Metrology Tool Automated Optimization Using Synthetic Images
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Solution Overview
Problem
Conventional optimization procedures for metrology tools in semiconductor manufacturing are time-consuming and inconsistent due to manual adjustments, leading to variations in performance across different operators and tools.
Innovation Solution
An automated optimization process for metrology tools that adjusts operating parameters based on predefined specifications, using synthetic images and wafer maps to minimize human intervention and ensure consistent peak performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual optimization procedures are used to adjust metrology tool parameters, then the tool can be optimized for specific targets, but the process becomes time-consuming and inconsistent across different operators
Solution Approach 1:
The system performs self-optimization by automatically adjusting metrology tool parameters based on synthetic images and specifications without requiring manual operator intervention. The automated optimization process eliminates human involvement while maintaining consistent, high-quality results across different tools and operators.
Solution Approach 2:
The system uses synthetic images that replicate real target characteristics to perform virtual optimization before actual measurement. This copying approach allows the system to learn optimal parameters from simulated data, eliminating the need for time-consuming manual optimization on actual targets while ensuring consistent results.
2Adaptability or versatility
If manual optimization is performed by different operators, then individual expertise can be applied, but the results vary significantly between operators and over time
Solution Approach 1:
The system replaces human operator judgment with automated algorithms that consistently apply optimization criteria. The system serves itself by automatically determining optimal parameters based on synthetic images and specifications, eliminating variability between operators while maintaining adaptability to different target types through programmable optimization criteria.
3Manufacturing precision
If extensive manual adjustment of operating parameters is performed to achieve optimum image characteristics, then measurement quality improves, but the setup time increases significantly
Solution Approach 1:
The system performs optimization in advance using synthetic images that represent real target characteristics. By pre-determining optimal parameters through virtual optimization before actual measurement, the system eliminates the need for time-consuming manual adjustments on the production floor while ensuring high image quality and measurement precision.
Solution Approach 2:
The system creates and uses synthetic copies of real targets to perform optimization virtually. This copying enables the system to determine optimal imaging parameters before actual measurement, significantly reducing setup time while maintaining the same image quality that would result from extensive manual optimization.
Data Source
AI summary
Disclosed are apparatus and methods for optimizing a metrology tool, such as an optical or scanning electron microscope so that minimum human intervention is achievable during the optimization. In general, a set of specifications and an initial input data are initially provided for a particular target. The specifications provide limits for characteristics of images that are to be measured by the metrology tool. The metrology tool is then automatically optimized for measuring the particular target so as to meet one or more of the provided specifications without further significant human intervention with respect to the metrology tool. In one aspect, the input data provided prior to the automated optimization procedure includes a plurality of target locations and a synthetic image of the particular target.