Pattern Edge Roughness Measurement Using PSD Noise Separation
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Solution Overview
Problem
Existing edge roughness measurement methods face challenges in achieving high accuracy due to random noise, especially when the number of line patterns is small or the lines are short, leading to decreased detection accuracy and increased complexity.
Innovation Solution
A pattern measurement system that generates individual line profiles, a sum line profile, and an original image, performs pattern matching and position correction, calculates power spectrum density, and averages it to detect random noise components accurately, allowing for higher accuracy edge roughness measurement without measurement pattern limitations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of line patterns is increased to improve noise detection accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent divides the image into multiple line profiles and processes each line profile separately to extract power spectrum density. By segmenting the measurement into individual line profiles and processing them independently, the system achieves accurate noise detection without requiring a large number of patterns, thus resolving the contradiction between measurement precision and device complexity
Solution Approach 2:
The patent performs preliminary position correction on each line profile using pattern matching before calculating power spectrum density. This preliminary action ensures accurate alignment of line profiles, enabling reliable noise detection from fewer patterns and reducing the need for increased pattern quantities, thereby maintaining measurement precision while avoiding increased processing complexity
2Device complexity
If only high frequency component is used to estimate random noise amount, then processing is simplified, but measurement precision deteriorates
Solution Approach 1:
The patent extends the noise detection approach from only high frequency components to include multiple frequency components by calculating power spectrum density across the entire frequency spectrum. This dimensional expansion in frequency analysis enables comprehensive noise characterization, improving measurement precision while maintaining processing efficiency through systematic frequency domain analysis
3Productivity
If PSD value is not sufficiently converged in high frequency portion, then processing is faster, but measurement precision deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where line profiles are iteratively position-corrected using pattern matching, and power spectrum density is recalculated based on the corrected profiles. This feedback loop continues until convergence is achieved, ensuring accurate PSD values in the high frequency portion while maintaining efficient processing through targeted iterations rather than exhaustive computation
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system enables high-accuracy detection of random noise components and measurement of edge roughness, improving detection accuracy and efficiency by eliminating measurement pattern limitations.
Implementation Method 1
a signal obtained by charged particle beam scan or light irradiation with respect to a sample
Implementation Method 2
a signal obtained by charged particle beam scan or light irradiation with respect to a sample
Data Source
AI summary
Proposed is a technique that can detect a random noise component at high accuracy without measurement pattern limitation and enables edge roughness measurement at higher accuracy. According to this disclosure, pattern matching and edge position correction are performed with respect to each of the left edge and the right edge of a line pattern in an obtained line pattern image, and an image with no roughness is generated. A PSD value is measured from the image, and the average PSD value of all the frequencies is determined as a random noise component, so that the random noise component can be detected at high accuracy. Further, the PSD value (random noise component) is subtracted from the PSD value of an original image, thereby measuring edge roughness at high accuracy.