System and method for generating and analyzing roughness measurements
Systems and methods are disclosed that remove noise from roughness measurements to determine roughness of a feature in a pattern structure. In one embodiment, a method for determining roughness of a feature in a pattern structure includes generating, using an imaging device, a set of one or more images, each including measured linescan information that includes noise. The method also includes detecting edges of the features within the pattern structure of each image without filtering the images, generating a biased power spectral density (PSD) dataset representing feature geometry information corresponding to the edge detection measurements, evaluating a high-frequency portion of the biased PSD dataset to determine a noise model for predicting noise over all frequencies of the biased PSD dataset, and subtracting the noise predicted by the determined noise model from a biased roughness measure to obtain an unbiased roughness measure.
1. A method for detecting edge positions in a pattern structure, the method comprising:
receiving an image comprising instances of features within the pattern structure, wherein the instances of the features within the pattern structure are described by linescan information; and
detecting the edge positions of the features within the pattern structure of the image without filtering the image, wherein the detecting is performed by:
applying a model to a single linescan of the linescan information,
adjusting, based on the single linescan, one or more parameters of the model,
obtaining, using the one or more adjusted parameters, a best fit of the model to the single linescan, and
detecting, using the best fit of the model to the single linescan, the edge positions of the features within the pattern structure of the image.
2. The method of claim 1 , wherein the model comprises an inverse linescan model (ILM).
3. The method of claim 1 , further comprising:
prior to detecting the edge positions of the features within the pattern structure of the image:
calibrating the model to an average linescan obtained by averaging, using the linescan information, over an axis of symmetry of the features,
wherein the calibrating comprises the adjusting, based on the average linescan, the one or more parameters of the model to obtain a second best fit of the model to the average linescan.
4. The method of claim 1 , further comprising:
prior to detecting the edge positions of the features within the pattern structure of the image:
calibrating the one or more parameters depending on materials of a sample, properties of an imaging device, and geometry of the features.
5. The method of claim 1 , further comprising:
prior to detecting the edge positions of the features within the pattern structure of the image:
calibrating the one or more parameters depending on the geometry of the features.
6. The method of claim 1 , further comprising:
detecting the edge positions of the features within the pattern structure of the image without postprocessing the image.
7. The method of claim 1 , further comprising:
generating, using an imaging device, the image, wherein the image includes the linescan information corresponding to the pattern structure that includes noise.
8. The method of claim 1 , further comprising detecting, based on the edge positions, displacement of edges of the features within the pattern structure.
9. A non-transitory, computer-readable medium storing instructions that, when executed, cause a processor to:
receive an image comprising instances of features within the pattern structure, wherein the instances of the features within the pattern structure are described by linescan information; and
detect the edge positions of the features within the pattern structure of the image without filtering the image, wherein the detecting is performed by:
apply a model to a single linescan of the linescan information,
adjust, based on the single linescan, one or more parameters of the model,
obtain, using the one or more adjusted parameters, a best fit of the model to the single linescan, and
detect, using the best fit of the model to the single linescan, the edge positions of the features within the pattern structure of the image.
10. The computer-readable medium of claim 9 , wherein the model comprises an inverse linescan model (ILM).
11. The computer-readable medium of claim 9 , wherein the processor is further caused to:
prior to detecting the edge positions of the features within the pattern structure of the image:
calibrate the model to an average linescan obtained by averaging, using the linescan information, over an axis of symmetry of the features,
wherein the calibrating comprises adjusting, based on the average linescan, the one or more parameters of the model to obtain a second best fit of the model to the average linescan.
12. The computer-readable medium of claim 9 , wherein the processor is further caused to:
prior to detecting the edge positions of the features within the pattern structure of the image:
calibrate the one or more parameters depending on materials of a sample, properties of an imaging device, and geometry of the features.
13. The computer-readable medium of claim 9 , wherein the processor is further caused to:
prior to detecting the edge positions of the features within the pattern structure of the image:
calibrate the one or more parameters depending on the geometry of the features.
14. The computer-readable medium of claim 9 , wherein the processor is further caused to:
detect the edge positions of the features within the pattern structure of the image without postprocessing the image.
15. The computer-readable medium of claim 9 , wherein the processor is further caused to:
generate, using an imaging device, the image, wherein the image includes the linescan information corresponding to the pattern structure that includes noise.
16. A system comprising:
a memory storing instructions; and
a processor communicatively coupled to the memory, the processor executes the instructions to:
receive an image comprising instances of features within the pattern structure, wherein the instances of the features within the pattern structure are described by linescan information; and
detect the edge positions of the features within the pattern structure of the image without filtering the image, wherein the detecting is performed by:
apply a model to a single linescan of the linescan information,
adjust, based on the single linescan, one or more parameters of the model,
obtain, using the one or more adjusted parameters, a best fit of the model to the single linescan, and
detect, using the best fit of the model to the single linescan, the edge positions of the features within the pattern structure of the image.
17. The system of claim 16 , wherein the model comprises an inverse linescan model (ILM).
18. The system of claim 16 , wherein the processor is further instructed to:
prior to detecting the edge positions of the features within the pattern structure of the image:
calibrate the model to an average linescan obtained by averaging, using the linescan information, over an axis of symmetry of the features,
wherein the calibrating comprises adjusting, based on the average linescan, the one or more parameters of the model to obtain a best fit of the model to the average linescan.
19. The system of claim 16 , wherein the processor is further instructed to:
prior to detecting the edge positions of the features within the pattern structure of the image:
calibrate the one or more parameters depending on materials of a sample, properties of an imaging device, and geometry of the features.
20. The system of claim 16 , wherein the processor is further instructed to:
prior to detecting the edge positions of the features within the pattern structure of the image:
calibrate the one or more parameters depending on the geometry of the features.