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1. (WO2019006221) GÉNÉRATION D’IMAGES À HAUTE RÉSOLUTION À PARTIR D’IMAGES À BASSE RÉSOLUTION POUR DES APPLICATIONS À SEMI-CONDUCTEURS
Note: Texte fondé sur des processus automatiques de reconnaissance optique de caractères. Seule la version PDF a une valeur juridique

WHAT IS CLAIMED IS:

1. A system configured to generate a high resolution image for a specimen from a low resolution image of the specimen, comprising:

one or more computer subsystems configured for acquiring a low resolution image of a specimen; and

one or more components executed by the one or more computer subsystems, wherein the one or more components comprise:

a deep convolutional neural network, wherein the deep convolutional neural network comprises:

one or more first layers configured for generating a representation of the low resolution image; and

one or more second layers configured for generating a high

resolution image for the specimen from the representation of the low resolution image, wherein the one or more second layers comprise a final layer configured to output the high resolution image, and wherein the final layer is further configured as a sub-pixel convolution layer.

2. The system of claim 1, wherein the deep convolutional neural network is configured such that the high resolution image generated by the one or more second layers has less noise than the low resolution image.

3. The system of claim 1, wherein the deep convolutional neural network is configured such that the high resolution image generated by the one or more second layers retains structural and spatial features of the low resolution image.

4. The system of claim 1, wherein the one or more components further comprise a context aware loss module configured to train the deep convolutional neural network, wherein during training of the deep convolutional neural network, the one or more computer subsystems input the high resolution image generated by the one or more second layers and a corresponding, known high resolution image for the specimen into the context aware loss module and the context aware loss module determines context aware loss in the high resolution image generated by the one or more second layers compared to the corresponding, known high resolution image.

5. The system of claim 4, wherein the context aware loss comprises content loss, style loss, and total variation regularization.

6. The system of claim 5, wherein the content loss comprises loss in low level features of the corresponding, known high resolution image.

7. The system of claim 5, wherein the style loss comprises loss in one or more abstract entities that qualitatively define the corresponding, known high resolution image.

8. The system of claim 4, wherein the context aware loss module comprises a pre-trained VGG network.

9. The system of claim 4, wherein the one or more components further comprise a tuning module configured to determine one or more parameters of the deep convolutional neural network based on the context aware loss.

10. The system of claim 1, wherein the one or more computer subsystems are further configured to perform one or more metrology measurements for the specimen based on the high resolution image generated by the one or more second layers.

11. The system of claim 1 , wherein the deep convolutional neural network functions independently of the imaging system that generated the low resolution image.

12. The system of claim 1, wherein the low resolution image is generated by one imaging system having a first imaging platform, wherein the one or more computer subsystems are further configured for acquiring another low resolution image generated for another specimen by another imaging system having a second imaging platform that is different than the first imaging platform, wherein the one or more first layers are configured for generating a representation of the other low resolution image, and wherein the one or more second layers are further configured for generating a high resolution image for the other specimen from the representation of the other low resolution image.

13. The system of claim 12, wherein the first imaging platform is an electron beam imaging platform, and wherein the second imaging platform is an optical imaging platform.

14. The system of claim 12, wherein the first and second imaging platforms are different optical imaging platforms.

15. The system of claim 12, wherein the first and second imaging platforms are different electron beam imaging platforms.

16. The system of claim 1, wherein the low resolution image is generated by an electron beam based imaging system.

17. The system of claim 1, wherein the low resolution image is generated by an optical based imaging system.

18. The system of claim 1, wherein the low resolution image is generated by an inspection system.

19. The system of claim 1, wherein the specimen is a wafer.

20. The system of claim 1, wherein the specimen is a reticle.

21. The system of claim 1 , wherein the deep convolutional neural network outputs the high resolution image at a throughput that is higher than a throughput for generating the high resolution image with a high resolution imaging system.

22. A system configured to generate a high resolution image for a specimen from a low resolution image of the specimen, comprising:

an imaging subsystem configured for generating a low resolution image of a

specimen;

one or more computer subsystems configured for acquiring the low resolution image of the specimen; and

one or more components executed by the one or more computer subsystems, wherein the one or more components comprise:

a deep convolutional neural network, wherein the deep convolutional neural network comprises:

one or more first layers configured for generating a representation of the low resolution image; and

one or more second layers configured for generating a high

resolution image for the specimen from the representation of the low resolution image, wherein the one or more second layers comprise a final layer configured to output the high resolution image, and wherein the final layer is further configured as a sub-pixel convolution layer.

23. A non-transitory computer-readable medium, storing program instructions executable on one or more computer systems for performing a computer-implemented method for generating a high resolution image for a specimen from a low resolution image of the specimen, wherein the computer-implemented method comprises:

acquiring a low resolution image of a specimen;

generating a representation of the low resolution image by inputting the low

resolution image into one or more first layers of a deep convolutional neural network; and

generating a high resolution image for the specimen based on the representation, wherein generating the high resolution image is performed by one or more second layers of the deep convolutional neural network, wherein the one or more second layers comprise a final layer configured to output the high resolution image, wherein the final layer is further configured as a sub- pixel convolution layer, wherein said acquiring, said generating the representation, and said generating the high resolution image are performed by the one or more computer systems, wherein one or more components are executed by the one or more computer systems, and

wherein the one or more components comprise the deep convolutional neural network.

24. A computer- implemented method for generating a high resolution image for a specimen from a low resolution image of the specimen, comprising:

acquiring a low resolution image of a specimen;

generating a representation of the low resolution image by inputting the low

resolution image into one or more first layers of a deep convolutional neural network; and

generating a high resolution image for the specimen based on the representation, wherein generating the high resolution image is performed by one or more second layers of the deep convolutional neural network, wherein the one or more second layers comprise a final layer configured to output the high resolution image, wherein the final layer is further configured as a sub- pixel convolution layer, wherein said acquiring, said generating the representation, and said generating the high resolution image are performed by one or more computer systems, wherein one or more components are executed by the one or more computer systems, and wherein the one or more components comprise the deep convolutional neural network.