Sub-Resolution Defect Detection via Multi-Focus Optical Alignment
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Solution Overview
Problem
Current defect detection methods struggle to accurately detect pattern defects in semiconductor templates with feature sizes smaller than the resolution limit of optical systems, particularly in nanoimprint lithography, where the use of DUV light is limited and electron beam sources have low throughput.
Innovation Solution
A defect detection method involving the irradiation of light from an optical system, capturing multiple optical images under varying conditions, performing correction processing using filters, aligning images based on correlation, and separating defect and non-defect pixels in a gray scale value space to enhance detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If DUV light is used for optical inspection, then throughput is improved, but detection precision deteriorates for patterns smaller than the resolution limit
Solution Approach 1:
The inspection process is segmented into multiple passes: first capturing an optical image at normal focus, then capturing additional images at defocused conditions, and finally processing these segmented images through correlation-based alignment and noise filtering to achieve high-precision defect detection that overcomes the resolution limit
Solution Approach 2:
The method transitions from single-plane optical imaging to multi-plane imaging by capturing images at different focus conditions (normal focus and defocused states). This dimensional change in the focal plane allows extraction of phase information and enhancement of sub-resolution defect detection capability
2Measurement precision
If electron beam source is used for high precision defect detection, then measurement precision is improved, but productivity deteriorates due to low throughput
Solution Approach 1:
The method replaces the electron beam inspection system with an optical inspection system that uses multiple defocused imaging and correlation processing. This substitution maintains high detection precision for sub-100nm patterns while achieving significantly higher throughput compatible with mass production
3Measurement precision
If multiple optical images are captured and processed, then detection precision is improved, but device complexity increases
Solution Approach 1:
The system uses the captured optical images themselves to perform alignment and defect detection. By calculating correlation between images at different focus conditions, the system automatically determines alignment offsets and identifies defects without requiring external reference markers or complex alignment mechanisms
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
Enables the precise detection of defects in patterns smaller than the optical system's resolution limit, improving yield in semiconductor manufacturing by distinguishing defects from base pattern noise and increasing throughput without the limitations of electron beam sources.
Implementation Method 1
irradiating light from a light source in an optical system and obtaining a plurality of optical images of a sample
Implementation Method 2
obtaining a plurality of optical images of a sample having a repeated pattern having a size smaller than a resolution of the optical system
Data Source
AI summary
A defect detection method comprising, irradiating light from a light source in an optical system and obtaining a plurality of optical images of a sample having a repeated pattern of a size smaller than a resolution of the optical system; while changing the conditions of the optical system, performing correction processing for the optical images with the use of at least one of a noise filter and a convolution filter; shifting a position of the other optical images based on any of the plurality of optical images, obtaining a relationship between shift amounts of the other optical images and a change of correlation of a gray scale value between the plurality of optical images, and performing positional alignment of the optical images based on the shift amount obtained when the correlation is highest, performing defect detection of the sample with the use of the optical images after the positional alignment.