- Patent Title: Prediction of process-sensitive geometries with machine learning
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Application No.: US15588984Application Date: 2017-05-08
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Publication No.: US10402524B2Publication Date: 2019-09-03
- Inventor: Liang Cao , Jie Zhang , David N. Power , Eric S. Parent
- Applicant: GLOBALFOUNDRIES Inc.
- Applicant Address: KY Grand Cayman
- Assignee: GLOBALFOUNDRIES INC.
- Current Assignee: GLOBALFOUNDRIES INC.
- Current Assignee Address: KY Grand Cayman
- Agency: Hoffman Warnick LLC
- Agent Anthony Canale
- Main IPC: G06F17/50
- IPC: G06F17/50 ; G03F1/36

Abstract:
Methods according to the disclosure include: predicting process-sensitive geometries (PSGs) in a proposed IC layout based on violations of a set of processing constraints for the proposed IC layout, the set of processing constraints being calculated with a predictive model based on a training data repository having a plurality of optical rule check (ORC) simulations for different IC layouts; identifying actual PSGs in a circuit manufactured using the proposed IC layout; determining whether the predicted PSGs correspond to the actual PSGs in the manufactured circuit as being correct; in response to the predicting being incorrect: adjusting the predictive model based on the actual PSGs, wherein the adjusting includes submitting additional ORC data to the training data repository; and flagging the proposed IC layout as incorrectly predicted; and in response to the predicting being correct, flagging the proposed IC layout as correctly predicted.
Public/Granted literature
- US20180322234A1 PREDICTION OF PROCESS-SENSITIVE GEOMETRIES WITH MACHINE LEARNING Public/Granted day:2018-11-08
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