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US 2019/0004504 A1Published application
Systems and methods for predicting defects and critical dimension using deep learning in the semiconductor manufacturing process
AssigneeKLA-Tencor Corporation
InventorArpit Yati
Life of this patent
Estimating the term…
Abstract
An initial inspection or critical dimension measurement can be made at various sites on a wafer. The location, design clips, process tool parameters, or other parameters can be used to train a deep learning model. The deep learning model can be validated and these results can be used to retrain the deep learning model. This process can be repeated until the predictions meet a detection accuracy threshold. The deep learning model can be used to predict new probable defect location or critical dimension failure sites.
Claims20
A method comprising:
- scanning a wafer with an inspection tool;
- confirming presence of at least one defect with a defect review tool;
- inputting parameters into a deep learning model, wherein the parameters include one or more of:
- a location of the defect with respect to one or more of a design, a care area, or a design clip;
- focus;
- exposure;
- a type of the defect;
- neighboring design sites;
- and
- a layer type;
- predicting defect sites, using a controller, based on the deep learning model;
- validating the defect sites;
- and
- retraining the deep learning model, wherein the predicting, the validating, and the retraining are repeated until the deep learning model meets a detection accuracy threshold.
11 more elements
A method comprising:
- scanning a wafer with a scanning electron microscope;
- calculating, using a controller, critical dimension variation across the wafer at sample sites;
- inputting parameters into a deep learning model, wherein the parameters include one or more of:
- a location where critical dimension is measured with respect to one of a design, a care area, or a design clip;
- focus;
- exposure;
- neighboring design sites;
- and
- a layer type;
- predicting critical dimension at sites across the wafer, using the controller, based on the deep learning model;
- validating the sites with different critical dimensions;
- and
- retraining the deep learning model, wherein the predicting, the validating, and the retraining are repeated until the deep learning model meets a detection accuracy threshold.
10 more elements
A system comprising:
- a controller in electronic communication with a scanning electron microscope or with the scanning electron microscope and an optical inspection tool, wherein the controller includes a processor and an electronic data storage unit in electronic communication with the processor, and wherein the controller is configured to:
- receive results of a review of a wafer, wherein the results are one of defect locations or critical dimension;
- input parameters in a deep learning model, wherein the parameters include one or more of:
- a location where the results are measured with respect to one of a design, a care area, or a design clip;
- focus;
- exposure;
- neighboring design sites;
- and
- a layer type;
- predict additional results at sites across the wafer, wherein the additional results are the same as the one of the defect locations or the critical dimension;
- receive validation of the results at the sites across the wafer;
- and
- retrain the deep learning model, wherein the predict step, the validate step, and the retrain step are repeated until the deep learning model meets a detection accuracy threshold.
10 more elements
Dependencies are inferred from the claim text.