Method for determining defectiveness of pattern based on after development image
Described herein is a method of training a model configured to predict whether a feature associated with an imaged substrate will be defective after etching of the imaged substrate and determining etch conditions based on the trained model. The method includes obtaining, via a metrology tool, (i) an after development image of the imaged substrate at a given location, the after development image including a plurality of features, and (ii) an after etch image of the imaged substrate at the given location; and training, using the after development image and the after etch image, the model configured to determine defectiveness of a given feature of the plurality of features in the after development image. In an embodiment, the determining of defectiveness is based on comparing the given feature in the after development image with a corresponding etch feature in the after etch image.
1. A method of training a model configured to predict whether a feature associated with an imaged substrate will be defective after etching of the imaged substrate, the method comprising:
obtaining, via a metrology tool, (i) an after development image of the imaged substrate at a given location, the after development image including a plurality of features, and (ii) an after etch image of the imaged substrate at the given location, the after etch image including etched features corresponding to the plurality of features; and
training, using the after development image and the after etch image, the model configured to determine defectiveness of a given feature of the plurality of features in the after development image,
wherein the determining of defectiveness is based on comparing the given feature in the after development image with a corresponding etch feature in the after etch image.
2. The method of claim 1 , wherein the model is an empirical model or a machine learning model, wherein the empirical model is a function of a physical characteristic of the feature associated with the imaged substrate.
3. The method of claim 1 , wherein the obtaining of the after development image comprises:
imaging, via a patterning apparatus, a mask pattern on a substrate;
obtaining a developed substrate of the imaged substrate;
aligning the metrology tool to the developed substrate at a given location; and
capturing an image of the developed substrate.
4. The method of claim 1 , wherein the obtaining of the after etch image comprises:
etching, via an etch process with specified etch conditions, the imaged substrate;
aligning the metrology tool to the etched substrate at the given location; and
capturing the after etch image of the etched substrate.
5. The method of claim 4 , wherein the etch conditions include etchant composition, plasma gas parameters, etching rate, electro-magnetic fields, a plasma potential, an inductive or a capacitive type of etching, a temperature of the substrate, ion energy distribution, ion angular distribution, sputtering and redeposition rates, or a combination thereof.
6. The method of claim 1 , wherein the training comprises:
aligning, based on the plurality of features, the after development image and the after etch image;
comparing each of the features of the plurality of features in the after development image with a corresponding feature of etched features in the after etch image;
determining, based on the comparison, whether a given etched feature in the after etch image satisfy a defect condition;
responsive to not satisfying the defect condition, classifying the given etched feature as defective; and
adjusting a model parameter value of the model based on the defectiveness of the given etched feature.
7. The method of claim 6 , wherein the adjusting of the model parameter value includes adjusting values of a plurality of model parameters.
8. The method of claim 6 , wherein the defective condition is a physical characteristic of the given etched feature in the after etch image.
9. The method of claim 8 , wherein the physical characteristic is at least one of:
a critical dimension of the given etched feature; or
a displacement of the given etched feature with respect to the given feature of the after development image.
10. The method of claim 1 , wherein the defectiveness is characterized by at least one of:
a binary determination of defective or not defective; or
a probability of the given feature being defective.
11. The method of claim 2 , wherein the machine learning model is a convolution neural network.
12. The method of claim 11 , wherein a plurality of model parameters are weights or biases associated with one or more layers of the machine learning model.
13. The method of claim 12 , wherein the plurality of model parameters being weights or biases includes the model parameters being weights and biases.
14. The method of claim 1 , wherein the metrology tool is an optical microscope or an electron beam microscope.
15. A system for determining a fraction of features that will fail after etching, the system comprises:
a metrology tool for capturing an after development image (ADI) of a substrate at a given location, the after development image including a plurality of features; and
a processor configured to:
execute a model for determining failure rates of the plurality of features of ADI that will fail after etching,
wherein the model is a combination of (i) a first probability distribution function configured to estimate a distribution of physical characteristic values for non-failing holes, and (ii) a second probability function configured to determine failure rates based on physical characteristic values of all the plurality of features of the ADI.