Process proximity correction method and the computing device for the same

Patent metadata

Field	Value
Publication number	US-2022035237-A1
Application number	US-202117245947-A
Country	US
Kind code	A1
Filing date	Apr 30, 2021
Priority date	Jul 29, 2020
Publication date	Feb 3, 2022
Grant date	—

Abstract

A process proximity correction method is performed by a process proximity correction computing device which performs a process proximity correction (PPC) through at least one of a plurality of processors. The process proximity correction method includes: converting a target layout including a plurality of patterns into an image, zooming-in or zooming-out the image at a plurality of magnifications to generate a plurality of input channels, receiving the plurality of input channels and performing machine learning to predict an after-cleaning image (ACI), comparing the predicted after-cleaning image with a target value to generate an after-cleaning image error, and adjusting the target layout on the basis of the after-cleaning image error.

First claim

What is claimed is:

1 . A process proximity correction method performed by a process proximity correction computing device which performs a process proximity correction (PPC) through at least one of a plurality of processors, the process proximity correction method comprising:
converting a target layout including a plurality of patterns into an image; zooming-in or zooming-out the image at a plurality of magnifications to generate a plurality of input channels; receiving the plurality of input channels and performing machine learning to predict an after-cleaning image (ACI); comparing the predicted after-cleaning image with a target value to generate an after-cleaning image error; and adjusting the target layout on the basis of the after-cleaning image error.

2 . The process proximity correction method of claim 1 , wherein the machine learning includes deep learning.

3 . The process proximity correction method of claim 2 , wherein the deep learning uses a generative adversarial network (GAN).

4 . The process proximity correction method of claim 2 , wherein the deep learning uses a convolutional neural network (CNN).

5 . The process proximity correction method of claim 2 , wherein the deep learning uses an artificial neural network (ANN).

6 . The process proximity correction method of claim 1 , wherein, when the after-cleaning image error becomes 0, adjustment of the target layout is stopped.

7 . The process proximity correction method of claim 1 , wherein the machine learning includes receiving of information on the plurality of patterns to predict the after-cleaning image.

8 . The process proximity correction method of claim 7 , wherein information on the plurality of patterns includes the number of the plurality of patterns.

9 . The process proximity correction method of claim 7 , wherein information on the plurality of patterns includes areas of each of the plurality of patterns.

10 . The process proximity correction method of claim 1 , wherein conversion into the image uses a binary type.

11 . The process proximity correction method of claim 1 , wherein conversion into the image uses an overlap type.

12 . The process proximity correction method of claim 1 , wherein conversion into the image uses a level set type.

13 . The process proximity correction method of claim 1 , wherein prediction of the after-cleaning image includes prediction of a critical dimension (CD) of the after-cleaning image.

14 . The process proximity correction method of claim 1 , further comprising:
predicting a plurality of after-cleaning images using the plurality of received input channels, respectively; and based on the plurality of after-cleaning images, determining whether to continue adjust the target layout or to stop further adjustment of the target layout.

15 . A process proximity correction method of a process proximity correction computing device which performs a process proximity correction (PPC) through at least one of a plurality of processors, the process proximity correction method comprising:
converting a target layout including a plurality of patterns into a first image; generating a first input channel by obtaining a first zoomed out version of the first image at a first magnification, and generating a second input channel by obtaining a second zoomed out version of the first image at a second magnification; receiving the first input channel and the second input channel and performing machine learning to predict an after-cleaning image (ACI); comparing the predicted after-cleaning image with a target value to generate after-cleaning image error; and adjusting the target layout and converting the target layout into a second image different from the first image, on the basis of the after-cleaning image error.

16 . The process proximity correction method of claim 15 , wherein when the after-cleaning image error becomes 0, adjustment of the target layout is stopped.

17 . The process proximity correction method of claim 15 , wherein the machine learning includes receiving of information on the plurality of patterns to predict the after-cleaning image.

18 . The process proximity correction method of claim 15 , wherein prediction of the after-cleaning includes prediction of a critical dimension (CD) of the after-cleaning image.

19 . The process proximity correction method of claim 15 , wherein the machine learning is performed using the first input channel and the second input channel to predict the ACI.

20 . A process proximity correction computing device which includes a plurality of processors,
wherein at least one of the plurality of processors executes a process proximity correction, and execution of the process proximity correction includes converting a target layout including a plurality of patterns into an image, zooming-in or zooming-out the image at a plurality of magnifications to generate a plurality of input channels, receiving the plurality of input channels and performing machine learning using the plurality of input channels, to predict after-cleaning image (ACI), comparing the predicted after-cleaning image with a target value to generate an after-cleaning image error, and adjusting the target layout on the basis of the after-cleaning image error.

21 - 27 . (canceled)

Assignees

• Samsung Electronics Co Ltd

Inventors

• Lee Soo Yong
• Kang Min-Cheol
• Kim U Seong
• Yang Seung Hune
• Lee Jee Yong

Classifications

• G06T7/0006

  using a design-rule based approach · CPC title

• G06T3/4046

  using neural networks · CPC title

• G06F30/392

  Floor-planning or layout, e.g. partitioning or placement · CPC title

• G06F30/3323

  using formal methods, e.g. equivalence checking or property checking · CPC title

• G03F1/76

  Patterning of masks by imaging · CPC title