US 7,465,590 B1Granted

Measurement of a sample using multiple models

AssigneeNanometrics Incorporated

InventorsYe Feng, Zhuan Liu

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Abstract

A sample that is processed to remove a top layer, e.g., using chemical mechanical polishing or etching, is accurately measured using multiple models of the sample. The multiple models may be constrained based on a pre-processing measurement of the sample. By way of example, the multiple models of the sample may be linked in pairs, where one pair includes a model simulating the pre-processed sample and another model simulating the post-processed sample with a portion of the top layer remaining, i.e., under-processing. Another pair of linked models includes a model simulating the pre-processed sample and a model simulating the post-processing sample with the top layer removed, i.e., the correct amount of processing or over-processing. The underlying layers in the linked model pairs are constrained to have the same parameters. The modeling process may use a non-linear regression or libraries.

Claims24

Dependent claims

A method comprising:

performing a first measurement of a sample having at least a first layer and a second layer that is under the first layer;
processing the sample to at least partially remove the first layer;
performing a second measurement of the sample after processing;
modeling the sample with a first pair of linked models, the first pair of linked models comprising a first model of the pre-processed sample and a second model of the post-processed sample, wherein the first layer of the first model and the first layer of the second model are independent variables and the second layer of the first model and the second layer of the second model are constrained to have the same parameters;
modeling the sample with a second pair of linked models, the second pair of linked models comprising a third model of the pre-processed sample and a fourth model of the post-processed sample, wherein the first layer of the third model is an independent variable, the first layer of the fourth model has a thickness of zero, the second layer of the third model and the second layer of the fourth model are independent variables;
using the first measurement, the second measurement, the first model, the second model, the third model, and the fourth model to determine at least one desired parameter of the post-processed sample;
and
reporting the results of the determined at least one desired parameter of the post-processed sample.

5 more elements

A method comprising:

performing a first measurement of a sample having a first layer, and a second layer that is under the first layer;
processing the sample to at least partially remove the first layer;
performing a second measurement of the sample after processing;
modeling the post-processed sample with a first model and the second measurement, the first model modeling the first layer as an independent variable and constraining the modeling of the second layer based on the first measurement;
modeling the post-processed sample with a second model and the second measurement, the second model modeling the first layer with a thickness of zero and modeling the second layer as an independent variable;
using the first measurement, second measurement, the first model and the second model to determine at least one parameter of the post-processed sample;
and
reporting the results of the determined at least one desired parameter of the post-processed sample.

5 more elements

Dependencies are inferred from the claim text.