Nondestructive characterization for crystalline wafers
A method of analyzing semiconductor wafers includes capturing a first image of a first crystalline material, etching a first surface of the first crystalline material to delineate etch defects in the first crystalline material, and capturing a second image of first crystalline material after etching the first surface of the first crystalline material. Based on the second image, labels of etch defects delineated in the first surface of the first crystalline material are generated. The first image and the labels of etch defects are spatially coordinated to form a defect map identifying one or more defects in the first image based on the delineated etch defects, and based on the defect map and nondestructive data obtained from a second crystalline material, defects in the second crystalline material are identified.
1. A method of analyzing crystalline material comprising:
capturing a first image of a first crystalline material;
etching a first surface of the first crystalline material to delineate etch defects in the first crystalline material;
capturing a second image of first crystalline material after etching the first surface of the first crystalline material;
based on the second image, generating labels of etch defects delineated in the first surface of the first crystalline material;
spatially correlating the first image and the labels of etch defects to form a defect map identifying one or more defects in the first image based on the delineated etch defects; and
based on the defect map and nondestructive data obtained from a second crystalline material, identifying defects in the second crystalline material.
2. The method of claim 1 , wherein the first crystalline material comprises a first silicon carbide (SiC) wafer and the second crystalline material comprises a second SiC wafer.
3. The method of claim 1 , further comprising classifying the identified defects into one or more defect categories, wherein the one or more defect categories include one or more of dislocations, hexagonal voids, and stacking faults.
4. The method of claim 1 , further comprising generating a model for identifying etch defects in other crystalline material from nondestructive data captured from the other crystalline material, wherein identifying defects in the second crystalline material is performed using the model.
5. The method of claim 4 , wherein generating the model comprises training a deep neural network using the defect map to detect the one or more defects in the nondestructive data.
6. The method of claim 1 , wherein the first image is captured from a first side of the first crystalline material, the method further comprising:
etching a second side of the first crystalline material;
capturing a third image of the first crystalline material from the second side of the first crystalline material after etching the second side of the first crystalline material; and
spatially correlating the first image, the third image and the labels of etch defects to form the defect map.
7. The method of claim 1 , wherein the one or more defects in the first image comprise threading screw dislocations and/or threading edge dislocations that are correlated with the delineated etch defects.
8. The method of claim 1 , wherein the first image and the second image are captured using photoluminescence microscopy.
9. A method of analyzing a crystalline material comprising:
detecting one or more defects in the crystalline material based on nondestructive data captured from the crystalline material; and
identifying the one or more defects based on destructive data captured from other crystalline material, wherein the destructive data are obtained using destructive characterization steps that are destructive to the other crystalline material.
10. The method of claim 9 , wherein the nondestructive data comprises one or more data signals that are spatially correlated to one or more positions of the crystalline material.
11. The method of claim 9 , further comprising capturing the nondestructive data.
12. The method of claim 11 , wherein said capturing the nondestructive data comprises photoluminescence microscopy.
13. The method of claim 9 , wherein detecting the one or more defects and identifying the one or more defects comprises supplying the nondestructive data to a deep neural network.
14. The method of claim 13 , wherein the deep neural network is trained by defect maps of the destructive data captured from the other crystalline material.
15. The method of claim 14 , wherein the destructive data is captured from etched surfaces of the other crystalline material.
16. The method of claim 9 , further comprising classifying the one or more defects into one or more defect categories based on the destructive data, wherein the one or more defect categories include one or more of dislocations, hexagonal voids, and stacking faults.
17. The method of claim 16 , wherein the dislocations include one or more of threading dislocations, threading edge dislocations, basal plane dislocations, threading screw dislocations, screw dislocations, super screw dislocations, and mixed dislocations.
18. The method of claim 9 , wherein the crystalline material comprises a silicon carbide (SiC) wafer.
19. The method of claim 9 , further comprising detecting an absence of one or more defects from the nondestructive data.
20. The method of claim 9 , wherein the crystalline material comprises one or more epitaxial layers.
21. A method of analyzing a crystalline material comprising:
capturing first nondestructive data of at least one portion of at least one crystalline material;
etching a first surface of the at least one crystalline material to delineate etch defects;
capturing destructive data of the at least one portion of the at least one crystalline material after etching the first surface of the at least one crystalline material;
comparing the first nondestructive data with the destructive data to generate at least one defect map;
capturing second nondestructive data of at least a portion of at least one other crystalline material; and
using the at least one defect map and the second nondestructive data of the at least one portion of the at least one other crystalline material to identify defects in the at least one portion of the at least one other crystalline material.
22. The method of claim 21 , wherein using the at least one defect map and the second nondestructive data of the at least one portion of the at least one other crystalline material to identify defects in the at least one portion of the at least one other crystalline material comprises:
based on the destructive data, generating labels of etch defects delineated in the first surface of the at least one crystalline material;
spatially correlating the first nondestructive data and the labels of etch defects to form the defect map, wherein the defect map identifies one or more defects in the first nondestructive data based on the delineated etch defects; and
based on the defect map and the second nondestructive data obtained from the at least one portion of the at least one other crystalline material, identifying defects in the at least one portion of the at least one other crystalline material.
23. The method of claim 22 , wherein the second nondestructive data is captured from a first side of the at least one other crystalline material, the method further comprising:
etching a second side of the at least one crystalline material;
capturing second destructive data of the at least one crystalline material from the second side of the at least one crystalline material after etching the second side of the at least one crystalline material; and
spatially correlating the first nondestructive data, the second destructive data and the labels of etch defects to form the defect map.
24. The method of claim 21 , wherein the at least one crystalline material comprises a first silicon carbide (SiC) wafer and the at least one other crystalline material comprises a second SiC wafer.
25. The method of claim 21 , further comprising classifying the identified defects into one or more defect categories, wherein the one or more defect categories include one or more of dislocations, hexagonal voids, and stacking faults.
26. The method of claim 21 , further comprising generating a model for identifying etch defects in other crystalline material from nondestructive data captured from the other crystalline material, wherein identifying defects in the at least one other crystalline material is performed using the model.
27. The method of claim 26 , wherein generating the model comprises training a deep neural network using the defect map to detect the one or more defects in the second nondestructive data.
28. The method of claim 21 , wherein the one or more defects in the second nondestructive data comprise threading screw dislocations and/or threading edge dislocations that are correlated with the delineated etch defects.
29. The method of claim 21 , wherein the first nondestructive data and the destructive data are captured using photoluminescence microscopy.