Methods and systems for forming a pattern on a surface using multi-beam charged particle beam lithography

Patent metadata

Field	Value
Publication number	US-11264206-B2
Application number	US-201916655582-A
Country	US
Kind code	B2
Filing date	Oct 17, 2019
Priority date	Mar 10, 2014
Publication date	Mar 1, 2022
Grant date	Mar 1, 2022

Abstract

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.

First claim

What is claimed is:

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.

Assignees

• D2S Inc

Inventors

• Fujimura Akira
• Nguyen Thang
• Baranwal Ajay
• Meyer Michael J
• Pillai Suhas

Classifications

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N3/0455

  Auto-encoder networks; Encoder-decoder networks · CPC title

• G06T2207/20081

  Training; Learning · CPC title

• H01J37/3177 Primary

  Multi-beam, e.g. fly's eye, comb probe · CPC title