Smart metrology on microscope images
Smart metrology methods and apparatuses disclosed herein process images for automatic metrology of desired features. An example method at least includes extracting a region of interest from an image, the region including one or more boundaries between different sections, enhancing at least the extracted region of interest based on one or more filters, generating a multi-scale data set of the region of interest based on the enhanced region of interest, initializing a model of the region of interest; optimizing a plurality of active contours within the enhanced region of interest based on the model of the region of interest and further based on the multi-scale data set, the optimized plurality of active contours identifying the one or more boundaries within the region of interest, and performing metrology on the region of interest based on the identified boundaries.
1. A method comprising:
extracting a region of interest from an image, the region including one or more boundaries between different sections of the region of interest;
generating a multi-scale data set of the region of interest based on the region of interest;
initializing a model of the region of interest, the initialized model determining at least a first boundary and a second boundary within the region of interest;
optimizing a plurality of active contours within the region of interest based on the model of the region of interest and further based on the multi-scale data set, the optimized plurality of active contours identifying the one or more boundaries within the region of interest; and
performing metrology on the region of interest based on the identified boundaries, wherein performing metrology on the region of interest comprises automatically performing geometric analysis of the different sections separated by the one or more boundaries.
2. The method of claim 1 , further comprising enhancing at least the extracted region of interest based on one or more filters, and wherein the multi-scale data is generated at least in part on the region of interest.
3. The method of claim 2 , wherein generating a multi-scale data set of the region of interest based on the region of interest comprises generating a plurality of image resolution levels of the region of interest using one or more scale spaces, wherein the one or more scale spaces selected from a Gaussian scale space, a geometric scale space, a non-linear scale space, and an adaptive space scale space.
4. The method of claim 2 , wherein enhancing at least the extracted region of interest based on one or more filters includes:
improving a contrast of at least the region of interest within the image; and
improving a signal to noise ratio of at least the region of interest within the image.
5. The method of claim 4 , wherein improving the contrast and the signal to noise ratio comprises automatically selecting and applying the one or more filters, wherein the one or more filters are selected from histogram manipulation, linear and non-linear contrast enhancement, data normalization based on local, low-frequency data distribution, gamma correction, log-correction, and brightness correction.
6. The method of claim 1 , wherein initializing a model of the region of interest, the initialized model determining at least first and second bounds of the region of interest comprises initializing the model of the region of interest based one or more image maps selected from binary labelled maps, interactive maps, distance maps, CAD maps, statistical models from the data, Dye cast, random distribution of geometric shapes, and geometric models.
7. The method of claim 1 , further comprising:
improving sharpness of the region of interest;
differentiating and detecting the one or more boundaries in the region of interest; and
wherein the model of the region of interest is initialized based in part on the differentiated and detected one or more initializing a model of the region of interest.
8. A method comprising,
extracting a region of interest from an image, the region including one or more boundaries between different sections of the region of interest;
generating a multi-scale data set of the region of interest based on the region of interest;
initializing a model of the region of interest, the initialized model determining at least a first boundary and a second boundary within the region of interest;
optimizing a plurality of active contours within the region of interest based on the model of the region of interest and further based on the multi-scale data set, the optimized plurality of active contours identifying the one or more boundaries within the region of interest, wherein optimizing a plurality of active contours within the region of interest based on the model of the region of interest and further based on the multi-scale data set comprises:
initializing a first plurality of active contours within the first and second bounds of the initialized model; and
allowing the first plurality of active contours to optimize to the plurality of active contours to identify the one or more boundaries within the region of interest; and
performing metrology on the region of interest based on the identified boundaries.
9. The method of claim 8 , wherein there are a greater number of active contours in the first plurality of active contours than there are boundaries within the region of interest.
10. A non-transitory, computer readable medium storing instructions that, when executed by one or more processors, triggers the one or more processors to perform the stems of:
extracting a region of interest from an image, the region including one or more boundaries between different sections of the region of interest;
generating a multi-scale data set of the region of interest based on the region of interest;
initializing a model of the region of interest, the initialized model determining at least first and second bounds of the region of interest;
optimizing a plurality of active contours within the region of interest based on the model of the region of interest and further based on the multi-scale data set, the optimized plurality of active contours identifying the one or more boundaries within the region of interest; and
performing metrology on the region of interest based on the identified boundaries, wherein performing metrology on the region of interest comprises automatically performing geometric analysis of the different sections separated by the one or more boundaries.
11. The non-transitory, computer readable medium of claim 10 , wherein optimizing a plurality of active contours within the region of interest based on the model of the region of interest and further based on the multi-scale data set comprises:
initializing a first plurality of active contours within the first and second bounds of the initialized model; and
allowing the first plurality of active contours to optimize to the plurality of active contours to identify the one or more boundaries within the region of interest.
12. The non-transitory, computer readable medium of claim 11 , wherein there are a greater number of active contours in the first plurality of active contours than there are boundaries within the region of interest.
13. The non-transitory, computer readable medium of claim 10 , wherein the instructions further trigger the processor to cause the performance of enhancing at least the extracted region of interest based on one or more filters, and wherein the multi-scale data is generated at least in part on the enhanced region of interest.
14. The non-transitory, computer readable medium of claim 13 , wherein generating a multi-scale data set of the region of interest based on the enhanced region of interest comprises generating a plurality of image resolution levels of the enhanced region of interest using one or more scale spaces, wherein the one or more scale spaces selected from a Gaussian scale space, a geometric scale space, a non-linear scale space, and an adaptive space scale space.
15. The non-transitory, computer readable medium of claim 10 , wherein enhancing at least the extracted region of interest based on one or more filters includes:
improving a contrast of at least the region of interest within the image; and
improving a signal to noise ratio of at least the region of interest within the image.
16. The non-transitory, computer readable medium of claim 15 , wherein improving the contrast and the signal to noise ratio comprises automatically selecting and applying the one or more filters, wherein the one or more filters are selected from histogram manipulation, linear and non-linear contrast enhancement, data normalization based on local, low-frequency data distribution, gamma correction, log-correction, and brightness correction.
17. The non-transitory, computer readable medium of claim 10 , wherein initializing a model of the region of interest, the initialized model determining at least first and second bounds of the region of interest comprises initializing the model of the region of interest based one or more image maps selected from binary labelled maps, interactive maps, distance maps, CAD maps, statistical models from the data, Dye cast, random distribution of geometric shapes, and geometric models.
18. The non-transitory, computer readable medium of claim 10 , wherein the instructions further trigger the processor to cause the performance of:
improving sharpness of the region of interest;
differentiating and detecting the one or more boundaries in the region of interest; and
wherein the model of the region of interest is initialized based in part on the differentiated and detected one or more initializing a model of the region of interest.