Pdf Solutions, Incorporated

Lakshmikar Kuravi

Lin Lee Cheong

Tomonori Honda

Jeffrey Drue David

Rohan Deodatta Kekatpure

Bogdan Cirlig

A robust predictive model. A plurality of different predictive models for a target feature are run, and a comparative analysis provided for each predictive model that meet minimum performance criteria for the target feature. One of the predictive models is selected, either manually or automatically, based on predefined criteria. For semi-automatic selection, a static or dynamic survey is generated for obtaining user preferences for parameters associated with the target feature. The survey results will be used to generate a model that illustrates parameter trade-offs, which will be used to finalize the optimal predictive model for the user.

Rational decision-making tool for semiconductor processes

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.

Failure detection and classsification using sensor data and/or measurement data

Testing data is evaluated by machine learning tools to determine whether to include or exclude chips from further testing.

Growing community of inventors

Campbell, CA, United States of America

Lakshmikar Kuravi