Overlay Error Measurement Using Sub-Resolution Inspection Images
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Solution Overview
Problem
In semiconductor wafer manufacturing, Non-Zero Offset (NZO) errors pose a challenge in layered manufacturing processes, as they lead to misalignment and require costly and time-consuming scanning electron microscope (SEM) calibration, which is not always necessary or efficient.
Innovation Solution
A method and system that utilize sub-resolution image analysis to measure NZO and other errors like Critical Dimension (CD) and edge-to-edge placement by capturing images of features and deriving error measurements from image parameters, reducing the need for SEM calibration by comparing image parameters with reference quantities, and using calibration data to convert these measurements into accurate error values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If SEM calibration is used to measure NZO errors, then measurement precision is improved, but productivity deteriorates and device complexity increases
Solution Approach 1:
The patent replaces the mechanical/electronic scanning electron microscope (SEM) system with an optical inspection system. The optical system captures images of metrology targets and uses image processing algorithms to measure NZO errors, substituting complex SEM hardware with simpler optical components and computational methods.
Solution Approach 2:
The patent creates optical copies (images) of the metrology targets using an inspection tool's imaging system. These image copies are then processed to extract NZO measurements, eliminating the need for direct physical measurement with SEM and enabling faster, non-destructive measurement.
2Measurement precision
If SEM calibration is used to measure NZO errors, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent replaces the complex SEM hardware system with a simpler optical inspection system combined with image processing software. The optical system uses standard imaging components and computational algorithms to achieve NZO measurement without requiring the complex electron beam generation, scanning, and detection mechanisms of SEM.
Solution Approach 2:
The inspection system performs self-calibration by capturing images of metrology targets with known geometries and using image processing algorithms to automatically determine NZO errors. The system uses its own imaging capability and computational resources to calibrate itself without external SEM intervention.
3Productivity
If sub-resolution image analysis is used to measure errors, then productivity is improved, but measurement precision may deteriorate
Solution Approach 1:
The patent transitions from direct spatial measurement (resolving features in real space) to measurement in the frequency domain or parameter space. By analyzing image parameters such as intensity distributions, edge positions, and pattern geometries through computational methods, the system extracts precise error measurements without requiring direct resolution of sub-resolution features.
Solution Approach 2:
The patent performs preliminary image capture and processing on metrology targets with known geometries to establish reference data and calibration curves. These preliminary measurements enable rapid error determination during actual production inspection without requiring real-time complex calculations on sub-resolution features.
Data Source
AI summary
A method and system for measuring overlay in a semiconductor manufacturing process comprise capturing an image of a feature in an article at a predetermined manufacturing stage, deriving a quantity of an image parameter from the image and converting the quantity into an overlay measurement. The conversion is by reference to an image parameter quantity derived from a reference image of a feature at the same predetermined manufacturing stage with known overlay (“OVL”). There is also disclosed a method of determining a device inspection recipe for use by an inspection tool comprising identifying device patterns as candidate device care areas that may be sensitive to OVL, deriving an OVL response for each identified pattern, correlating the OVL response with measured OVL, and selecting some or all of the device patterns as device care areas based on the correlation.