Lithography Pattern Matching with Flexible Search Conditions
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Solution Overview
Problem
Current pattern recognition and search methods in lithography are inefficient, particularly when dealing with large numbers of patterns, as they are overly restrictive and slow, limiting their ability to flexibly match fuzzy patterns and handle topological differences, leading to increased computational resources and time.
Innovation Solution
A method that involves defining flexible search conditions using relative positions and tolerance limits between features, allowing for dynamic adjustments of bounding boxes and inclusion of extra features, enabling efficient pattern matching and identification of patterns with extra features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current pattern recognition and search methods are used, then pattern matching can be performed, but computational time and resources increase significantly due to overly restrictive conditions
Solution Approach 1:
The patent segments the pattern matching process into two distinct phases: (1) a fast preliminary phase that identifies candidate patterns using relaxed search conditions, and (2) a subsequent verification phase that applies strict matching criteria only to these candidates. This segmentation eliminates the need to apply restrictive conditions to all patterns, dramatically reducing computational time while maintaining matching accuracy.
Solution Approach 2:
The patent applies partial action by implementing flexible search conditions that allow patterns with extra features or topological differences to be identified as candidates. Instead of requiring exact matches from the outset, the system performs partial matching with tolerance for deviations, then verifies exact matches only for promising candidates, thereby reducing overall computational burden.
2Adaptability or versatility
If flexible pattern matching is implemented to handle topological differences, then adaptability improves, but computational complexity increases
Solution Approach 1:
The patent implements dynamic search conditions that adapt based on the matching phase. During the preliminary candidate identification phase, the system uses dynamic, flexible conditions that tolerate extra features and topological variations. In the verification phase, conditions become static and strict. This dynamic adjustment of criteria provides adaptability without permanently increasing algorithmic complexity.
Solution Approach 2:
The patent performs preliminary filtering using flexible conditions to identify candidate patterns before applying complex verification algorithms. This preliminary action with relaxed criteria reduces the dataset size that subsequently requires complex processing, thereby achieving adaptability while controlling overall computational complexity.
3Measurement precision
If strict search conditions are applied to all patterns, then measurement precision is maintained, but productivity decreases
Solution Approach 1:
The patent extracts and applies strict search conditions only to the subset of candidate patterns identified in the preliminary phase, rather than applying them to all patterns in the dataset. This extraction approach maintains measurement precision for final matches while dramatically improving productivity by limiting complex verification to a small fraction of total patterns.
Solution Approach 2:
The patent applies different quality standards to different stages of the search process. Local quality is maintained during preliminary screening with flexible conditions, while global precision is ensured through strict verification of final candidates. This localized application of strict conditions optimizes both accuracy and efficiency.
Data Source
AI summary
A method of determining candidate patterns from a set of patterns of a patterning process. The method includes obtaining (i) a set of patterns of a patterning process, (ii) a search pattern having a first feature and a second feature, and (iii) a search condition comprising a relative position between the first feature and the second feature of the search pattern; and determining a set of candidate patterns from the set of patterns that satisfies the search condition associated with the first feature and the second feature of the search pattern.