APPARATUS AND METHOD FOR INFERRING PARAMETERS OF A MODEL OF A MEASUREMENT STRUCTURE FOR A PATTERNING PROCESS

• US 20180239851A1
• Filed: 02/20/2018
• Published: 08/23/2018
• Est. Priority Date: 02/21/2017
• Status: Active Application

• Current Assignee: ASML Netherlands BV (ASML Holding NV)
• Original Assignee: ASML Netherlands BV (ASML Holding NV)
• Inventors: YPMA, Alexander, VAN DER SCHAAR, Maurits, TSIROGIANNIS, Georgios, KARSSEMEIJER, Leendert Jan, FAN, Chi-Hsiang
• Application Number:
  US15/900,735
• Publication Number:
  US 20180239851A1
• Time in Patent Office: Days
• CPC Class Codes: G03F 7/70516 Calibration of components of the microlithographic apparatus, e.g. light sources, addressable masks or detectors
• CPC Class Codes: G03F 7/70616 Monitoring the printed patterns
• CPC Class Codes: G03F 7/70625 Dimensions, e.g. line width, critical dimension [CD], profile, sidewall angle or edge roughness
• CPC Class Codes: G03F 7/70633 Overlay, i.e. relative alignment between patterns printed by separate exposures in different layers, or in the same layer in multiple exposures or stitching
• CPC Class Codes: G03F 9/7019 Calibration
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks

Abstract

A process of calibrating parameters of a stack model used to simulate the performance of measurement structures in a patterning process, the process including: obtaining a stack model used in a simulation of performance of measurement structures; obtaining calibration data indicative of performance of the measurement structures; calibrating parameters of the model by, until a termination condition occurs, repeatedly: simulating performance of the measurement structures with the simulation using a candidate model; approximating the simulation, based on a result of the simulation, with a surrogate function; and selecting a new candidate model based on the approximation.

Claims

1. A method of calibrating parameters of a stack model used to simulate the performance of measurement structures in a patterning process, the method comprising:

obtaining a stack model used in a simulation of performance of measurement structures used in a patterning process;

obtaining calibration data indicative of performance of the measurement structures in the patterning process, the calibration data being empirical measurements or results of simulations of performance of the measurement structures;

after obtaining the calibration data, calibrating, by a processing system, parameters of the stack model by, until a termination condition occurs, repeatedly;

performing the simulation of performance of measurement structures using a candidate stack model having candidate-model parameters;

approximating the simulation over a range of candidate stack models, based on a result of the simulation, with a surrogate function, wherein the surrogate function;

takes as an input candidate stack models having candidate-model parameters, and

outputs a measure of fitness and/or a measure of uncertainty about fitness, wherein fitness is indicative of differences between approximated simulation results based on an input candidate stack model and the obtained calibration data; and

selecting a new candidate model based on the measure of fitness and/or measure of uncertainty about fitness.

2. The method of claim 1, wherein calibrating parameters of the stack model comprises calibrating a model of a patterned film stack in which the measurement structures are formed, wherein calibrating is performed using Bayesian optimization and wherein the surrogate function is fitted to simulation results.

3. The method of claim 1, wherein calibrating parameters of the stack model comprises concurrently calibrating parameters of a plurality of stack models of a plurality of measurement structures, the plurality of measurement structures including an alignment mark, an overlay metrology target, a critical dimension metrology target, a plurality of alignment marks, a plurality of overlay metrology targets, a plurality of critical dimension metrology targets, or a combination selected therefrom.

4. The method of claim 1, comprising:

determining that a previous stack model results in a simulation that does not correctly predict the performance of the measurement structures in the patterning process relative to obtained empirical measurements of performance,

wherein;

calibrating is performed in response to the determination, and

the calibration causes the previous stack model to change such that the simulation more closely matches the obtained empirical measurements relative to simulations based on the previous stack model.

5. The method of claim 1, wherein approximating the simulation with the surrogate function comprises approximating an aggregate measure of differences between the empirical measurements and the simulation over a range of candidate models as a Gaussian process, wherein the measure of fitness is a mean of the Gaussian process and the measure of uncertainty is a variance or standard deviation of the Gaussian process.

6. The method of claim 1, wherein approximating the simulation over a range of candidate stack models, based on a result of the simulation, with a surrogate function comprises:

obtaining a prior version of the surrogate function; and

transforming the prior version of the surrogate function into a posterior version of the surrogate function based on a data likelihood function and the results of the simulation with Bayes'"'"' rule of inference.

7. The method of claim 1, wherein the simulation is configured to simulate responses of measurement structures in the form of alignment marks or overlay metrology targets to process variation by varying parameters of the stack model, the parameters including film thickness, etch depth, line-width, and/or line-pitch, and simulating results of the variations.

