    Abstract: In some examples, a machine learning model may be trained to denoise an image. In some examples, the machine learning model may identify noise in an image of a sequence based at least in part, on at least one other image of the sequence. In some examples, the machine learning model may include a recurrent neural network. In some examples, the machine learning model may have a modular architecture including one or more building units. In some examples, the machine learning model may have a multi-branch architecture. In some examples, the noise may be identified and removed from the image by an iterative process.
    Type: Application
    Filed: August 18, 2021
    Publication date: September 22, 2022
    Applicant: MICRON TECHNOLOGY, INC.
    Inventors: Bambi L. DeLaRosa, Katya Giannios, Abhishek Chaurasia
  • MODULAR MACHINE LEARNING MODELS FOR DENOISING IMAGES AND SYSTEMS AND METHODS FOR USING SAME
    Publication number: 20220301113
    Abstract: In some examples, a machine learning model may be trained to denoise an image. In some examples, the machine learning model may identify noise in an image of a sequence based at least in part, on at least one other image of the sequence. In some examples, the machine learning model may include a recurrent neural network. In some examples, the machine learning model may have a modular architecture including one or more building units. In some examples, the machine learning model may have a multi-branch architecture. In some examples, the noise may be identified and removed from the image by an iterative process.
    Type: Application
    Filed: August 18, 2021
    Publication date: September 22, 2022
    Applicant: MICRON TECHNOLOGY, INC.
    Inventors: Bambi L. DeLaRosa, Katya Giannios, Abhishek Chaurasia
  • WAFER-SCALE MEMORY TECHNIQUES
    Publication number: 20210240344
    Abstract: Techniques for wafer-scale memory device and systems are provided. In an example, a wafer-scale memory device can include a large single substrate, multiple memory circuit areas including dynamic random-access memory (DRAM), the multiple memory circuit areas integrated with the substrate and configured to form an array on the substrate, and multiple streets separating the memory circuit areas. The streets can accommodate attaching the substrate to a wafer-scale processor. In certain examples, the large, single substrate can have a major surface area of more than 20,000 square millimeters (mm2).
    Type: Application
    Filed: January 29, 2021
    Publication date: August 5, 2021
    Inventors: Brent Keeth, Bambi L. DeLaRosa, Eiichi Nakano