Machine learning for selecting initial source shapes for source mask optimization
Initial source shapes for source mask optimization are determined based on a layout of the lithographic mask. In one approach, a layout of a lithographic mask is received. Different sections of the lithographic mask, referred to as clips, are selected. These clips are applied to a machine learning model which infers source shapes from the clips. The inferred source shapes are used as the initial source shapes for source mask optimization.
1. A method comprising:
receiving a layout of a lithographic mask;
applying, by a processor, a machine learning model to infer source shapes from the layout of the lithographic mask; and
providing the inferred source shapes as initial source shapes that are used as a starting point for a source mask optimization process.
2. The method of claim 1 , wherein the machine learning model infers source seeds from the layout of the lithographic mask; and using the inferred source shapes as initial source shapes comprises generating the initial source shapes from the inferred source seeds.
3. The method of claim 1 , further comprising:
selecting clips from the layout of the lithographic mask based on a sensitivity metric to process variations in the clips, wherein the machine learning model infers source shapes from the selected clips.
4. The method of claim 3 , wherein the sensitivity metric is a sensitivity metric of aerial images produced by the clips to process variations in the clips.
5. The method of claim 1 , further comprising:
selecting clips from the layout of the lithographic mask based on which clips are close to design rule limits for the layout of the lithographic mask, wherein the machine learning model infers source shapes from the selected clips.
6. The method of claim 1 , wherein the layout of the lithographic mask includes features for testing compliance with design rules, and the method further comprises:
selecting clips from the layout of the lithographic mask that include the features, wherein the machine learning model infers source shapes from the selected clips.
7. The method of claim 1 , wherein an output of the machine learning model comprises images of Fourier Bessel coefficients for the inferred source shapes.
8. The method of claim 1 , wherein the machine learning model comprises a U-net.
9. The method of claim 1 , wherein the method is performed iteratively for different design iterations of the layout of the lithographic mask.
10. A non-transitory computer readable medium comprising stored instructions, which when executed by a processor, cause the processor to:
receive a layout of a lithographic mask;
select clips from the layout of the lithographic mask;
select source shapes based on the selected clips; and
provide the selected source shapes as initial source shapes that are used as a starting point for a source mask optimization process.
11. The non-transitory computer readable medium of claim 10 , wherein selecting the clips is based on sensitivity to process variations in the clips.
12. The non-transitory computer readable medium of claim 11 , wherein selecting clips is based on sensitivity of aerial images produced by the clips to process variations in the clips.
13. The non-transitory computer readable medium of claim 11 , wherein selecting clips is based on sensitivity of resist profiles produced by the clips to process variations in the clips.
14. The non-transitory computer readable medium of claim 10 , wherein selecting clips is based on which clips are close to design rule limits for the layout of the lithographic mask.
15. The non-transitory computer readable medium of claim 10 , wherein the layout of the lithographic mask includes features for testing compliance with design rules, and the selected clips said features.
16. A system comprising:
a memory storing instructions; and
a processor, coupled with the memory and to execute the instructions, the instructions when executed cause the processor to:
access a training set comprising (a) inputs comprising clips taken from layouts of lithographic masks; and (b) outputs comprising corresponding source shapes; and
train a machine learning model using the training set, wherein the machine learning model infers initial source shapes from layouts of lithographic masks, the initial source shapes used as starting points for a source mask optimization process.
17. The system of claim 16 , wherein the input clips in the training set comprise instances of parameterized clips for different parameter values.
18. The system of claim 16 , wherein the input clips in the training set comprise clips based on design rules for the layout of the lithographic mask.
19. The system of claim 16 , wherein the input clips in the training set comprise clips representing contacts, lines and spaces of different geometries.
20. The system of claim 16 , wherein the input clips in the training set comprise input clips and corresponding source shapes from previously designed lithographic masks and source shapes.