Metrology Simulation for Target Sensitivity Analysis
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Solution Overview
Problem
Metrology in semiconductor manufacturing faces challenges in efficiently evaluating parameter settings and performing root cause analysis due to the sensitivity of target design, production processes, and metrology measurements to multiple parameters, making it difficult to predict and optimize performance effectively.
Innovation Solution
A system comprising an input module for receiving parameters, a metrology simulation unit for simulating measurements and generating metrics, a sensitivity analysis module for deriving functional dependencies and uncertainties, and a target optimization module for ranking targets and conditions based on simulated measurements, utilizing computer processors to analyze and optimize target geometry and metrology configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If metrology simulations are used to predict performance and evaluate dependence on multiple input parameters, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system segments the complex sensitivity analysis into distinct functional modules: an input module for receiving parameters, a metrology simulation unit for performing simulations, a sensitivity analysis module for deriving functional dependencies, and a target optimization module for ranking targets. This modular segmentation allows each component to handle specific aspects of the analysis independently, improving measurement precision while managing system complexity through organized functional decomposition.
2Manufacturing precision
If sensitivity analysis is performed on multiple parameters to evaluate target design and process settings, then manufacturing precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary sensitivity analysis by deriving functional dependencies of metrology measurements on input parameters before actual manufacturing decisions are made. The sensitivity analysis module calculates derivatives and uncertainty propagation in advance, allowing manufacturers to pre-evaluate the impact of parameter variations on measurement outcomes. This preliminary action enables faster, more informed decision-making during actual manufacturing processes, improving precision without excessive time loss during production.
3Reliability
If comprehensive parameter analysis is conducted to enable root cause analysis of metrology excursions, then reliability is improved, but device complexity increases
Solution Approach 1:
The system implements feedback mechanisms where the sensitivity analysis module continuously monitors the relationship between input parameters and metrology measurements. By calculating functional dependencies and uncertainties, the system provides feedback on which parameters most significantly affect measurement outcomes. This feedback enables targeted root cause analysis of metrology excursions by identifying sensitive parameters that require closer monitoring, improving reliability while managing complexity through focused analysis on critical parameters rather than exhaustive examination of all parameters.
Data Source
AI summary
Systems and method are provided for analyzing target, process and metrology configuration sensitivities to a wide range of parameters, according to external requirements or inner development and verification needs. Systems comprise the following elements. An input module is arranged to receive parameters relating to targets, target metrology conditions and production processes, to generate target data. A metrology simulation unit is arranged to simulate metrology measurements of targets from the target data and to generate multiple metrics that quantify the simulated target measurements. A sensitivity analysis module is arranged to derive functional dependencies of the metrics on the parameters and to define required uncertainties of the parameters with respect to the derived functional dependencies. Finally, a target optimization module is arranged to rank targets and target metrology conditions with respect to the simulated target measurements.