Photo Mask Evaluation via Inter-Pattern Distance Grouping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The advancement in scale reduction and complexity of mask patterns makes it difficult to accurately evaluate photo masks, leading to acceptable products being regarded as unacceptable.
Innovation Solution
A method involving the measurement of pattern dimensions and inter-pattern distances on photo masks, grouping dimensional differences based on these distances, and calculating evaluation values to determine the acceptability of the masks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional photo mask evaluation methods are used, then the evaluation process is simple, but the measurement precision deteriorates due to scale reduction and pattern complexity
Solution Approach 1:
The patent segments the photo mask pattern into multiple measurement regions (first, second, third regions) with different inter-pattern distance characteristics. Each region is evaluated separately with region-specific evaluation values, allowing precise local evaluation while managing overall complexity through structured division of the evaluation space.
Solution Approach 2:
The patent applies local quality by setting different evaluation criteria for different regions based on their inter-pattern distance characteristics. Regions with small inter-pattern distances use one evaluation standard, while regions with large inter-pattern distances use another, making the evaluation method adapted to local pattern characteristics and improving overall measurement precision.
2Measurement precision
If uniform evaluation criteria are applied to all pattern portions, then the evaluation method is simple, but the measurement precision deteriorates due to variations in inter-pattern distances
Solution Approach 1:
The patent divides the photo mask into multiple measurement regions based on inter-pattern distance characteristics and applies different evaluation criteria to each region. This local quality approach ensures that each region is evaluated with appropriate standards, improving measurement precision while maintaining manageable complexity through systematic regional classification.
Solution Approach 2:
The patent changes the evaluation parameters (evaluation values and criteria) based on the inter-pattern distance parameters of different regions. By adjusting evaluation thresholds and standards according to local geometric parameters, the method achieves higher precision without requiring completely complex evaluation systems.
3Measurement precision
If multiple measurement regions with different evaluation criteria are used, then the measurement precision improves, but the ease of operation deteriorates
Solution Approach 1:
The patent segments the evaluation process into distinct measurement regions with clear boundaries and characteristics. This segmentation, while improving precision, is designed with systematic rules for region identification and evaluation criterion selection, which helps maintain operational simplicity through structured procedures.
Solution Approach 2:
The patent automatically adjusts evaluation parameters based on measured inter-pattern distances, reducing manual intervention. The system changes evaluation criteria based on objective geometric parameters, which improves precision while minimizing the operational complexity that would arise from manual parameter selection.
Data Source
AI summary
A method of evaluating a photo mask, includes measuring each dimension of a plurality of pattern portions of a mask pattern formed on a photo mask, obtaining an inter-pattern distance between the pattern portion and a pattern different from the pattern portion with respect to each of the pattern portions, obtaining a dimensional difference between the measured dimension of the pattern portion and a target dimension of the pattern portion with respect to each of the pattern portions, grouping the dimensional difference obtained for each pattern portion into a plurality of groups in accordance with the inter-pattern distance obtained for each pattern portion, obtaining an evaluation value based on the dimensional difference in each group with respect to each of the groups, and evaluating the photo mask based on the evaluation value.