Periodic Pattern Signal Removal for Substrate Defect Detection
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Solution Overview
Problem
Existing metrology and inspection tools struggle to differentiate defects on substrates from background signals caused by periodic patterns, leading to false defect detection and inefficiencies.
Innovation Solution
A method involving Fourier analysis and threshold techniques to decompose and eliminate signals from periodic patterns on substrates, followed by inverse transformation and equalization to enhance defect detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional inspection algorithms are used to detect defects on substrates with periodic patterns, then defect detection can be performed, but false defect detection increases due to background signals from periodic structures
Solution Approach 1:
The patent segments the defect detection process into multiple processing stages: acquiring original image data, performing Fourier transformation to separate periodic pattern signals from defect signals, selectively removing periodic pattern components, and reconstructing the image. This segmentation allows the system to isolate and eliminate background signals while preserving defect signals, thereby improving detection accuracy and reducing false positives
Solution Approach 2:
The patent introduces Fourier transformation as an intermediary process between image acquisition and defect analysis. This mathematical transformation acts as a mediator that converts spatial domain image data into frequency domain representation, where periodic pattern signals and defect signals can be differentiated and separated. The intermediary process enables selective removal of periodic pattern components without affecting defect detection
2Measurement precision
If model images of circuits are used for comparison, then defect detection can be performed, but processing time and computational resources increase
Solution Approach 1:
The patent extracts only the essential periodic pattern signal components from the image data through Fourier transformation, rather than using complete model images for comparison. By extracting and removing only the periodic pattern components, the system achieves defect detection without requiring time-consuming model image matching, significantly reducing processing time while maintaining detection capability
Solution Approach 2:
The patent enables the inspection system to self-process and self-analyze the acquired image data through automated Fourier transformation and periodic pattern removal. The system uses its own acquired data to identify and eliminate periodic pattern signals without requiring external model images, making the process self-sufficient and reducing computational overhead associated with model-based comparison methods
Data Source
AI summary
Various examples described herein include various mechanisms, techniques, and methods to subtract collected signals caused by a periodic pattern formed on substrates to enable a higher level of defect detection on substrates. Signals detected by various types of metrology and substrate-inspection systems that are caused by periodic patterns on inspected substrates can be reduced or eliminated by, for example, a Fourier analysis of the detected signals. Both gray-scale value thresholds and area thresholds may be applied after the Fourier analysis of the image and are sufficient for defect detection on an image with a substantially-reduced number of false defects or no defects being present in a final image produced after processing. Other techniques and methods are also disclosed.