Multi-scale autoencoders for semiconductor workpiece understanding
An example method includes obtaining a workpiece image of a semiconductor workpiece. The example method includes providing the workpiece image as input to a machine-learned encoding model. The example method includes obtaining an output from the machine-learned encoding model, the output includes an encoding corresponding to the semiconductor workpiece. The example method includes determining one or more characteristics of the semiconductor workpiece based at least in part on the encoding or modifying a semiconductor manufacturing process based at least in part on the encoding.
1. A method for inspecting semiconductor workpieces, the method comprising:
obtaining a workpiece image of a semiconductor workpiece;
providing the workpiece image as input to a machine-learned encoding model;
obtaining an output from the machine-learned encoding model, the output comprising an encoding corresponding to the semiconductor workpiece; and
determining one or more characteristics of the semiconductor workpiece based at least in part on the encoding, the one or more characteristics associated with a crystalline material feature; and
modifying a semiconductor manufacturing process based at least in part on the encoding.
2. The method of claim 1 , further comprising:
downsampling a workpiece image portion of the workpiece image from a first resolution to a second resolution to produce a downsampled image;
upsampling the downsampled image to the first resolution to produce a first smoothed image at the first resolution; and
subtracting the first smoothed image from the workpiece image portion to produce a first residual image.
3. The method of claim 2 , further comprising providing the first residual image to the machine-learned encoding model.
4. The method of claim 2 , wherein:
the workpiece image portion comprises a crop of the workpiece image according to one or more crop coordinates; and
the method further comprises providing the crop coordinates of the crop of the workpiece image to the machine-learned encoding model.
5. The method of claim 2 , wherein the first smoothed image comprises lower-resolution features and the first residual image comprises higher-resolution features.
6. The method of claim 2 , further comprising:
downsampling a residual portion of the first residual image to produce a downsampled residual portion;
upsampling the downsampled residual portion to produce a second smoothed image; and
subtracting the second smoothed image from the residual portion of the first residual image to produce a second residual image.
7. The method of claim 6 , further comprising providing the second residual image to the machine-learned encoding model.
8. The method of claim 6 , wherein the second residual image corresponds to a smaller portion of the semiconductor workpiece than the first residual image.
9. The method of claim 1 , further comprising:
downsampling the workpiece image to produce a downsampled workpiece image having a lower resolution than the workpiece image; and
providing the downsampled workpiece image to the machine-learned encoding model.
10. The method of claim 1 , wherein the machine-learned encoding model comprises an encoder portion of an autoencoder.
11. The method of claim 1 , further comprising providing workpiece characterization data of the semiconductor workpiece as input to the machine-learned encoding model.
12. The method of claim 1 , wherein the one or more characteristics of the semiconductor workpiece comprises a quality characteristic of the semiconductor workpiece.
13. The method of claim 1 , wherein the one or more characteristics of the semiconductor workpiece comprises an anomaly characteristic.
14. The method of claim 13 , wherein determining the one or more characteristics of the semiconductor workpiece based at least in part on the encoding comprises detecting the anomaly characteristic based at least in part on the encoding corresponding to the semiconductor workpiece.
15. The method of claim 1 , wherein the one or more characteristics of the semiconductor workpiece comprises a similarity characteristic of the semiconductor workpiece to one or more additional semiconductor workpieces.
16. The method of claim 15 , wherein determining the one or more characteristics of the semiconductor workpiece based at least in part on the encoding comprises:
identifying one or more closest encodings corresponding to the one or more additional semiconductor workpieces to the encoding corresponding to the semiconductor workpiece; and
determining the similarity characteristic based at least in part on the one or more closest encodings.
17. The method of claim 1 , wherein the semiconductor workpiece comprises a silicon carbide crystalline material.
18. A method for inspecting semiconductor workpieces, the method comprising:
obtaining a workpiece image of a semiconductor workpiece;
providing the workpiece image as input to a machine-learned encoding model;
obtaining an output from the machine-learned encoding model, the output comprising an encoding corresponding to the semiconductor workpiece;
identifying one or more features on the semiconductor workpiece based at least in part on the encoding, the one or more features comprising one or more crystalline material features; and
modifying a semiconductor manufacturing process based at least in part on the encoding.
19. The method of claim 18 , further comprising:
downsampling a workpiece image portion of the workpiece image from a first resolution to a second resolution to produce a downsampled image;
upsampling the downsampled image to the first resolution to produce a first smoothed image at the first resolution; and
subtracting the first smoothed image from the workpiece image portion to produce a first residual image.
20. The method of claim 18 , wherein the machine-learned encoding model comprises an encoder portion of an autoencoder.
21. The method of claim 18 , wherein the semiconductor workpiece comprises a silicon carbide crystalline material.