Patents by Inventor Tomonori Honda

Tomonori Honda 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).

  • Patent number: 11029673
    Abstract: Robust machine learning predictions. Temporal dependencies of process targets for different machine learning models can be captured and evaluated for the impact on process performance for target. The most robust of these different models is selected for deployment based on minimizing variance for the desired performance characteristic.
    Type: Grant
    Filed: June 12, 2018
    Date of Patent: June 8, 2021
    Assignee: PDF Solutions, Inc.
    Inventors: Tomonori Honda, Rohan D. Kekatpure, Jeffrey Drue David
  • Patent number: 11029359
    Abstract: A model is generated for predicting failures at the wafer production level. Input data from sensors is stored as an initial dataset, then data exhibiting excursions or useless impact is removed from the dataset. The dataset is converted into target features, where the target features are useful in predicting whether a wafer will be normal or not. A trade-off between positive and negative results is selected, and a plurality of predictive models are created. The final model is selected based on the trade-off criteria, and deployed.
    Type: Grant
    Filed: March 8, 2019
    Date of Patent: June 8, 2021
    Assignee: PDF Solutions, Inc.
    Inventors: Tomonori Honda, Lin Lee Cheong, Lakshmikar Kuravi
  • Patent number: 11022642
    Abstract: A method for predicting yield for a semiconductor process. A particular type of wafer is fabricated to have a first set of features disposed on the wafer, with a wafer map identifying a location for each of the first set of features on the wafer. Data from wafer acceptance tests and circuit probe tests is collected over time for wafers of that particular type as made in a semiconductor fabrication process, and at least one training dataset and a least one validation dataset are created from the collected data. A second set of “engineered” features are created and also incorporated onto the wafer and wafer map. Important features from the first and second sets of features are identified and selected, and using those important features as inputs, a number of different process models are run, with yield as the target. The results of the different models can be combined, for example, statistically.
    Type: Grant
    Filed: August 24, 2018
    Date of Patent: June 1, 2021
    Assignee: PDF Solutions, Inc.
    Inventors: Jeffrey Drue David, Tomonori Honda, Lin Lee Cheong
  • Publication number: 20210142122
    Abstract: Classifying wafers using Collaborative Learning. An initial wafer classification is determined by a rule-based model. A predicted wafer classification is determined by a machine learning model. Multiple users can manually review the classifications to confirm or modify, or to add user classifications. All of the classifications are input to the machine learning model to continuously update its scheme for detection and classification.
    Type: Application
    Filed: October 14, 2020
    Publication date: May 13, 2021
    Applicant: PDF Solutions, Inc.
    Inventors: Tomonori Honda, Richard Burch, John Kibarian, Lin Lee Cheong, Qing Zhu, Vaishnavi Reddipalli, Kenneth Harris, Said Akar, Jeffrey D David, Michael Keleher, Brian Stein, Dennis Ciplickas
  • Publication number: 20210117861
    Abstract: A sequence of models accumulates r-squared values for an increasing number of variables in order to quantify the importance of each variable to the prediction of a targeted yield or parametric response.
    Type: Application
    Filed: October 16, 2020
    Publication date: April 22, 2021
    Applicant: PDF Solutions, Inc.
    Inventors: Richard Burch, Qing Zhu, Jonathan Holt, Tomonori Honda
  • Publication number: 20210103489
    Abstract: Scheme for detection and classification of semiconductor equipment faults. Sensor traces are monitored and processed to separate known abnormal operating conditions from unknown abnormal operating conditions. Feature engineering permits focus on relevant traces for a targeted feature. A machine learning model is built to detect and classify based on an initial classification set of anomalies. The machine learning model is continuously updated as more traces are processed and learned.
    Type: Application
    Filed: October 6, 2020
    Publication date: April 8, 2021
    Applicant: PDF Solutions, Inc.
    Inventors: Richard Burch, Jeffrey D. David, Qing Zhu, Tomonori Honda, Lin Lee Cheong
  • Publication number: 20200388545
    Abstract: A maintenance tool for semiconductor process equipment and components. Sensor data is evaluated by machine learning tools to determine when to schedule maintenance action.
    Type: Application
    Filed: August 25, 2020
    Publication date: December 10, 2020
    Applicant: PDF Solutions, Inc.
    Inventors: Tomonori Honda, Jeffrey Drue David, Lin Lee Cheong
  • Patent number: 10777470
    Abstract: Testing data is evaluated by machine learning tools to determine whether to include or exclude chips from further testing.
    Type: Grant
    Filed: March 26, 2019
    Date of Patent: September 15, 2020
    Assignee: PDF Solutions, Inc.
