Methods and systems for forming a pattern on a surface using multi-beam charged particle beam lithography
Methods for fracturing or mask data preparation are disclosed in which a set of single-beam charged particle beam shots is input; a calculated image is calculated using a neural network, from the set of single-beam charged particle beam shots; and a set of multi-beam shots is generated based on the calculated image, to convert the set of single-beam charged particle beam shots to the set of multi-beam shots which will produce a surface image on the surface. Methods for training a neural network include inputting a set of single-beam charged particle beam shots; calculating a set of calculated images using the set of single-beam charged particle beam shots; and training the neural network with the set of calculated images.
1. A method for forming a pattern on a surface using multi-beam charged particle beam lithography, the method comprising:
inputting a set of single-beam charged particle beam shots;
calculating a calculated image using the set of single-beam charged particle beam shots, wherein the calculating comprises pixel-to-pixel conversion using a neural network, wherein the neural network comprises a U-Net;
generating a set of multi-beam shots based on the calculated image, to convert the set of single-beam charged particle beam shots to the set of multi-beam shots which will produce a surface image on the surface, wherein the surface image matches the calculated image, within a pre-determined tolerance, and wherein the generating is performed using a computing hardware processor; and
forming the pattern on the surface using the set of multi-beam shots.
2. The method of claim 1 wherein the pattern comprises one or more patterns, and wherein the calculated image comprises contour information for the one or more patterns.
3. The method of claim 1 wherein the calculated image comprises a predicted image calculated by the neural network.
4. The method of claim 3 wherein the predicted image comprises an aerial image.
5. The method of claim 1 wherein each multi-beam shot comprises a plurality of beamlets.
6. The method of claim 1 wherein shots in the set of single-beam charged particle beam shots comprise assigned dosages.
7. The method of claim 1 wherein shots in the set of single-beam charged particle beam shots have no assigned dosages.
8. The method of claim 1 wherein shots in the set of single-beam charged particle beam shots overlap.
9. The method of claim 1 wherein shots in the set of single-beam charged particle beam shots comprise variable shaped beam (VSB) shots.
10. The method of claim 1 wherein the pattern comprises one or more patterns, wherein the multi-beam charged particle beam lithography comprises exposing a plurality of pixels or beamlets, and wherein the step of generating comprises:
determining pixel or beamlet dosage information for each of the patterns in the one or more patterns; and
generating the set of multi-beam shots from the pixel or beamlet dosage information for the one or more patterns.
11. The method of claim 1 wherein the calculating further comprises calculating an identity image from the set of single-beam charged particle beam shots.
12. The method of claim 11 wherein the identity image is divided into tiles.
13. The method of claim 1 wherein the U-Net further comprises four convolution layers in a contracting network and four transposed convolution layers in an expansion network.
14. The method of claim 13 wherein a stride of 2 and a kernel size of 4×4 is used in each of the four convolution layers in the contracting network and in each of the four transposed convolution layers in the expansion network.