Patents by Inventor Hang Duk JUNG

Hang Duk JUNG 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).

  • Machine learning-based semiconductor manufacturing yield prediction system and method
    Patent number: 11494636
    Abstract: Provided is a machine learning-based semiconductor manufacturing yield prediction system and method. A result prediction method according to an embodiment of the present invention comprises: learning different neural network models by classifying different types of data according to their types and respectively inputting the classified different types of data to the different neural network models; and predicting result values by classifying input data according to their types and respectively inputting the classified input data to different neural network models. Therefore, it is possible to apply different neural network models to respective data according to their types, thereby ensuring a neural network model having a structure appropriate for the characteristics of each type of data and thus accurately predicting a result value.
    Type: Grant
    Filed: November 29, 2017
    Date of Patent: November 8, 2022
    Assignee: SK HOLDINGS CO., LTD.
    Inventors: Hang Duk Jung, Yong Sik Moon, Myung Seung Son, Min Hwan Lee, Jun Taek Park
  • MACHINE LEARNING-BASED SEMICONDUCTOR MANUFACTURING YIELD PREDICTION SYSTEM AND METHOD
    Publication number: 20190286983
    Abstract: Provided is a machine learning-based semiconductor manufacturing yield prediction system and method. A result prediction method according to an embodiment of the present invention comprises: learning different neural network models by classifying different types of data according to their types and respectively inputting the classified different types of data to the different neural network models; and predicting result values by classifying input data according to their types and respectively inputting the classified input data to different neural network models. Therefore, it is possible to apply different neural network models to respective data according to their types, thereby ensuring a neural network model having a structure appropriate for the characteristics of each type of data and thus accurately predicting a result value.
    Type: Application
    Filed: November 29, 2017
    Publication date: September 19, 2019
    Applicant: SK HOLDINGS CO., LTD.
    Inventors: Hang Duk JUNG, Yong Sik MOON, Myung Seung SON, Min Hwan LEE, Jun Taek PARK