Critical Dimension Uniformity Reconstruction via Multi-Feature Stitching
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Solution Overview
Problem
Current methods for determining critical dimension uniformity in photolithography struggle with accurately measuring and correcting variations across semiconductor wafers, particularly as feature sizes shrink, leading to inconsistencies in integrated circuit quality and performance.
Innovation Solution
A method involving the measurement of multiple types of critical features at various locations on a substrate, combining these measurements using a combined measurement function defined by coefficients to determine and correct critical dimension variations, which can include transformations to enhance smoothness and minimize curvature, allowing for improved uniformity reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple types of features are measured at various locations on the substrate, then the accuracy of critical dimension uniformity reconstruction is improved, but the complexity of the measurement and data processing system increases
Solution Approach 1:
The substrate is divided into multiple measurement locations, and different types of features (isolated lines, dense lines, contact holes) are measured separately at each location. This segmentation allows for comprehensive coverage of critical dimensions across the substrate while enabling systematic data organization and processing, resolving the contradiction between measurement accuracy and system complexity.
Solution Approach 2:
A universal combined measurement function is developed that can process measurements from multiple feature types using a unified mathematical model. This multi-functional approach integrates diverse measurement data (transmittance, reflectance, scattering) into a single reconstruction framework, improving accuracy without proportionally increasing system complexity.
2Manufacturing precision
If measurements are combined using transformations defined by coefficients to enhance smoothness, then the quality of critical dimension reconstruction is improved, but the complexity of data processing increases
Solution Approach 1:
Measurement data undergoes mathematical transformations with adjustable coefficients that control the smoothness of the reconstructed critical dimension map. By optimizing these parameters, the method enhances reconstruction quality while maintaining controllable processing complexity through systematic coefficient determination.
Solution Approach 2:
The combined measurement function incorporates feedback mechanisms where measurement results from different feature types are iteratively refined using predetermined relationships. This feedback loop continuously improves reconstruction quality by adjusting transformations based on observed deviations, balancing accuracy enhancement with processing complexity.
3Reliability
If different types of features are measured to reduce noise and positional errors, then the reliability of critical dimension uniformity determination is improved, but the time required for measurement and processing increases
Solution Approach 1:
Measurements from multiple feature types (isolated lines, dense lines, contact holes) are merged into a single combined measurement function. This combining approach reduces noise and positional errors by averaging out random variations across different feature types, improving reliability while the unified processing framework minimizes additional time requirements.
Solution Approach 2:
The method performs preliminary measurements on multiple feature types simultaneously across the substrate, collecting all necessary data in advance. This preliminary action allows for comprehensive noise reduction and error correction during the reconstruction phase, improving reliability without significantly extending total measurement time due to efficient parallel data collection.
Data Source
AI summary
Data associated with a substrate can be processed by measuring a property of at least a first type of specific features and a second type of specific features on a substrate. The first type of specific features is measured at a first plurality of locations on the substrate to generate a first group of measured values, and the second type of specific features is measured at a second plurality of locations on the substrate to generate a second group of measured values, in which the first and second groups of measured values are influenced by critical dimension variations of the substrate. A combined measurement function is defined based on combining the at least first and second groups of measured values. At least one group of measured values is transformed prior to combining with another group or other groups of measured values, in which the transformation is defined by a group of coefficients. Variations in the critical dimension across the substrate are determined based on the combined measurement function and a predetermined relationship between the measured values and the critical dimension.