Failure detection and classsification using sensor data and/or measurement data
AssigneePDF Solutions, Inc.
InventorsTomonori Honda, Lin Lee Cheong, Lakshmikar Kuravi
Life of this patent
Estimating the term…
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.
Claims19
A method for generating a robust predictive model for failure detection and classification in a semiconductor process, comprising:
- collecting a first set of input data from a plurality of data sources during a plurality of production runs for making semiconductor devices according to a semiconductor process;
- organizing the first set of input data into a plurality of training datasets;
- converting selected portions of the plurality of training datasets into a plurality of target features for the semiconductor manufacturing process;
- modifying the plurality of converted training datasets by removing one or more of the plurality of target features that exhibit negligible impact on classifying the semiconductor devices as normal or abnormal;
- analyzing a second set of input data using a plurality of machine learning models, each machine learning model trained using at least one of the plurality of modified training datasets and configured to classify the semiconductor devices as either normal or abnormal;
- and
- selecting one of the plurality of machine learning models for deployment in the semiconductor process.
A method, comprising:
- obtaining a plurality of training datasets, each training dataset having input data from a plurality of data sources associated with a semiconductor manufacturing process;
- converting selected portions of the plurality of training datasets into a plurality of target features for the semiconductor manufacturing process;
- modifying the plurality of converted training datasets by removing one or more of the plurality target features that exhibit negligible impact on classifying devices made from the semiconductor manufacturing process as normal or abnormal;
- creating a plurality of models for the plurality of target features, and training the plurality of models using the plurality of modified training datasets;
- and
- selecting one of the plurality of models to deploy for the semiconductor manufacturing process.
A computing system having a processor and non-transitory storage for program instruction, the program instructions configured for causing the processor to:
- obtain a plurality of training datasets, each training dataset having input data from a plurality of data sources associated with a semiconductor manufacturing process;
- convert selected portions of the plurality of training datasets into a plurality of target features for the semiconductor manufacturing process;
- modify the plurality of converted training datasets to remove one or more of the plurality target features that exhibit negligible impact on classifying devices made from the semiconductor manufacturing process as normal or abnormal;
- create a plurality of models for the plurality of target features, and training the plurality of models using the plurality of modified training datasets;
- and
- select one of the plurality of models to deploy for the semiconductor manufacturing process.
Dependencies are inferred from the claim text.