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1. US20200175664 - Defect Classification by Fitting Optical Signals to a Point-Spread Function

Note: Text based on automatic Optical Character Recognition processes. Please use the PDF version for legal matters

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Claims

1. A non-transitory computer-readable storage medium storing one or more programs for execution by one or more processors of a semiconductor-inspection system that includes an optical microscope, the one or more programs comprising instructions for:
deriving a difference image between a test image of a semiconductor die and a reference image, wherein the test image is generated by inspecting the semiconductor die using the optical microscope;
for each defect of a plurality of defects for the semiconductor die, fitting a point-spread function to the defect as indicated in the difference image and determining one or more dimensions of the fitted point-spread function; and
based at least in part on the one or more dimensions of the fitted point-spread function for respective defects of the plurality of defects, distinguishing potential defects of interest in the plurality of defects from nuisance defects.
2. The computer-readable storage medium of claim 1, wherein:
the point-spread function is a two-dimensional Gaussian function; and
the one or more dimensions comprise a first dimension indicative of a width of the fitted Gaussian function in a first direction and a second dimension indicative of a width of the fitted Gaussian function in a second direction.
3. The computer-readable storage medium of claim 2, wherein:
the first dimension is selected from the group consisting of a standard deviation of the fitted Gaussian function in the first direction and a full width at half maximum of the fitted Gaussian function in the first direction; and
the second dimension is selected from the group consisting of a standard deviation of the fitted Gaussian function in the second direction and a full width at half maximum of the fitted Gaussian function in the second direction.
4. The computer-readable storage medium of claim 1, wherein the one or more dimensions comprise:
a first distance between maximal gradients of the fitted point-spread function in a first direction; and
a second distance between maximal gradients of the fitted point-spread function in a second direction.
5. The computer-readable storage medium of claim 1, wherein the one or more dimensions comprise:
a first area under a cross-section of the fitted point-spread function in a first direction through a maximum of the fitted point-spread function, normalized by the height of the maximum; and
a second area under a cross-section of the fitted point-spread function in a second direction through the maximum, normalized by the height of the maximum.
6. The computer-readable storage medium of claim 1, wherein the point-spread function is a sinc function.
7. The computer-readable storage medium of claim 1, wherein the point-spread function is a polynomial function.
8. The computer-readable storage medium of claim 1, wherein the instructions for fitting the point-spread function to the defect comprise instructions for fitting a numeric simulation of the point-spread function to the defect.
9. The computer-readable storage medium of claim 1, wherein the one or more programs further comprise instructions for identifying the plurality of defects in the difference image before fitting the point-spread function to the defect for each of the plurality of defects.
10. The computer-readable storage medium of claim 1, wherein:
the one or more programs do not include instructions for identifying the plurality of defects before fitting the point-spread function to the defect for each of the plurality of defects; and
the instructions for fitting the point-spread function to the defect for each of the plurality of defects comprise, for each respective pixel of a plurality of pixels in the difference image, instructions for fitting the point-spread function to a respective location in the difference image, wherein the respective location corresponds to the respective pixel and comprises the respective pixel and other pixels of the plurality of pixels that surround the respective pixel.
11. The computer-readable storage medium of claim 10, wherein the instructions for distinguishing the potential defects of interest from nuisance defects comprise instructions for:
removing, from a set of candidate pixels in the difference image, pixels that do not satisfy a criterion that is based on at least one of the one or more dimensions of the fitted point-spread function; and
after performing the removing, identifying the potential defects of interest from the set of candidate pixels.
12. The computer-readable storage medium of claim 10, wherein the difference image indicates degrees of difference between the test image and the reference image on a pixel-by-pixel basis, and the instructions for distinguishing the potential defects of interest from nuisance defects comprise instructions for:
adjusting the degrees of difference for respective pixels of the difference image using a criterion that is based on at least one of the one or more dimensions of the fitted point-spread function for the respective pixels; and
after performing the adjusting, identifying the potential defects of interest from the set of candidate pixels.
13. The computer-readable storage medium of claim 1, wherein the instructions for distinguishing the potential defects of interest from nuisance defects comprise instructions for:
