Clustering sub-care areas based on noise characteristics
A care area is determined in an image of a semiconductor wafer. The care area is divided into sub-care areas based on the shapes of polygons in a design file associated with the care area. A noise scan of a histogram for the sub-care areas is then performed. The sub-care areas are clustered into groups based on the noise scan of the histogram.
1. A method comprising:
generating an image of a semiconductor wafer using a wafer inspection tool;
receiving, at a processor, the image of the semiconductor wafer;
determining, using a processor, a care area in the image;
dividing the care area into sub-care areas using the processor based on shapes of polygons in a design file associated with the care area;
performing a noise scan of a histogram for the sub-care areas using the processor, wherein the noise scan of the histogram is based on a difference image between the image and a reference image at the sub-care area; and
clustering the sub-care areas into groups using the processor based on the noise scan of the histogram.
2. The method of claim 1 , further comprising determining, using the processor, if a defect is present at a pixel of one of the sub-care areas.
3. The method of claim 1 , wherein the clustering further includes:
determining a bandwidth of the histogram between density points at each end of the histogram using the processor; and
determining a median, a maximum, and a minimum of the bandwidth using the processor.
4. The method of claim 3 , wherein the clustering further includes:
generating a dendrogram based on the groups; and
adjusting a cutline on the dendrogram until all groups satisfy a data sufficiency constraint.
5. The method of claim 1 , wherein the clustering further includes:
determining a mean, a first threshold, and a second threshold of the noise scan, wherein the first threshold and the second threshold are less than 4-sigma from the mean;
clustering the sub-care areas for each die into a first category between the mean and first threshold and a second category between the first threshold and second threshold; and
determining consistency of populations based on the clustering using the processor.
6. The method of claim 5 , further comprising merging dies of the sub-care areas using the processor.
7. The method of claim 5 , wherein the first threshold and the second threshold are each one of 1-sigma, 3-sigma, and 4-sigma.
8. The method of claim 7 , wherein the clustering further includes:
clustering the groups with a statistical point-based 1-sigma to generate a first output;
clustering the first output with a statistical point-based 3-sigma to generate a second output; and
clustering the second output with a statistical point-based 4-sigma.
9. The method of claim 1 , wherein the determining the group of sub-care areas is across a row of dies on the semiconductor wafer.
10. The method of claim 1 , further comprising combining two of the sub-care areas, wherein detectability of the two of the sub-care areas is approximately unchanged after the combining.
11. A non-transitory computer readable medium storing a program configured to instruct a processor to execute the method of claim 1 .
12. A system comprising:
a light source;
a stage configured to hold a semiconductor wafer;
a detector configured to receive light from the light source reflected off the semiconductor wafer; and
a processor in electronic communication with the detector, wherein the processor is configured to:
receive an image of the semiconductor wafer using data from the detector;
determine a care area in the image;
divide the care area into sub-care areas based on shapes of polygons in a design file associated with the care area;
perform a noise scan of a histogram for the sub-care areas, wherein the noise scan of the histogram is based on a difference image between the image and a reference image at the sub-care area; and
cluster the sub-care areas into groups based on the noise scan of the histogram.
13. The system of claim 12 , wherein the processor is further configured to determine if a defect is present at a pixel of one of the sub-care areas.
14. The system of claim 12 , wherein the clustering further includes:
determining a bandwidth of the histogram between density points at each end of the histogram using the processor; and
determining a median, a maximum, and a minimum of the bandwidth using the processor.
15. The system of claim 12 , wherein the clustering further includes:
determining a mean, a first threshold, and a second threshold of the noise scan, wherein the first threshold and the second threshold are less than 4-sigma from the mean;
clustering the sub-care areas into a first category between the mean and first threshold and a second category between the first threshold and second threshold; and
determining consistency of populations based on the clustering using the processor.
16. The system of claim 15 , wherein the first threshold and the second threshold are each one of 1-sigma, 3-sigma, and 4-sigma.
17. The system of claim 16 , wherein the clustering further includes:
clustering the groups with a statistical point-based 1-sigma to generate a first output;
clustering the first output with a statistical point-based 3-sigma to generate a second output; and
clustering the second output with a statistical point-based 4-sigma.
18. The system of claim 12 , wherein the determining the group of sub-care areas is across a row of dies on the semiconductor wafer.
19. The system of claim 12 , wherein the processor is further configured to combine two of the sub-care areas, wherein detectability of the two of the sub-care areas is approximately unchanged after the combining.