Adaptive Overlay Sampling for Wafer Process Variation Control
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Solution Overview
Problem
Conventional overlay control schemes in semiconductor manufacturing face inefficiencies due to static sampling techniques that fail to adapt to lot-to-lot and wafer-to-wafer variances, leading to over or under sampling, increased computational load, and high costs without significant accuracy improvements.
Innovation Solution
A system and method for multi-merit adaptive sampling that dynamically adjusts sampling points based on calculated figure of merit metrics, increasing points in regions with high process variation and decreasing points in regions with low variation, thereby optimizing sampling efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static sampling techniques are used, then the sampling rules remain constant throughout the product process, but it leads to over sampling and under sampling, impacting the effectiveness of metrology resources and increasing cost of ownership
Solution Approach 1:
The patent implements dynamic sampling by adjusting sampling rules based on real-time process conditions. The system calculates figure of merit metrics from metrology data and uses these to dynamically determine sampling rates, transitioning from static fixed rules to adaptive sampling that responds to actual process variation levels.
Solution Approach 2:
The system establishes a feedback loop where metrology data is continuously collected, figure of merit metrics are calculated, and sampling rules are adjusted based on these metrics. This closed-loop approach allows the system to learn from process variations and optimize sampling strategies accordingly.
2Measurement precision
If dense sampling techniques are used, then more detailed information about the wafer's features and variations is provided, but the computational load and correction complexity during the alignment process is increased
Solution Approach 1:
The patent applies local quality by determining different sampling rates for different regions or conditions. Instead of uniformly dense sampling across all wafers, the system identifies regions with high process variation and applies denser sampling only where needed, while using reduced sampling in stable regions.
Solution Approach 2:
The system changes sampling parameters dynamically based on calculated figure of merit metrics. When metrics indicate high process variation, the sampling rate is increased; when metrics show stable processes, the sampling rate is reduced, optimizing the balance between measurement precision and computational load.
3Ease of manufacture
If static sampling is used, then implementation is simple, but it does not detect process excursions as quickly as they occur
Solution Approach 1:
The system transitions from static to dynamic sampling rules that automatically adjust to process conditions. This enables rapid detection of process excursions by increasing sampling intensity when variations are detected, while maintaining simplicity through automated rule-based adjustments rather than complex manual intervention.
Data Source
AI summary
A method for multi-merit adaptive sampling may include receiving an initial sampling map and a first set of metrology data. The method may further include calculating figure of merit metrics. The method may include ranking each initial sampling point in the initial sampling map based on the figure of merit metrics and levels of process variation. The method may include generating an adjusted sampling plan for at least one of a second sample in the first lot or a future process layer of the first sample based on the rank of each initial sampling point. An adjusted set of sampling points may be generated by increasing a number of sampling points in a first sample region associated with a first level of process variation and decreasing a number of sampling points in a second sample region associated with a second level of process variation.