Automated inspection system
US-2024420305-A1 · Dec 19, 2024 · US
US9305343B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-9305343-B2 |
| Application number | US-201414250438-A |
| Country | US |
| Kind code | B2 |
| Filing date | Apr 11, 2014 |
| Priority date | Apr 12, 2013 |
| Publication date | Apr 5, 2016 |
| Grant date | Apr 5, 2016 |
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A plurality of images is captured, and the plurality of images is integrated and displayed after one or more ROIs included in the captured images are extracted and classified. At integration, an integration method is controlled according to a classification result of the ROI.
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What is claimed is: 1. An observation method comprising: acquiring a plurality of captured images of a same position on a sample; extracting one or more regions to be observed from among the plurality of captured images acquired in the acquiring a plurality of captured images; classifying the one or more regions to be observed extracted in the extracting one or more regions to be observed; and integrating the plurality of captured images and generating an integrated image by selecting an integration method from a plurality of predetermined integration methods based on a classification result of the classifying the one or more regions to be observed, wherein said selecting an integration method further comprises selecting one of a weighted sum processing with difference information integration method, a weighted sum processing without difference information integration method, and a weighted sum after brightness adjustment integration method to be performed, based on at least whether or not the one or more regions are present at an upper side of a pattern and a relative brightness of said pattern. 2. The observation method according to claim 1 , wherein the classifying the one or more regions to be observed classifies the one or more regions to be observed based on information related to heights of a plurality of objects existing in the regions to be observed, and the integrating the plurality of captured images determines the integration method based on the information related to the heights of the objects. 3. The observation method according to claim 1 , wherein the classifying the regions to be observed classifies which at least two of an upper layer pattern region, a lower layer region, and a pattern side wall region the regions to be observed belong to. 4. The observation method according to claim 1 , wherein the integrating the plurality of captured images integrates the regions to be observed and peripheral regions of the regions to be observed to at least one of the plurality of captured images after deforming the regions to be observed and the peripheral regions of the regions to be observed based on shape information among the plurality of captured images in the peripheral regions of the regions to be observed. 5. The observation method according to claim 1 , wherein the integrating the plurality of captured images storing a brightness/darkness relationship between the regions to be observed and peripheral regions of the regions to be observed and performing the integration. 6. The observation method according to claim 1 , further comprising: displaying the integrated image generated by the integrating the plurality of captured images. 7. An observation device comprising: an image acquisition unit configured to capture a sample and to acquire a plurality of captured images of a same position on said sample; a region to be observed extraction unit configured to extract one or more regions to be observed from the plurality of captured images acquired in the image acquisition unit; a region to be observed classification unit configured to classify the one or more regions to be observed extracted in the region to be observed extraction unit; and an image integration unit configured to integrate the plurality of captured images to generate an integrated image by selecting an integration method from a plurality of predetermined integration methods based on a classification result of the region to be observed classification unit, wherein said selecting an integration method further comprises selecting one of a weighted sum processing with difference information integration method, a weighted sum processing without difference information integration method, and a weighted sum after brightness adjustment integration method to be performed, based on at least whether or not the one or more regions are present at an upper side of a pattern and a relative brightness of said pattern. 8. The observation device according to claim 7 , wherein the region to be observed classification unit classifies the one or more regions to be observed based on information related to heights of a plurality of objects existing in the regions to be observed, and the image integration unit determines the integration method based on the information related to heights of the objects. 9. The observation device according to claim 7 , wherein the region to be observed classification unit classifies which at least two of an upper layer pattern region, a lower layer region, and a pattern side wall region the regions to be observed belong to. 10. The observation device according to claim 7 , wherein the image integration unit integrates the regions to be observed and peripheral regions of the regions to be observed to at least one of the plurality of captured images after deforming the regions to be observed and the peripheral regions of the regions to be observed based on shape information among the plurality of captured images in the peripheral regions of the regions to be observed. 11. The observation unit according to claim 1 , wherein the image integration unit stores a brightness/darkness relationship between the regions to be observed and peripheral regions of the regions to be observed, and performs integration. 12. The observation device according to claim 7 , further comprising: a display unit configured to display the integrated image generated. by the image integration unit. 13. The observation method according to claim 1 , wherein said selecting an integration method further comprises: selecting a weighted sum processing with difference information integration method to be performed, if the one or more regions are present at an upper side of a pattern and transparency is high, or if the one or more regions is not present at the upper side of a pattern; selecting a weighted sum processing without difference information integration method to be performed, if the one or more regions is present at the upper side of a pattern, transparency is not high, the one or more regions is not present in a region of a shadow of the upper layer pattern, and a relative brightness is high, or if the one or more regions is present at the upper side of a pattern, transparency is not high, and the one or more regions is not present in a region of a shadow of the upper layer pattern; and selecting a weighted sum after brightness adjustment integration method to be performed, if the one or more regions is present at the upper side of a pattern, transparency is not high, the one or more regions is present in the region of a shadow of the upper layer pattern, and the relative brightness is high. 14. The observation device according to claim 7 , wherein said selecting an integration method further comprises: selecting a weighted sum processing with difference information integration method to be performed, if the one or more regions are present at an upper side of a pattern and transparency is high, or if the one or more regions is not present at the upper side of a pattern; selecting a weighted sum processing without difference information integration method to be performed, if the one or more regions is present at the upper side of a pattern, transparency is not high, the one or more regions is not present in a region of a shadow of the upper layer pattern, and a relative brightness is high, or if the one or more regions is present at the upper side of a pattern, transparency is not high, and the one or more regions is not present in a region of a shadow of the upper layer pattern; and selecting a weighted sum after brightness adjustment integratio
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