SEM image enhancement methods and systems
Systems and methods for image enhancement are disclosed. A method for enhancing an image may include acquiring a first scanning electron microscopy (SEM) image at a first resolution. The method may also include acquiring a second SEM image at a second resolution. The method may further include providing an enhanced image by using the first SEM image as a reference to enhance the second SEM image. The enhanced image may be provided by using one or more features extracted from the first image to enhance the second SEM image, or using the first SEM image as a reference to numerically enhance the second SEM image.
1 . A method for enhancing an image, the method comprising:
scanning a wafer simultaneously using an on-axis beam and at least one off-axis beam of a multi-beam system;
acquiring a first scanning electron microscopy (SEM) image by use of the on-axis beam of the multi-beam system;
acquiring a second SEM image by use of the at least one off-axis beam of the multi-beam system, wherein the first SEM image acquired via the on-axis beam has a higher resolution than the second SEM image acquired via the at least one off-axis beam; and
providing an enhanced image, the enhanced image being provided by using the first SEM image as a reference to numerically enhance the second SEM image,
wherein numerically enhancing the second SEM image comprises numerically blurring data representing the first SEM image to match data representing the second SEM image.
2 . The method of claim 1 , wherein the enhanced image is provided by using one or more features extracted from the first SEM image to enhance the second SEM image.
3 . The method of claim 1 , further comprising:
extracting a trained feature from the first SEM image.
4 . The method of claim 1 , further comprising recognizing one or more features in the second SEM image as corresponding one or more features in the first SEM image based on a comparison between the numerically blurred data and the data representing the second SEM image.
5 . An inspection system, comprising:
a memory storing a set of instructions; and
a processor configured to execute the set of instructions to cause the inspection system to:
scan a wafer simultaneously using an on-axis beam and at least one off-axis beam of a multi-beam system;
acquire a first scanning electron microscopy (SEM) image by use of the on-axis beam of the multi-beam system;
acquire a second SEM image by use of the at least one off-axis beam of the multi-beam system, wherein the first SEM image acquired via the on-axis beam has a higher resolution than the second SEM image acquired via the at least one off-axis beam; and
provide an enhanced image, the enhanced image being provided by using the first SEM image as a reference to numerically enhance the second SEM image by numerically blurring data representing the first SEM image to match data representing the second SEM image.
6 . The system of claim 5 , wherein the enhanced image is provided by using one or more features extracted from the first SEM image to enhance the second SEM image.
7 . The system of claim 5 , wherein the processor is further configured to execute the set of instructions to cause the inspection system to:
extract a trained feature from the first SEM image.
8 . The system of claim 7 , wherein the trained feature is extracted from the first SEM image using a machine learning network.
9 . The system of claim 8 , wherein the processor is further configured to execute the set of instructions to cause the inspection system to:
acquire at least one additional SEM image by use of the on-axis beam; and
extract at least one additional trained feature from the at least one additional SEM image using the machine learning network.
10 . The system of claim 7 , wherein the processor is further configured to execute the set of instructions to cause the inspection system to:
extract pattern information of the second SEM image;
determine that the trained feature is identified on the second SEM image using the pattern information; and
in response to the determination that the trained feature is identified on the second SEM image, provide the enhanced image by enhancing the second SEM image using the identified trained feature.
11 . The system of claim 7 , wherein the processor is further configured to execute the set of instructions to cause the inspection system to acquire the first SEM image and extract the trained feature from the first SEM image in an offline mode.
12 . The system of claim 10 , wherein the determination that the trained feature is identified on the second SEM image includes a comparison of the pattern information and the trained feature.
13 . The system of claim 5 , wherein the processor is further configured to execute the set of instructions to cause the inspection system to:
analyze data representing the first SEM image to obtain one or more spatial-spectral characteristics; and
provide the enhanced image by applying one or more numerical compensations to data representing the second SEM image on the one or more spatial-spectral characteristics obtained.
14 . The system of claim 13 , wherein the one or more spatial-spectral characteristics obtained include phase and amplitude characteristics.
15 . The system of claim 13 , wherein the one or more numerical compositions are applied in a Fourier domain.
16 . The system of claim 13 , wherein the one or more numerical compensations deconvolve the second SEM image.
17 . The system of claim 5 , wherein the processor is further configured to execute the set of instructions to cause the inspection system to recognize one or more features in the second SEM image as corresponding one or more features in the first SEM image based on a comparison between the numerically blurred data and the data representing the second SEM image.