Optical Metrology Influence Mapping for Unknown Sample Sections
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Solution Overview
Problem
Conventional optical metrology techniques struggle with measuring samples that include unknown and non-periodic sections, as they require preliminary structural information and periodicity, making it difficult to build accurate models for such structures.
Innovation Solution
The method involves generating an influence map of deviation contributions from unknown sections by decoupling them from known base contributions, allowing for the removal of undesired influences and enabling the modeling of structure-of-interest parameters using a rigorous model for known sections and an effective model for unknown sections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional optical metrology techniques are used to measure samples with unknown sections, then measurement can be performed, but measurement precision deteriorates due to inability to model unknown structures
Solution Approach 1:
The patent segments the sample into two distinct parts: a structure-of-interest (SOI) with known parameters that can be rigorously modeled, and an unknown section that contributes only base and deviation signals. This segmentation allows the measurement system to focus modeling efforts on the SOI while treating the unknown section as a source of interference that can be characterized and removed through influence mapping, thereby resolving the contradiction between measurement precision and modeling complexity.
Solution Approach 2:
The patent extracts the contribution from the unknown section by generating an influence map that separates base signals (systematic contributions) from deviation signals (location-specific variations). This extraction process removes the confounding effects of the unknown section from the measured data, allowing accurate parameter determination for the SOI without requiring a complete model of the entire sample structure.
2Reliability
If preliminary structural information is required for modeling, then model accuracy improves, but adaptability to samples with unknown sections deteriorates
Solution Approach 1:
The patent applies local quality by requiring preliminary structural information only for the structure-of-interest (SOI) while treating the unknown section differently. The influence map captures the local characteristics of the unknown section at each measurement location through base and deviation signals, allowing the system to adapt to samples with unknown sections without compromising model accuracy for the SOI.
Solution Approach 2:
The patent creates a simplified representation (influence map) of the unknown section's contribution rather than attempting to model its complete structure. This copy consists of base signals and deviation signals that capture the essential effects of the unknown section on measurements, enabling the system to handle diverse unknown structures without requiring detailed structural information about each one.
3Measurement precision
If rigorous modeling of entire sample is attempted, then measurement precision improves, but device complexity and computational requirements worsen
Solution Approach 1:
The patent extracts and separates the contribution from the unknown section into base signals and deviation signals through influence mapping. This extraction allows the rigorous modeling to be applied only to the structure-of-interest, while the unknown section's effects are handled through the pre-computed influence map, significantly reducing computational complexity while maintaining measurement precision.
Solution Approach 2:
The patent performs preliminary characterization of the unknown section by generating the influence map before actual measurements. This preliminary action captures the base and deviation signals that represent the unknown section's contribution, allowing subsequent measurements to be corrected efficiently without requiring real-time rigorous modeling of the entire sample, thus reducing computational complexity.
Data Source
AI summary
Optical measurement of a sample that includes a structure-of-interest (SOI) optically coupled to a section having an unknown structure is optically measured using an influence map of the deviation contribution from the unknown structure. The influence map is generated by obtaining metrology data for a plurality of locations that include the SOI and unknown structure and determining the deviation contribution at each location by decoupling the deviation contribution from base contributions from the SOI and unknown structure. During measurement of a location, the deviation contribution associated with that location may be obtained from the influence map and removed from the measured data. The processed data may be fit with a model that includes a rigorous model for the SOI and an effective model for the base contribution of the unknown structure to determine one or more parameters of the SOI.