Granted Patent B2

US 12,613,472 · App. 18/008,765 · Granted Apr 28, 2026

Inventors: Aiqin Jiang (Duanesburg, NY); Sudharshanan Raghunathan (Fremont, CA); Jill Elizabeth Freeman (San Jose, CA); Fuming Wang (Santa Clara, CA); Fei Yan (Shenzhen, CN)

Assignee: ASML NETHERLANDS B.V.

G03F7/705G03F7/70625G03F7/7065G03F7/706837G03F7/706839

Abstract

Claims (67)

1 . A non-transitory computer-readable medium having instructions that, when executed by a computer system, are configured to cause the computer system to at least:

obtain measurement data of a plurality of features printed on a substrate, the measurement data comprising tens of thousands or more data points;

generate, based on the measurement data associated with a group of features that are different, a base failure rate model for the group of features, wherein the base failure rate model is configured to identify a process window according to a failure rate of the group of features; and

use the base failure rate model and characteristic data of a specific feature to generate a feature-specific failure rate model for the specific feature, wherein the feature-specific failure rate model identifies a feature-specific process window such that an estimated failure rate of the specific feature is below a specified threshold.

2 . The computer-readable medium of claim 1 , wherein the instructions are further configured to cause the computer system to group, based on a metric, the plurality of features into a plurality of groups comprising the group.

3 . The computer-readable medium of claim 2 , wherein the metric includes: (a) a process window of each feature of the features, wherein the process window is a function of process variables associated with the corresponding feature, and (b) a characteristic parameter associated with the corresponding feature.

4 . The computer-readable medium of claim 3 , wherein the process variables include a focus and dose value associated with an apparatus used for the patterning process, and wherein the characteristic parameter includes a critical dimension (CD) value of the corresponding feature.

5 . The computer-readable medium of claim 2 , wherein the instructions configured to cause the computer system to group the features are further configured to cause the computer system to:

for each feature of the features, obtain as a metric value a mean CD value of the corresponding feature and a plurality of dose values and focus values associated with the mean CD at an edge of a process window of the corresponding feature, and

cluster the features into the groups of features based on the metric values for the features, wherein features within a specific group have metric values within a threshold.

6 . The computer-readable medium of claim 1 , wherein the instructions configured to cause the computer system to obtain the measurement data are further configured to cause the computer system to:

obtain images having the features printed on the substrate, and

analyze the images to select features that satisfy a specified criterion as the plurality of features.

7 . The computer-readable medium of claim 6 , wherein the specified criterion includes a number of occurrences of a feature on the substrate being below a first threshold or a number of occurrences of the feature in an image of the images being below a second threshold.

8 . The computer-readable medium of claim 1 , wherein the instructions configured to cause the computer system to generate the base failure rate model for the group of features are further configured to cause the computer system to:

obtain mean CD of the group of features;

determine local CD uniformity (LCDU) data of the group of features as the measurement data associated with the group of features;

obtain, based on the LCDU data, (i) a probability density function of CD of the group of features defined as a function of the mean CD, a dose value and a variance of the dose value of the patterning process, and (ii) a CD limit of the patterning process based on failure rate measurements of features in the group of features;

determine an estimated failure rate of the group of features based on the CD limit and the probability density function of the CD; and

generate the base failure rate model, which identifies the process window related to the dose value such that the estimated failure rate of the group of features is less than a predetermined threshold.

9 . The computer-readable medium of claim 1 , wherein the instructions configured to cause the computer system to generate the feature-specific failure rate model are further configured to cause the computer system to:

receive measurement data associated with the specific feature, wherein the measurement data includes CD values of the specific feature for a plurality of dose and focus values;

determine a specified metric value from the measurement data, wherein the specified metric value is determined as a function of a specified mean CD value and a plurality of dose values and focus values associated with the specified mean CD value at an edge of a process window associated with the specific feature; and

select, from the database, a specified base failure rate model whose metric value matches the specified metric value.

10 . The computer-readable medium of claim 1 , wherein the instructions are further configured to cause the computer system to:

obtain a base probability density function of CD from the specified base failure rate model; and

adjust a mean CD of the base probability density function based on the specified mean CD value to generate an adjusted probability density function.

11 . The computer-readable medium of claim 10 , wherein the instructions are further configured to cause the computer system to:

obtain as CD profile data at least one selected from: an interfield CD variation, intrafield CD variation, and/or a probability density function of CD of the specific feature; and

convolve the adjusted probability density function with the CD profile data to generate a convoluted probability density function associated with the specific feature.

