Lithography Metrology Sampling via Statistical Wafer Grouping
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Solution Overview
Problem
Current advanced process control systems in lithographic manufacturing face challenges in accurately controlling industrial processes due to varying contexts among product units, leading to increased metrology overhead and reduced performance, especially in multi-threaded processes where representative sample wafers are not consistently selected.
Innovation Solution
A method that selects sample product units for metrology based on statistical analysis of object data, allowing for more reliable performance improvements by grouping product units and selecting representative wafers per group, thereby reducing noise and contamination in control systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sample wafers are selected without statistical analysis, then metrology overhead is reduced, but control accuracy deteriorates due to noise and contamination
Solution Approach 1:
The system performs preliminary statistical analysis of object data before selecting sample wafers for metrology. This advance preparation identifies representative wafers and groups them by context, ensuring that subsequent metrology measurements are conducted on appropriate samples without requiring excessive analysis or measurements later in the process.
Solution Approach 2:
The system changes the selection criteria for sample wafers from random or simple sequential selection to statistically-based selection using object data parameters. By analyzing parameters such as overlay measurements, wafer characteristics, and process conditions, the system identifies wafers that best represent their respective groups, thereby improving measurement accuracy while maintaining efficient metrology overhead.
2Reliability
If context-based threading is implemented, then control performance is improved, but device complexity increases due to multiple threads
Solution Approach 1:
The system applies different control strategies to different groups of wafers based on their specific context characteristics. Instead of using a single uniform control approach for all wafers, the system identifies distinct context groups (such as different product types, process conditions, or tool configurations) and creates separate threads for each group, allowing optimized control performance for each local context while managing overall system complexity.
3Measurement precision
If representative wafers are selected per group, then noise in control system is reduced, but selection complexity increases
Solution Approach 1:
The system creates simplified representations or models of wafer groups based on statistical analysis of object data. Instead of manually selecting representative wafers through complex evaluation, the system uses algorithms to identify and select wafers that best represent their groups, effectively copying the essential characteristics of each group into a manageable set of sample wafers that can be used for control purposes.
Data Source
AI summary
A lithographic process is performed on a plurality of semiconductor substrates. The method includes selecting one or more of the substrates as one or more sample substrates. Metrology steps are performed only on the selected one or more sample substrates. Based on metrology results of the selected one or more sample substrates, corrections are defined for use in controlling processing of the substrates or of future substrates. The selection of the one or more sample substrates is based at least partly on statistical analysis of object data measured in relation to the substrates. The same object data or other data can be used for grouping substrates into groups. Selecting of one or more sample substrates can include selecting substrates that are identified by the statistical analysis as most representative of the substrates in their group and/or include elimination of one or more substrates that are identified as unrepresentative.