Wafer Geometry Interferometry for Lithography Focus Control
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Solution Overview
Problem
Critical dimension and pattern defectivity in semiconductor fabrication are affected by focus errors and wafer geometry, necessitating improved systems and methods for prediction and control.
Innovation Solution
A method and system that utilize patterned wafer geometry measurements to predict critical dimension and pattern defectivity by establishing correlations between wafer geometry data and critical dimension or defect measurements, selecting optimal thresholds for binary maps, and providing predictions for new wafers to improve fabrication processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional lithography processes are used without wafer geometry consideration, then the fabrication process is simpler, but critical dimension control and pattern defectivity worsen
Solution Approach 1:
The system performs wafer geometry measurements and creates predictive models before the actual lithography process. By measuring wafer geometry parameters (flatness, curvature, thickness) in advance and using machine learning algorithms to predict critical dimension outcomes, the system prepares correction strategies beforehand, improving precision without adding complexity to the core lithography process
Solution Approach 2:
The system implements a feedback loop where critical dimension measurements from reference wafers are correlated with wafer geometry data, and this feedback is used to train predictive models. The models then predict critical dimension issues for new wafers, enabling proactive process adjustments that improve manufacturing precision while maintaining operational simplicity
2Manufacturing precision
If wafer geometry measurements and predictive modeling are implemented, then critical dimension control improves, but measurement and data processing requirements increase
Solution Approach 1:
The system uses a single wafer geometry measurement system that serves multiple functions: measuring flatness, curvature, thickness, and other geometric parameters. This multi-functional approach enables comprehensive wafer characterization without requiring multiple specialized measurement devices, thus improving pattern defectivity control while managing measurement requirements
Solution Approach 2:
The system creates binary maps that are simplified representations or copies of the actual wafer geometry and critical dimension data. These binary maps capture essential patterns and defects without requiring full-resolution measurements, enabling effective analysis and prediction while reducing the overall measurement burden
3Reliability
If reference wafer analysis is performed to establish predictive models, then prediction accuracy for new wafers improves, but processing time and computational resources increase
Solution Approach 1:
The system performs reference wafer analysis and establishes predictive models in advance, before production wafer processing begins. By completing the computationally intensive model training using historical reference data beforehand, the system achieves high prediction accuracy for new wafers with minimal real-time computational burden, thus improving reliability while managing time loss
Solution Approach 2:
The system focuses the predictive modeling on the most critical wafer geometry parameters and critical dimension metrics that have the strongest correlation. By concentrating analysis on these key parameters rather than attempting to model all possible variables, the system achieves sufficient prediction accuracy while significantly reducing processing time and computational resources
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 effective prediction and control of critical dimension and pattern defectivity, mitigating focus errors and improving lithography processes by using patterned wafer geometry measurements to enhance process control and reduce defects.
Implementation Method 1
Predicting and controlling critical dimension issues and pattern defectivity in wafers using interferometry
Data Source
AI summary
Systems and methods for predicting and controlling pattern quality data (e.g., critical dimension and/or pattern defectivity) in patterned wafers using patterned wafer geometry (PWG) measurements are disclosed. Correlations between PWG measurements and pattern quality data measurements may be established, and the established correlations may be utilized to provide pattern quality data predictions for a given wafer based on geometry measurements obtained for the give wafer. The predictions produced may be provided to a lithography tool, which may utilize the predictions to correct focus and/or title errors that may occur during the lithography process.