Defect detection, classification, and process window control using scanning electron microscope metrology
The processor receives the semiconductor wafer image, and combines the machine learning algorithm and critical size uniformity parameters to solve the problem of defect detection and classification in the existing technology, achieve efficient classification and training of semiconductor wafer defects, and improve the quality of manufacturing. Rate.
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[0035] While claimed subject matter will be described in terms of particular embodiments, other embodiments, including embodiments that do not provide all of the benefits and features set forth herein, are also within the scope of the disclosure. Various structural, logical, process step and electrical changes may be made without departing from the scope of the present invention. Accordingly, the scope of the present invention is defined only with reference to the appended claims.
[0036] The output of critical dimension uniformity (CDU) features on SEM re-inspection or inspection platforms can be integrated into defect classification exercises. Metrological features such as CDU measure pattern fidelity metrics such as pattern width, length, diameter, area, angle, roughness, edge placement error, etc. The automated defect classification platform can automatically classify defects based on specific defect attributes or can use a neural network that has been trained to recogni...
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