Machine learning based subresolution assist feature placement

Patent metadata

Field	Value
Publication number	US-12416854-B2
Application number	US-202117907756-A
Country	US
Kind code	B2
Filing date	Feb 12, 2021
Priority date	Mar 3, 2020
Publication date	Sep 16, 2025
Grant date	Sep 16, 2025

Abstract

A method for training a machine learning model to generate a characteristic pattern, the method includes obtaining training data associated with a reference feature in a reference image. The training data includes (i) location data of each portion of the reference feature, and (ii) a presence value indicating whether the portion of the reference feature is located within a reference assist feature generated for the reference feature. The method includes training the machine learning model to predict a presence value based on the actual presence value in the training data. The predicted presence value indicates whether a portion of a feature (e.g., a skeleton point on a skeleton of a contour of the feature) is to be covered by an assist feature. The training is performed based on the training data such that a metric between a predicted presence value and the presence value is minimized.

First claim

The invention claimed is:

1. A non-transitory computer-readable medium having instructions therein that, when executed by a computer system, are configured to cause the computer to at least:
obtain training data associated with a reference feature in a reference image, wherein the training data includes (i) location data of a portion of the reference feature, and (ii) a presence value indicating whether the portion of the reference feature is located within a reference assist feature generated for the reference feature; and
train, based on the training data, a machine learning model configured to generate a characteristic pattern, such that a metric between a predicted presence value and the presence value is minimized.

2. The computer-readable medium of claim 1 , wherein the reference image is a continuous transmission mask (CTM) image generated by simulation of an optical proximity correction process using a target pattern, and wherein the reference feature corresponds to a target feature from the target pattern.

3. The computer-readable medium of claim 1 , wherein the reference assist feature includes one or more sub-resolution assist features placed around the reference feature, the one or more sub-resolution assist features being rectilinear in shape.

4. The computer-readable medium of claim 1 , wherein the training data includes training data for a plurality of reference features in one or more reference images.

5. The computer-readable medium of claim 1 , wherein the location data includes location data of each portion of a plurality of portions of the reference feature.

6. The computer-readable medium of claim 1 , wherein the instructions configured to cause the computer system to train the machine learning model are further configured to cause the computer system to:
(a) execute, the machine learning model, using the training data, to output the predicted presence value associated with the corresponding portion of the reference feature;
(b) determine the metric between the predicted presence value and the presence value;
(c) adjust the machine learning model such that the metric is reduced;
(d) determine whether the metric is minimized; and
(e) responsive to the metric not being minimized, perform (a), (b), (c), and (d).

7. The computer-readable medium of claim 1 , wherein the instructions are further configured to cause the computer system to:
obtain a specified reference image; and
determine, via execution of the machine learning model using the specified reference image, a preferred assist feature for placement in relation to a specified feature of the specified reference image, wherein the specified feature corresponds to a target feature of a target pattern to be printed on the substrate.

8. The computer-readable medium of claim 7 , wherein the instructions are further configured to cause the computer system to generate a characteristic pattern with the preferred assist feature, wherein the characteristic pattern is a pixelated image that includes the preferred assist feature placed in relation to the specified feature.

9. The computer-readable medium of claim 8 , wherein the instructions configured to cause the computer system to generate the characteristic pattern are further configured to cause the computer system to generate the characteristic pattern with a plurality of preferred assist features, wherein the preferred assist features are placed in relation to a plurality of reference features of the specified reference image, and wherein the preferred assist features are determined by execution of the machine learning model for the reference features.

10. The computer-readable medium of claim 7 , wherein the instructions configured to cause the computer system to determine the preferred assist feature are further configured to cause the computer system to:
obtain, from the specified reference image and using an intensity threshold, a specified contour of the specified feature,
generate a skeleton of the specified contour,
input location data and distance data to the machine learning model, wherein the location data includes coordinates of a set of points on the skeleton and the distance data indicates a closest distance from a point of the set of points to the specified contour, and
obtain, from the machine learning model, a predicted presence value for each point of the set of points, wherein the predicted presence value indicates whether the corresponding point is predicted to be located within the preferred assist feature.

11. The computer-readable medium of claim 10 , wherein the instructions are further configured to cause the computer system to:
generate a plurality of assist feature sets to cover points from the covered set of points,
determine a reward value for each assist feature set using a scoring function, and
determine a first assist feature set of the assist feature sets having a highest reward value as the preferred assist feature for placement in relation to the specified contour.

12. The computer-readable medium of claim 1 , wherein the instructions configured to cause the computer system to obtain the training data are further configured to cause the computer system to:
generate a plurality of assist feature sets for the reference feature based on a set of constraints for manufacturing of a mask pattern, wherein each assist feature set is associated with a reward value that is determined based on a specified scoring function,
determine a specified assist feature set of the plurality of assist feature sets associated with a highest reward value as the reference assist feature,
determine a status value for each portion of the reference feature as a function of the reward value of the plurality of assist feature sets and a number of assist feature sets in which the corresponding portion is determined to be located within, and
generate the location data and the presence value of the training data for each portion of the reference feature, wherein the presence value is set to a first value if the status value of the corresponding portion satisfies a status threshold, the first value indicating that the corresponding portion is located within the reference assist feature.

13. The computer-readable medium of claim 12 , wherein the instructions configured to cause the computer system to generate the presence value are further configured to cause the computer system to set the presence value to a second value if the status value of the corresponding portion does not satisfy the status threshold, the second value indicating that the corresponding portion is not located within the reference assist feature.

14. The computer-readable medium of claim 12 , wherein the instructions configured to cause the computer system to generate the plurality of assist feature sets are further configured to cause the computer system to:
generate a skeleton of the reference feature,
select a plurality of cut-off points on the skeleton, wherein each cut-off point segments the skeleton into a plurality of segments, and
for each cut-off point, generate an assist feature set having a reference assist feature for each segment of the plurality of segments, wherein the assist feature set is generated based on the set of constraints and a distance value associated with each point of a set of points on the skeleton.

15. The computer-readable medium of claim 14 , wherein the instructions configured to cause the computer system to determine the specified assist feature set having the highest reward value are further configured to cause the computer system to determine, using the specified scoring

Assignees

• Asml Netherlands Bv

Inventors

• Tao Jun
• Cao Yu
• Spence Christopher Alan

Classifications

• G06F30/398

  Design verification or optimisation, e.g. using design rule check [DRC], layout versus schematics [LVS] or finite element methods [FEM] (optical proximity correction [OPC] design processes G03F1/36) · CPC title

• G06F30/27

  using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model · CPC title

• G03F7/706841

  Machine learning · CPC title

• G03F7/70441

  Optical proximity correction [OPC] · CPC title

• G03F1/36 Primary

  Masks having proximity correction features; Preparation thereof, e.g. optical proximity correction [OPC] design processes · CPC title