Scatterometry Metrology Resonance Region Analysis for Process Variation
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Solution Overview
Problem
Current metrology techniques face challenges in accurately identifying and evaluating process variations on wafers, particularly in detecting and quantifying symmetric and asymmetric variations, which affect measurement accuracy and robustness.
Innovation Solution
The method involves deriving a landscape from simulation and scatterometry measurements to identify resonance regions related to optical illumination, analyzing their dependency on parameters, and estimating process variation, using these insights to enhance metrology tool performance and target design.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional metrology techniques are used to measure process variations, then measurement capability is provided, but measurement accuracy and robustness deteriorate due to inability to properly identify and quantify symmetric and asymmetric variations
Solution Approach 1:
The patent segments process variations into symmetric and asymmetric components, allowing separate identification and quantification of each type. This segmentation enables the metrology system to accurately measure different variation modes independently, improving both measurement accuracy and robustness by addressing each variation type with appropriate measurement strategies.
Solution Approach 2:
The patent introduces the dimension of symmetry analysis by characterizing process variations in terms of symmetric and asymmetric components. This dimensional transformation allows the metrology system to capture information about process variations that traditional single-dimension measurements miss, thereby improving measurement accuracy and robustness simultaneously.
2Measurement precision
If resonance regions are identified and analyzed for process variation estimation, then measurement accuracy improves, but device complexity increases due to additional analysis steps
Solution Approach 1:
The patent performs preliminary identification of resonance regions in the landscape before conducting detailed process variation analysis. By pre-identifying these critical regions, the system focuses computational resources only where needed, improving process variation detection accuracy while avoiding the complexity of analyzing entire parameter spaces uniformly.
Solution Approach 2:
The patent applies different analysis methods to different regions of the landscape, specifically focusing detailed analysis on resonance regions while using coarser analysis elsewhere. This local quality approach improves measurement accuracy in critical areas without proportionally increasing overall device complexity.
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
This approach enables efficient identification and quantification of process variations, improving measurement accuracy and robustness by anticipating recipe breakdowns and guiding target design processes, thus enhancing the reliability of metrology measurements.
Implementation Method 1
identifying, in the derived landscape, at least one resonance region that corresponds to a resonance of optical illumination in a measured location
Data Source
AI summary
Method, metrology modules and RCA tool are provided, which use the behavior of resonance region(s) in measurement landscapes to evaluate and characterize process variation with respect to symmetric and asymmetric factors, and provide root cause analysis of the process variation with respect to process steps. Simulations of modeled stacks with different layer thicknesses and process variation factors may be used to enhance the analysis and provide improved target designs, improved algorithms and correctables for metrology measurements. Specific targets that exhibit sensitive resonance regions may be utilize to enhance the evaluation of process variation.