Lithography Measurement Location Selection for Process Variation Detection
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Solution Overview
Problem
Existing patterning processes face challenges in detecting process variations that affect yield, leading to substantial product defects and increased cycle time due to inefficient metrology regimens.
Innovation Solution
Intelligently and dynamically selecting measurement locations based on computational lithography and process variation analysis, using a process condition map and simulation models to identify potential yield issues and optimize metrology resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If measurements are taken at all locations to ensure comprehensive quality control, then measurement precision and reliability improve, but measurement time and productivity decrease
Solution Approach 1:
The patent applies local quality by identifying and measuring only specific critical locations on the substrate rather than uniform measurements across all areas. The system determines measurement locations based on process condition maps that highlight regions most susceptible to process variations, thereby concentrating measurement resources where they provide maximum value for detecting process anomalies while minimizing overall measurement time.
Solution Approach 2:
The patent implements preliminary action by using process condition maps and simulation models to predict which locations are most likely to exhibit process variations before measurements are taken. This advance identification of critical measurement locations allows the system to focus measurements on high-risk areas, improving detection capability while reducing the number of measurements required and thus decreasing cycle time.
2Reliability
If comprehensive metrology regimens are implemented to detect process variations, then product quality and yield improve, but measurement complexity and resource allocation difficulty increase
Solution Approach 1:
The patent applies dynamics by making the measurement location selection adaptive and dynamic rather than static. The system uses process condition maps that capture spatial variations in process conditions and dynamically determines measurement locations based on these conditions. This dynamic approach allows the metrology regimen to adapt to changing process states, improving yield detection capability while simplifying resource allocation through automated, condition-based location selection.
Solution Approach 2:
The patent introduces an intermediary element in the form of process condition maps and simulation models that mediate between the complex process variations and the measurement system. These intermediaries translate complex process data into actionable measurement location recommendations, simplifying the metrology regimen by providing a clear framework for selecting critical measurement points without requiring direct analysis of all process parameters.
3Productivity
If measurements are delayed until later in the process to reduce rework, then productivity improves, but the amount of work-in-progress at risk increases
Solution Approach 1:
The patent implements preliminary action by using process condition maps and simulation models to predict process outcomes before completing the full manufacturing sequence. By identifying critical measurement locations in advance based on simulated process variations, the system enables early detection of potential defects at optimized measurement locations, allowing for timely corrective actions that reduce rework while limiting work-in-progress exposure.
Solution Approach 2:
The patent applies feedback by using measurement results from critical locations to update process condition maps and adjust subsequent measurements or process parameters. This feedback loop enables the system to learn from actual process variations and refine measurement strategies, improving early defect detection capability while maintaining productivity through targeted measurements that provide actionable feedback for process control.
Data Source
AI summary
A process of selecting a measurement location, the process including: obtaining pattern data describing a pattern to be applied to substrates in a patterning process; obtaining a process characteristic measured during or following processing of a substrate, the process characteristic characterizing the processing of the substrate; determining a simulated result of the patterning process based on the pattern data and the process characteristic; and selecting a measurement location for the substrate based on the simulated result.