12 . The computer-readable medium of claim 11 , wherein the instructions are further configured to cause the computer system to:

determine the estimated failure rate of the specific feature based on the convoluted probability density function; and

generate the feature-specific failure rate model configured to identify the feature-specific process window.

13 . The computer-readable medium of claim 1 , wherein the instructions are further configured to cause the computer system to adjust one or more apparatuses of the patterning process based on the process window related to the specified feature to reduce or minimize a failure rate associated with the specified feature.

14 . The computer-readable medium of claim 1 , wherein the instructions configured to cause the computer system to obtain the measurement data are further configured to cause the computer system to:

obtain a first set of process windows of the features, wherein the first set of process windows is representative of a first characteristic parameter of the features for a first set of dose and focus values, wherein the first set of process windows includes a first process window of a first feature of the features;

obtain multiple process window metrics for the features based on the first set of process windows;

rank the features based on one or more of the process window metrics;

obtain a second set of process windows of the features, wherein the second set of process windows includes a second process window of the first feature, wherein the second set of process windows is a function of (i) a second characteristic parameter associated with the features and (ii) a second set of dose and focus values associated with the second characteristic parameter;

perform an overlapping operation with the first process window and the second process window to generate an overlapped processed window, wherein the overlapped processed window is an intersection of shapes of the first process window and the second process window on a graph; and

obtain the process window metrics for the first feature based on the overlapped processed window.

15 . A method comprising:

obtaining measurement data of a plurality of features printed on a substrate, the measurement data comprising tens of thousands or more data points;

generating, by a hardware computer system and based on the measurement data associated with a group of features that are different, a base failure rate model for the group of features, wherein the base failure rate model is configured to identify a process window according to a failure rate of the group of features; and

using, by the hardware computer system, the base failure rate model and characteristic data of a specific feature to generate a feature-specific failure rate model for the specific feature, wherein the feature-specific failure rate model identifies a feature-specific process window such that an estimated failure rate of the specific feature is below a specified threshold.

16 . The method of claim 15 , further comprising grouping,

based on a metric, the plurality of features into a plurality of groups comprising the group.

17 . The method of claim 15 , wherein the generating the base failure rate model for the group of features includes:

obtaining mean CD of the group of features;

determining local CD uniformity (LCDU) data of the group of features as the measurement data associated with the group of features;

obtaining, based on the LCDU data, (i) a probability density function of CD of the group of features defined as a function of the mean CD, a dose value and a variance of the dose value of the patterning process, and (ii) a CD limit of the patterning process based on failure rate measurements of features in the group of features;

determining an estimated failure rate of the group of features based on the CD limit and the probability density function of the CD; and

generating the base failure rate model, which identifies the process window related to the dose value such that the estimated failure rate of the group of features is less than a predetermined threshold.

18 . The method of claim 15 , wherein the obtaining the measurement data includes:

obtaining a first set of process windows of the features, wherein the first set of process windows is representative of a first characteristic parameter of the features for a first set of dose and focus values, wherein the first set of process windows includes a first process window of a first feature of the features;

obtaining multiple process window metrics for the features based on the first set of process windows;

ranking the features based on one or more of the process window metrics;

obtaining a second set of process windows of the features, wherein the second set of process windows includes a second process window of the first feature, wherein the second set of process windows is a function of (i) a second characteristic parameter associated with the features and (ii) a second set of dose and focus values associated with the second characteristic parameter;

performing an overlapping operation with the first process window and the second process window to generate an overlapped processed window, wherein the overlapped processed window is an intersection of shapes of the first process window and the second process window on a graph; and

obtaining the process window metrics for the first feature based on the overlapped processed window.

19 . The method of claim 15 , further comprising:

receiving measurement data associated with the specific feature, wherein the measurement data includes CD values of the specific feature for a plurality of dose and focus values;

determining a specified metric value from the measurement data, wherein the specified metric value is determined as a function of a specified mean CD value and a plurality of dose values and focus values associated with the specified mean CD value at an edge of a process window associated with the specific feature; and

selecting, from the database, a specified base failure rate model whose metric value matches the specified metric value.

20 . The method of claim 15 , further comprising:

obtaining a base probability density function of CD from the specified base failure rate model; and

adjusting a mean CD of the base probability density function based on the specified mean CD value to generate an adjusted probability density function.

Assignments (4)

Priority Claims (2)

Continuity (1)

References Cited (39)