    Inventors: Lin Lee Cheong, Tomonori Honda, Rohan D. Kekatpure, Lakshmikar Kuravi, Jeffrey Drue David
  • Patent number: 10558766
    Abstract: A new and/or improved method, apparatus and/or system is disclosed which aids in extending correct behavioral models to include fault modes and in fault mode analysis of components and/or systems in simulated model environments, including, e.g., FMEA and FMECA and diagnostic fault tree generation.
    Type: Grant
    Filed: December 28, 2016
    Date of Patent: February 11, 2020
    Assignee: Palo Alto Research Center Incorporated
    Inventors: Bhaskar Saha, Tomonori Honda, Ion Matei, Daniel G. Bobrow, Johan Dekleer, William C. Janssen, Tolga Kurtoglu
  • Publication number: 20190304849
    Abstract: Testing data is evaluated by machine learning tools to determine whether to include or exclude chips from further testing.
    Type: Application
    Filed: March 26, 2019
    Publication date: October 3, 2019
    Applicant: StreamMosaic, Inc.
    Inventors: Lin Lee Cheong, Tomonori Honda, Rohan D. Kekatpure, Lakshmikar Kuravi, Jeffrey Drue David
  • Publication number: 20180356807
    Abstract: Robust machine learning predictions. Temporal dependencies of process targets for different machine learning models can be captured and evaluated for the impact on process performance for target. The most robust of these different models is selected for deployment based on minimizing variance for the desired performance characteristic.
    Type: Application
    Filed: June 12, 2018
    Publication date: December 13, 2018
    Inventors: Tomonori Honda, Rohan D. Kekatpure, Jeffrey Drue David
  • Patent number: 10078062
    Abstract: A method and system for detecting fault in a machine. During operation, the system obtains control signals and corresponding sensor data that indicates a condition of the machine. The system determines consistent time intervals for each of the control signals. During a consistent time interval the standard deviation of a respective control signal is less than a respective predetermined threshold. The system aggregates the consistent time intervals to determine aggregate consistent intervals. The system then maps the aggregate consistent intervals to the sensor data to determine time interval segments for the sensor data. The system may generate features based on the sensor data. Each respective feature is generated from a time interval segment of the sensor data. The system trains a classifier using the features, and applies the classifier to additional sensor data indicating a condition of the machine over a period of time to detect a machine fault.
    Type: Grant
    Filed: December 15, 2015
    Date of Patent: September 18, 2018
    Assignee: PALO ALTO RESEARCH CENTER INCORPORATED
    Inventors: Hoda M. A. Eldardiry, Linxia Liao, Tomonori Honda, Bhaskar Saha, Rui Abreu
  • Publication number: 20170193143
    Abstract: A new and/or improved method, apparatus and/or system is disclosed which aids in extending correct behavioral models to include fault modes and in fault mode analysis of components and/or systems in simulated model environments, including, e.g., FMEA and FMECA and diagnostic fault tree generation.
    Type: Application
    Filed: December 28, 2016
    Publication date: July 6, 2017
    Applicant: Palo Alto Research Center Incorporated
    Inventors: Bhaskar Saha, Tomonori Honda, Ion Matei, Daniel G. Bobrow, Johan Dekleer, William C. Janssen, Tolga Kurtolglu
  • Publication number: 20170167993
    Abstract: A method and system for detecting fault in a machine. During operation, the system obtains control signals and corresponding sensor data that indicates a condition of the machine. The system determines consistent time intervals for each of the control signals. During a consistent time interval the standard deviation of a respective control signal is less than a respective predetermined threshold. The system aggregates the consistent time intervals to determine aggregate consistent intervals. The system then maps the aggregate consistent intervals to the sensor data to determine time interval segments for the sensor data. The system may generate features based on the sensor data. Each respective feature is generated from a time interval segment of the sensor data. The system trains a classifier using the features, and applies the classifier to additional sensor data indicating a condition of the machine over a period of time to detect a machine fault.
    Type: Application
    Filed: December 15, 2015
    Publication date: June 15, 2017
    Applicant: Palo Alto Research Center Incorporated
    Inventors: Hoda M. A. Eldardiry, Linxia Liao, Tomonori Honda, Bhaskar Saha, Rui Abreu
  • Patent number: 5368665
    Abstract: A method of applying porous building plates includes applying plate-like porous building plates having pores on the surfaces and then jointing them by means of a coated joint method. In the method, the pores on the surfaces of the porous building plates are packed with an acid soluble substance and then the plates are joined by means of a coated joint method. Subsequently, the surfaces of the porous building plates are washed with an acid.
    Type: Grant
    Filed: December 1, 1992
    Date of Patent: November 29, 1994
    Assignee: INAX Corporation
    Inventors: Akihisa Koyama, Syoji Kitahara, Tomonori Honda