Pattern Retargeting Bias Rules for Dense Lithography Features
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Solution Overview
Problem
Current rule-based retargeting methods in lithographic projection apparatuses are limited in accuracy and flexibility, failing to account for the complex interactions between neighboring features, leading to inconsistencies and inaccuracies in pattern printing.
Innovation Solution
A method that determines biasing rules based on additional properties such as density and geometry of features within a measurement region, allowing for more precise retargeting by applying specific biases to improve pattern accuracy and consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional rule-based retargeting methods are used, then the processing speed is maintained, but the manufacturing precision and accuracy of pattern printing deteriorate due to inability to account for complex interactions between neighboring features
Solution Approach 1:
The retargeting method is segmented into distinct processing stages: (1) extracting geometric properties of individual features, (2) determining biasing rules based on local density and geometry, and (3) applying selective biases. This segmentation allows the system to maintain rule-based speed while incorporating complex interaction analysis through structured property extraction and rule application.
Solution Approach 2:
The invention adds a new dimension to traditional retargeting by incorporating density-based property analysis alongside geometric dimensions. Instead of relying solely on feature size and spacing, the method evaluates local pattern density and geometric properties, creating a multi-dimensional rule space that captures complex neighbor interactions without requiring full computational modeling.
2Manufacturing precision
If traditional rule-based retargeting methods are used, then the method simplicity and interpretability are maintained, but the manufacturing precision deteriorates due to limited ability to represent complex patterns
Solution Approach 1:
The method introduces new parameters (density, geometric properties) that modify traditional retargeting rules. By changing the parameter space from simple dimensional measurements to include density and geometry metrics, the system achieves higher precision while maintaining rule-based interpretability through structured parameter evaluation and rule application.
Solution Approach 2:
Geometric properties and density metrics serve as intermediaries between the physical pattern structure and the retargeting rules. These intermediate parameters translate complex neighbor interactions into quantifiable metrics that can be evaluated by modified rule-based algorithms, preserving interpretability while capturing sophisticated pattern interactions.
3Manufacturing precision
If additional properties such as density and geometry are incorporated into retargeting, then the manufacturing precision improves, but the computational complexity increases
Solution Approach 1:
Geometric properties and density metrics are calculated in advance during the property extraction phase, before rule application. This preliminary computation of structural characteristics allows the subsequent biasing rules to operate efficiently on pre-processed data, reducing overall computational complexity while maintaining high precision through comprehensive feature analysis.
Data Source
AI summary
A method for generating a retargeted pattern for a target pattern to be printed on a substrate. The method includes obtaining (i) the target pattern comprising at least one feature, the at least one feature having geometry including a first dimension and a second dimension, and (ii) a plurality of biasing rules defined as a function of the first dimension, the second dimension, and a property associated with features of the target pattern within a measurement region; determining values of the property at a plurality of locations on the at least one feature of the target pattern, each location surrounded by the measurement region; selecting, from the plurality of biasing rules based on the values of the property, a sub-set of biases; and generating the retargeted pattern by applying the selected sub-set of biases to the at least one feature of the target pattern.