In order to determine whether an exposure apparatus is outputting the correct dose of radiation and its projection system is focusing the radiation correctly, a test pattern is used on a mask for printing a specific marker onto a substrate. This marker is then measured by an inspection apparatus, such as a scatterometer, to determine whether there are errors in focus and dose and other related properties. The test pattern is configured such that changes in focus and dose may be easily determined by measuring the properties of a pattern that is exposed using the mask. The test pattern may be a 2D pattern where physical or geometric properties, e.g., pitch, are different in each of the two dimensions. The test pattern may also be a one-dimensional pattern made up of an array of structures in one dimension, the structures being made up of at least one substructure, the substructures reacting differently to focus and dose and giving rise to an exposed pattern from which focus and dose may be determined.

Lithographic Focus and Dose Measurement Using A 2-D Target

Measurements of line roughness are separated into groups depending upon pre-layers. Image data collected from similar pre-layer types are considered together in order to separate effects of line roughness from distortion of measurements caused by the pre-layers. The resulting line roughness measurements are used to estimate an aspect of line quality.

Measurement of line-edge-roughness and line-width-roughness on pre-layered structures

A system and method for assessing line edge roughness (LER) is disclosed. An artificial conformal liner on a simulation test structure absorbs the same amount of light that otherwise would be scattered in the dark-field by a rough surface. A RCWA based scatterometry model is used to model absorption and the absorption is correlated to line edge roughness, which allows RCWA to be used in effect to model LER.

Scatterometry measurement of line edge roughness in the bright field

Systems and methods are disclosed that remove noise from roughness measurements to determine roughness of a feature in a pattern structure. In one embodiment, a method for determining roughness of a feature in a pattern structure includes generating, using an imaging device, a set of one or more images, each including measured linescan information that includes noise. The method also includes detecting edges of the features within the pattern structure of each image without filtering the images, generating a biased power spectral density (PSD) dataset representing feature geometry information corresponding to the edge detection measurements, evaluating a high-frequency portion of the biased PSD dataset to determine a noise model for predicting noise over all frequencies of the biased PSD dataset, and subtracting the noise predicted by the determined noise model from a biased roughness measure to obtain an unbiased roughness measure.

Edge detection system and its use for optical proximity correction

System and method for removing noise from roughness measurements

A test structure is presented test structure on a substrate for monitoring a LER and/or LWR effect, said test structure comprising an array of features manufactured with amplified LER and/or LWR effect.

Boaz Brill

Papers

Line edge roughness measuring technique and test structure