Method and apparatus for evaluating an unknown effect of defects of an element of a photolithography process

Patent metadata

Field	Value
Publication number	US-11774859-B2
Application number	US-202017087970-A
Country	US
Kind code	B2
Filing date	Nov 3, 2020
Priority date	May 18, 2018
Publication date	Oct 3, 2023
Grant date	Oct 3, 2023

Abstract

The present invention relates to a method and an apparatus for determining at least one unknown effect of defects of an element of a photolithography process. The method comprises the steps of: (a) providing a model of machine learning for a relationship between an image, design data associated with the image and at least one effect of the defects of the element of the photolithography process arising from the image; (b) training the model of machine learning using a multiplicity of images used for training purposes, design data associated with the images used for training purposes and corresponding effects of the defects; and (c) determining the at least one unknown effect of the defects by applying the trained model to a measured image and the design data associated with the measured image.

First claim

What is claimed is:

1. A method for determining at least one unknown effect of defects of an element of a photolithography process, wherein the method comprises the steps of:
a. providing a model of machine learning for a relationship between an image, design data associated with the image and at least one effect of the defects of the element of the photolithography process arising from the image;
b. training the model of machine learning using a multiplicity of images used for training purposes, design data associated with the images used for training purposes and corresponding effects of the defects; and
c. determining the at least one unknown effect of the defects by applying the trained model of machine learning to a measured image and the design data associated with the measured image,
wherein the image comprises an image recorded by an optical imaging system, and
wherein the image recorded by the optical imaging system comprises an aerial image and/or wherein the aerial image comprises an aerial image focus stack.

2. The method of claim 1 , wherein the image further comprises at least one element from the group: an image recorded by a scanning particle microscope, and an image recorded by a scanning probe microscope.

3. The method of claim 1 , wherein the model of machine learning comprises at least one element from the group: a parametric mapping, an artificial neural network, a deep neural network, a time delay neural network, a convolutional neural network, a recurrent neural network, a long short-term memory network, and/or a generative model.

4. The method of claim 3 , wherein the model of machine learning comprises:
a. at least one encoder block for determining information-carrying features of an image and the design data associated with the image; and
b. at least one decoder block for producing at least one effect of the defects from the determined information-carrying features, wherein the at least one effect of the defects shows what an overlay of the image with a reference image looks like.

5. The method of claim 1 , wherein the defects comprise at least one element from the group: placement errors of one or more pattern elements of the element of the photolithography process; critical dimension errors of one or more pattern elements of the element of the photolithography process; and overlay errors of two or more photolithographic masks.

6. The method of claim 1 , wherein the training of the model of machine learning comprises: providing the plurality of images used for training purposes and the plurality of design data associated with the images used for training purposes as input data and providing the plurality of effects of the defects corresponding to the images used for training purposes as comparison data for the output data of the model of machine learning.

7. The method of claim 1 , wherein images used for training purposes comprise measured images and/or simulated images.

8. The method of claim 7 , further including the step of: simulating design data and/or modified design data of the element for the photolithography process for the purposes of producing simulated images.

9. The method of claim 8 , wherein producing simulated images comprises at least one element from the group: carrying out a rigorous simulation by numerically solving Maxwell's equations, wherein design data and/or modified design data of the element of the photolithography process are used as input data, carrying out a simulation with the aid of a Kirchhoff model, wherein the design data and/or the modified design data of the element of the photolithography process are used as input data, carrying out a particle-beam-based imaging simulation, wherein design data and/or modified design data of the element of the photolithography process are used as input data and carrying out a scanning-probe-based imaging simulation, wherein design data and/or modified design data of the element of the photolithography process are used as input data.

10. The method of claim 1 , wherein the provision of corresponding effects of the defects for the purposes of training the model of machine learning further comprises the step of: overlaying an image used for training purposes with a reference image for producing the at least one effect of the defects corresponding to the image.

11. The method of claim 1 , wherein the provision of corresponding effects of the defects for the purposes of training the model of machine learning further comprises the step of: determining a reference image by: imaging a substantially defect-free region of the element of the photolithography process, which has the same pattern elements as the region of the measured image, and/or simulating the design data for the region of the measured image of the element of the photolithography process.

12. The method of claim 10 , wherein overlaying the image with the reference image comprises: forming a difference between the image and the reference image.

13. The method of claim 12 , wherein forming the difference comprises at least one element from the group: determining a deviation of a critical dimension, determining a contrast deviation, and determining a placement deviation of one or more pattern elements.

14. The method of claim 1 , wherein the training of the model of machine learning comprises:
a. training the model of machine learning using a first number of simulated images, design data associated with the simulated images with corresponding effects of the defects in a first phase; and
b. training the model of machine learning using a second number of measured images, design data associated with the measured images with corresponding effects of the defects in a second phase, with the first phase being carried out before the second phase.

15. The method of claim 14 , wherein the first number of simulated images is greater than the second number of measured images.

16. The method of claim 14 , wherein steps a. and b. are run through at least twice.

17. A computer program comprising instructions which, when executed by a computer system, prompt the computer system to carry out the method steps of claim 1 .

18. An apparatus for determining at least one unknown effect of defects of an element of a photolithography process, the apparatus comprising:
a. means for providing a model of machine learning for a relationship between an image, design data associated with the image and at least one effect of the defects arising from the image;
b. means for training the model of machine learning using a multiplicity of images used for training purposes, design data associated with the images used for training purposes and the corresponding effects of the defects; and
c. means for determining the unknown effect of the defects by applying the trained model of machine learning to a measured image and the design data associated with the measured image,
wherein the image comprises an image recorded by an optical imaging system, and
wherein the image recorded by the optical imaging system comprises an aerial image and/or wherein the aerial image comprises an aerial image focus stack.

19. The apparatus of claim 18 , wherein the apparatus comprises an exposure system for the element of the photolithography process and a magnifying lens that is embodied to image a portion of the element of the photolithography process on a photodetector.

20. The computer program of claim 17 , comprising instructions which, when executed by the computer system, prompt the computer system to carry out t

Assignees

• Zeiss Carl Smt Gmbh

Inventors

• Freytag Alexander
• Husemann Christoph
• Seidel Dirk
• Schmidt Carsten

Classifications

• G06N3/0455

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

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/0475

  Generative networks · CPC title

• G06N3/0442

  characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] · CPC title

• G06N3/09

  Supervised learning · CPC title