Adaptive Guardbands for Substrate Processing Yield Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional substrate processing systems face issues with false positives and missed positives due to narrow or broad guardbands, leading to material waste, decreased yield, and increased downtime, as they incorrectly label substrates as meeting or not meeting property values.
Innovation Solution
Implementing guardband improvements by analyzing trace data to determine dynamic and adaptive guardbands, allowing for wider limits at certain portions and narrower limits where necessary, and identifying guardband violation data points for corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If narrow guardbands are used to improve manufacturing precision, then substrate property values meet threshold values more accurately, but false positives increase leading to material waste and decreased yield
Solution Approach 1:
The patent implements dynamic guardbands that adapt based on trace data analysis and violation shape characterization. Instead of using fixed narrow guardbands that cause false positives, the system dynamically adjusts guardband parameters based on learned patterns from historical data, reducing false positives while maintaining manufacturing precision for actual violations.
Solution Approach 2:
The system changes guardband parameters dynamically based on the classification of violation shapes. Different violation shapes receive different guardband adjustments, allowing the system to maintain high precision for critical violations while being more tolerant of benign variations, thereby reducing material waste from false positives.
2Loss of substance
If broad guardbands are used to reduce false positives, then material waste decreases, but missed positives increase leading to decreased yield
Solution Approach 1:
The patent segments the guardband application by classifying violation shapes into different categories. Each category receives tailored guardband treatment, allowing the system to use broader guardbands for benign patterns (reducing false positives and material waste) while maintaining stricter monitoring for critical patterns (preventing missed positives and protecting yield).
Solution Approach 2:
Different regions of the parameter space receive different guardband strictness based on violation shape classification. Critical regions maintain narrow effective guardbands to prevent missed positives, while benign regions use broader guardbands to reduce false positives and material waste, optimizing both yield and material efficiency.
3Device complexity
If fixed guardbands are used to simplify the system, then device complexity is reduced, but false positives and missed positives increase due to inability to adapt
Solution Approach 1:
The guardband system performs self-adjustment through automated trace data analysis and violation shape characterization. The system learns from historical data and automatically adapts guardband parameters without requiring manual intervention or complex external control systems, achieving high reliability while keeping the overall system architecture relatively simple.
Solution Approach 2:
The system implements feedback loops where trace data from substrate processing is continuously analyzed, violation shapes are characterized, and guardband parameters are adjusted based on this feedback. This automated feedback mechanism enables the system to adapt to changing conditions and maintain high classification accuracy without increasing operational complexity.
4Productivity
If dynamic and adaptive guardbands are implemented to reduce false positives and missed positives, then yield and material efficiency improve, but device complexity increases
Solution Approach 1:
The system performs preliminary analysis of trace data to establish baseline violation shapes and characteristics before applying dynamic guardband adjustments. This preparatory work allows the system to implement adaptive guardbands more efficiently, reducing the computational complexity during real-time operation while maintaining high yield through accurate classification.
Data Source
AI summary
A method includes identifying trace data including a plurality of data points, the trace data being associated with production, via a substrate processing system, of substrates having property values that meet threshold values. The method further includes determining, based on a guardband, guardband violation data points of the plurality of data points of the trace data. The method further includes determining, based on the guardband violation data points, guardband violation shape characterization. Classification of additional guardband violation data points of additional trace data is to be based on the guardband violation shape characterization. Performance of a corrective action associated with the substrate processing system is based on the classification.