Composite Wafer Metrics for Faster Die Failure Root Cause Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current semiconductor device fabrication processes face challenges in accurately identifying the root cause of die failures due to limited correlation between patterned wafer geometry metrics and wafer characterization data, leading to increased turnaround time and potential multi-step process issues.
Innovation Solution
A system and method for auto-correlation of wafer characterization data and generation of composite wafer metrics, utilizing a controller with an auto-correlation module to receive patterned wafer geometry metrics, determine correlations, generate rankings, construct composite metric models, and produce statistical process control outputs, thereby enhancing the correlation between geometry features and die failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single geometry metric is selected to correlate with die failure, then the analysis complexity is reduced, but the correlation level is limited (less than 70 percent) and root cause identification accuracy deteriorates
Solution Approach 1:
The patent combines multiple individual geometry metrics into a single composite geometry metric that captures the cumulative effect of multiple geometric features on die failure. This composite metric merges information from multiple sources (different geometry metrics, process parameters) to achieve higher correlation with die failure than any single metric could provide alone, resolving the contradiction between analysis simplicity and correlation accuracy.
Solution Approach 2:
The patent creates a composite geometry metric analogous to composite materials, where multiple individual metric components are combined to form a new metric with superior correlation properties. The composite metric integrates multiple geometric features and process parameters to achieve a correlation level exceeding 70 percent with die failure, while maintaining the simplicity of a single metric for analysis and decision-making.
2Measurement precision
If multiple geometry metrics are individually checked to improve root cause identification, then the correlation accuracy improves, but the turnaround time increases to several days or weeks
Solution Approach 1:
The patent performs preliminary actions by pre-calculating and storing the relationships between multiple geometry metrics and die failure data in a database. When analysis is needed, the system quickly retrieves pre-computed composite metrics and their correlations, avoiding the need to perform time-consuming individual metric analyses at the time of root cause identification. This preliminary preparation reduces turnaround time from days/weeks to much faster intervals.
Solution Approach 2:
The patent creates a simplified copy or representation of the complex multi-metric analysis in the form of a composite geometry metric. This composite metric serves as a condensed model that encapsulates the essential information from multiple individual metrics, allowing rapid assessment of root cause likelihood without requiring detailed analysis of each individual metric, thus reducing turnaround time while maintaining accuracy.
3Device complexity
If a single geometry metric is used to represent multiple wafer geometry frequencies, then the analysis is simplified, but the correlation may not hold across all frequencies reducing reliability
Solution Approach 1:
The patent applies local quality by incorporating frequency-specific information into the composite geometry metric. Rather than using a single generic metric for all frequencies, the composite metric is constructed to account for the specific geometric features and their frequency dependencies at different locations in the wafer. This allows the metric to maintain high correlation with die failure across multiple frequencies while preserving the simplicity of a unified analysis approach.
Data Source
AI summary
A system includes a controller with processors configured to execute an auto-correlation module embodied in one or more sets of program instructions stored in memory. The auto-correlation module is configured to cause the processors to receive one or more patterned wafer geometry metrics, receive wafer characterization data from one or more characterization tools, determine a correlation between the one or more patterned wafer geometry metrics and the wafer characterization data, generate a ranking of the one or more patterned wafer geometry metrics based on the determined correlation, construct a composite metric model from a subset of the one or more patterned wafer geometry metrics based on the ranking of the one or more patterned wafer geometry metrics, generate one or more composite wafer metrics from the composite metric model, and generate a statistical process control output based on the one or more composite wafer metrics.