8. The method of claim 1, wherein approximating the simulation over a range of candidate stack models comprises root-mean-square values of performance indicator differences between approximated simulation results based on input candidate stack models and the obtained calibration data.

9. The method of claim 1, wherein the performance of measurement structures is indicative of a ratio of change in a parameter of the model to a change in a measure of alignment.

10. The method of claim 1, wherein calibrating parameters of the model comprises repeatedly, in at least some iterations, training the surrogate function based on simulation results.

11. The method of claim 1, wherein:

the measurement structures comprise a grating at least partially overlapping another grating in a film stack; and

more than four parameters of the stack model are concurrently calibrated with a global optimization.

12. The method of claim 1, wherein the surrogate function correlates points in a parameter space of the stack model with respective statistical distributions of outputs at the respective points.

13. The method of claim 12, comprising adjusting the surrogate function based on the result of the simulation by:

for a point in the parameter space of the stack model upon which the simulation is based;

aligning a measure of central tendency of the respective statistical distribution to the result of the simulation; and

reducing or eliminating a measure of variance of the respective statistical distribution; and

for a point in the parameter space adjacent the point upon which the simulation is based;

adjusting a measure of central tendency of the respective statistical distribution to be closer to the result of the simulation; and

reducing a measure of variance of the respective statistical distribution.

14. The method of claim 1, wherein selecting a new candidate stack model based on the measure of fitness and/or the measure of uncertainty about fitness comprises determining candidate stack model parameters by determining an extremum of an acquisition function that is based on both the measure of fitness and the measure of uncertainty about fitness.

15. The method of claim 14, wherein:

the extremum is a global maximum;

between repetitions of the calibration, adjusting a parameter of the acquisition function to change relative effects of the measure of fitness and the measure of uncertainty about fitness to decrease an amount of effect on the acquisition function by the measure of uncertainty about fitness and increases an amount of effect on the acquisition function by the measure of fitness.

16. The method of claim 1, wherein calibrating parameters of the stack model comprises calibrating parameters of statistical distributions of parameters of the stack model.

17. The method of claim 1, wherein calibrating parameters of the model comprises using simulations of both alignment mark performance and overlay metrology target performance to infer a plurality of parameters of a film stack with which both measurement structures in the form of alignment marks and overlay metrology targets are formed.

18. The method of claim 1, comprising:

simulating performance of the measurement structures using calibrated parameters of the stack model;

causing a calibrated simulation result to be displayed to a user;

receiving, from the user, an adjustment to the measurement structures; and

patterning a plurality of substrates based on measurements of the measurement structures.

19. A system, comprising:

one or more processors; and

memory storing instructions that when executed by at least some of the processors effectuate operations comprising;

obtaining a stack model used in a simulation of performance of measurement structures used in a patterning process;

obtaining calibration data indicative of performance of the measurement structures in the patterning process, the calibration data being empirical measurements or results of simulations of performance of the measurement structures;

after obtaining the calibration data, calibrating parameters of the stack model by, until a termination condition occurs, repeatedly;

performing simulation of the performance of the measurement structures using a candidate stack model having candidate-model parameters;

approximating the simulation over a range of candidate stack models, based on a result of the simulation, with a surrogate function, wherein the surrogate function;

takes as an input candidate stack models having candidate-model parameters, and

outputs a measure of fitness and/or a measure of uncertainty about fitness, wherein fitness is indicative of differences between approximated simulation results based on input candidate stack models and the obtained calibration data; and

selecting a new candidate model based on the measures of fitness and/or measures of uncertainty about fitness; and

storing the new candidate model parameters associated with the new candidate model as calibrated parameters of the stack model in memory.

20. A method of calibrating parameters of a stack model used to simulate the performance of measurement structures for a patterning process, the method comprising:

obtaining a stack model used in a simulation of the performance of the measurement structures;

obtaining calibration data indicative of performance of the measurement structures in the patterning process, the calibration data being empirical measurements or results of simulations of performance of the measurement structures;

after obtaining the calibration data, calibrating, by a processing system, parameters of the stack model by, until a termination condition occurs, repeatedly;

a) simulating performance of the measurement structures based on a candidate stack model having candidate-model parameters;

b) approximating the simulated performance over a range of candidate stack models, based on evaluation of a surrogate function mapping the candidate-model parameters to a measure of fitness and/or a measure of uncertainty about fitness, wherein the fitness is indicative of a difference between the approximated simulated performance and the calibration data;

c) selecting a new candidate stack model based on the fitness and/or uncertainty about the fitness;

d) go back to a), wherein the performance is simulated based on the new candidate stack model having new candidate model parameters.