Metrology Data Fusion via Intermediary Correction Models
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Solution Overview
Problem
Existing hybrid metrology tools face limitations in combining measurement results from disparate tools, leading to measurement errors due to inaccuracies and differences in measurement techniques, requiring robust methods to correct offsets and correlation slope errors.
Innovation Solution
A system and method for combining raw data from multiple metrology tools involve identifying a training component, collecting signals from different tools, transforming them, determining relationships with reference values, and creating training models to accurately measure parametric values for target components, allowing for model-less or model-free measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If measurement results from multiple disparate metrology tools are combined using traditional hybrid metrology techniques, then measurement coverage and potential accuracy are improved, but measurement errors occur due to offsets and correlation slope errors between tools
Solution Approach 1:
The patent transforms raw measurement data from multiple metrology tools through parameter transformations and corrections. It applies offset corrections and correlation slope error corrections to align parameters from different tools, converting disparate measurements into a unified parameter space that maintains both accuracy and consistency across tool boundaries
Solution Approach 2:
The patent introduces an intermediary data transformation layer between raw measurements and final results. This intermediary process includes calibration data, offset corrections, and correlation models that mediate between different tool measurement systems, enabling accurate combination without direct tool-to-tool errors
2Ease of operation
If injection or simple feed forward technique is used to combine metrology results, then the process is simple and fast, but the method is not robust and measurement errors occur
Solution Approach 1:
The patent performs preliminary actions by pre-calibrating tools and pre-computing correction parameters (offsets, correlation slopes) before actual measurement combining. This preliminary setup enables the subsequent combining process to be both simple in execution and robust in results, as the complex corrections are prepared in advance
Solution Approach 2:
The patent implements feedback mechanisms where measurement results are used to update and refine correction parameters. The system continuously adjusts offset corrections and correlation models based on observed measurement discrepancies, maintaining robustness while keeping the operational process simple through automated feedback loops
3Measurement precision
If results data transform technique is used to correct offsets and correlation errors, then measurement accuracy is improved, but the method requires complex corrections and intimate knowledge of measurement algorithms across different tools
Solution Approach 1:
The patent develops a universal data transformation framework that can handle multiple tool types and measurement parameters through a single unified correction approach. The system uses universal correction models (offsets, correlation slopes) that apply across different metrology tools, reducing the need for tool-specific intimate knowledge while maintaining high accuracy
4Reliability
If co-optimization method is used to simultaneously regress models for all tools, then measurement consistency is improved, but the method requires intimate working knowledge of measurement algorithms and calibration methods across competing suppliers' tools
Solution Approach 1:
The patent introduces an intermediary standardized interface layer that enables co-optimization without requiring intimate knowledge of proprietary algorithms. This intermediary layer translates various tool-specific measurement algorithms into a common parameter space, allowing consistent multi-tool regression while shielding users from supplier-specific algorithmic complexities
Data Source
AI summary
A system, method and computer program product are provided for combining raw data from multiple metrology tools. Reference values are obtained for at least one parameter of a training component. Signals are collected for the at least one parameter of the training component, utilizing a first metrology tool and a different second metrology tool. Further, at least a portion the signals are transformed into a set of signals, and for each of the at least one parameter of the training component, a corresponding relationship between the set of signals and the reference values is determined and a corresponding training model is created therefrom. Signals from a target component are collected utilizing at least the first metrology tool and the second metrology tool, and each created training model is applied to the signals collected from the target component to measure parametric values for the target component.