Interpretable deep learning-based defect detection and classification
AssigneeKLA Corporation
InventorsXu Zhang, Li He, Sankar Venkataraman
Life of this patent
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Abstract
An explanation of a detection/classification algorithm made using a deep learning neural network clarifies the results that are formed and helps a user to identify the root cause of defect detection/classification model performance issues. A relevance map is determined based on a layer-wise relevance propagation algorithm. A mean intersection over union score between the relevance map and a ground truth is determined. A part of one of the semiconductor images that contributed to the classification using the deep learning model based on the relevance map and the mean intersection over union score is determined.
Claims18
A method comprising:
- performing defect detection and classification on a plurality of semiconductor images using a processor with a deep learning model;
- applying, using the processor, a layer-wise relevance propagation algorithm on the semiconductor images from the deep learning model;
- determining, using the processor, a relevance map based on the layer-wise relevance propagation algorithm;
- determining, using the processor, a mean intersection over union score between the relevance map and a ground truth;
- and
- determining, using the processor, a part of one of the semiconductor images that contributed to the classification using the deep learning model based on the relevance map and the mean intersection over union score.
A wafer inspection tool comprising:
- an energy beam source that generates an energy beam;
- a stage configured to hold a wafer;
- a detector that receives the energy beam returned from the wafer;
- and
- a processor in electronic communication with the detector, wherein the processor is configured to
- generate a plurality of semiconductor images using data received from the detector;
- perform defect detection and classification on the plurality of semiconductor images using a deep learning model;
- apply a layer-wise relevance propagation algorithm on the semiconductor images from the deep learning model;
- determine a relevance map based on the layer-wise relevance propagation algorithm;
- determine a mean intersection over union score between the relevance map and a ground truth;
- and
- determine a part of one of the semiconductor images that contributed to the classification using the deep learning model based on the relevance map and the mean intersection over union score.
A non-transitory computer-readable storage medium, comprising one or more programs for executing the following steps on one or more computing devices:
- performing defect detection and classification on a plurality of semiconductor images using a deep learning model;
- applying a layer-wise relevance propagation algorithm on the semiconductor images from the deep learning model;
- determining a relevance map based on the layer-wise relevance propagation algorithm;
- determining a mean intersection over union score between the relevance map and a ground truth;
- and
- determining a part of one of the semiconductor images that contributed to the classification using the deep learning model based on the relevance map and the mean intersection over union score.
Dependencies are inferred from the claim text.