SEM Edge Position Analysis With Weighted Local Regression
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Solution Overview
Problem
Existing methods for determining edge positions in SEM images of line-and-space patterns are hindered by noise, leading to inaccurate detection of edge positions and loss of high spatial frequency components.
Innovation Solution
A method involving the use of a weight function to selectively reduce noise by aggregating intensity values and performing local regression on SEM images, using a weight function that prioritizes average edge positions and a fitting function defined by the intensity profile, to accurately determine edge positions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional edge detection methods are used on SEM images, then the process is simple, but noise causes inaccurate edge position detection and loss of high spatial frequency components
Solution Approach 1:
The patent segments the edge detection process into multiple stages: (1) obtaining an average intensity profile by aggregating intensity values along the line extension direction, (2) determining average edge positions from this profile, (3) performing local regression with weight functions centered at average edge positions, and (4) determining final edge positions from the fitted intensity profiles. This segmentation allows systematic noise reduction while preserving high spatial frequency components.
Solution Approach 2:
The patent applies local quality by using weight functions that are localized around average edge positions. The weight function assigns higher weights to regions near the average edge position and lower weights to distant regions, enabling localized noise reduction that preserves local edge features and high spatial frequency components while reducing noise in other areas.
2Object-affected harmful factors
If noise reduction is applied to SEM images, then noise is reduced, but high spatial frequency components may be lost
Solution Approach 1:
The patent changes the parameter of weight function characteristics (width, shape, and positioning) to optimize the balance between noise reduction and preservation of high spatial frequency components. By adjusting the weight function parameters and performing local regression only in specific regions around edge positions, the method reduces noise while preserving the high spatial frequency information contained in the edge transitions.
Data Source
AI summary
An edge position determination method includes determining an average edge position by aggregating intensity values in an extension direction of lines in a SEM image of line-and-space, fitting a first intensity profile indicating distribution of the intensity values in a direction perpendicular to the extension direction at coordinates indicating each position in the extension direction in the SEM image using a weight function in which a weight at the average edge position is the largest and a fitting function defined in accordance with the first intensity profile, and determining an edge position at the coordinates from a second intensity profile obtained through the fitting.