determining, for a first defect of the plurality of defects, that a first dimension of the one or more dimensions of the fitted point-spread function exceeds, or equals or exceeds, a first threshold; and
in response to determining that the first dimension exceeds, or equals or exceeds, the first threshold, classifying the first defect as a nuisance defect.
14. The computer-readable storage medium of claim 13, wherein the instructions for distinguishing the potential defects of interest from nuisance defects further comprise instructions for:
determining, for a second defect of the plurality of defects, that the first dimension is less than, or less than or equal to, a second threshold, wherein the second threshold is less than the first threshold; and
in response to determining that the first dimension is less than, or less than or equal to, the second threshold, classifying the second defect as a nuisance defect.
15. The computer-readable storage medium of claim 1, wherein the one or more dimensions of the fitted point-spread function comprise two dimensions, and the instructions for distinguishing the potential defects of interest from nuisance defects comprise instructions for:
determining, for a first defect of the plurality of defects, whether a metric satisfies a threshold, wherein the metric is a function of the two dimensions; and
in response to a determination that the metric does not satisfy the threshold, classifying the first defect as a nuisance defect.
16. The computer-readable storage medium of claim 1, wherein the instructions for distinguishing the potential defects of interest from nuisance defects comprise instructions for providing the one or more dimensions of the fitted point-spread function for the respective defects of the plurality of defects as input to a machine-learning algorithm trained to distinguish potential defects of interest from nuisance defects.
17. The computer-readable storage medium of claim 1, wherein the one or more programs further comprise instructions for determining a goodness of fit of the fitted point-spread function for the respective defects of the plurality of defects,
wherein distinguishing the potential defects of interest from nuisance defects is further based at least in part on the goodness of fit for the respective defects.
18. The computer-readable storage medium of claim 1, wherein the respective defects of the plurality of defects are not resolved in the difference image.
19. The computer-readable storage medium of claim 1, wherein the one or more programs further comprise instructions for generating a report specifying the potential defects of interest.
20. A semiconductor-inspection system, comprising:
an optical microscope;
one or more processors; and
memory storing one or more programs for execution by the one or more processors, the one or more programs including instructions for:
deriving a difference image between a test image of a semiconductor die and a reference image, wherein the test image is generated by inspecting the semiconductor die using the optical microscope;
for each defect of a plurality of defects for the semiconductor die, fitting a point-spread function to the defect as indicated in the difference image and determining one or more dimensions of the fitted point-spread function; and
based at least in part on the one or more dimensions of the fitted point-spread function for respective defects of the plurality of defects, distinguishing potential defects of interest in the plurality of defects from nuisance defects.
21. A method of identifying semiconductor defects of interest, comprising:
inspecting a semiconductor die using an optical microscope to generate a test image of the semiconductor die; and
in a computer system comprising one or more processors and memory storing instructions for execution by the one or more processors:
deriving a difference image between the test image of the semiconductor die and a reference image;
for each defect of a plurality of defects for the semiconductor die, fitting a point-spread function to the defect as indicated in the difference image and determining one or more dimensions of the fitted point-spread function; and
based at least in part on the one or more dimensions of the fitted point-spread function for respective defects of the plurality of defects, distinguishing potential defects of interest in the plurality of defects from nuisance defects.
22. A non-transitory computer-readable storage medium storing one or more programs for execution by one or more processors of a semiconductor-inspection system that includes an optical microscope, the one or more programs comprising instructions for:
deriving a difference image between a test image of the semiconductor die and a reference image, wherein the test image is generated by inspecting the semiconductor die using the optical microscope;
fitting a summation of a plurality of point-spread functions to the difference image, wherein each point-spread function of the plurality of point-spread functions is centered on a distinct predefined location in the semiconductor die and has a fixed width associated with the optical microscope, the fitting comprising determining coefficients of respective point-spread functions of the plurality of point-spread functions; and
based at least in part on the coefficients of the respective point-spread functions, distinguishing potential defects of interest in a plurality of defects for the semiconductor die from nuisance defects.