Mask Feature Optimization Using Shadow Region Gradient Screening
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Solution Overview
Problem
Current SRAF generation methods are complex, require extensive experimental experience, and are inefficient, especially for new processes where effective assist features cannot be quickly generated.
Innovation Solution
A method that involves acquiring a new contour by moving the edges of the main pattern, setting a shadow region based on this contour, screening the mask gradient field using this shadow region, and generating SRAFs or SRIFs based on the screened gradient field, thereby ensuring a predetermined distance from the main pattern without the need for additional manufacturing checks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rule-based SRAF generation method is used, then SRAF can be generated based on accumulated experimental experience, but the process becomes complex and requires large amount of MRC determinations
Solution Approach 1:
The patent replaces the mechanical/experimental rule-based approach with a model-based computational approach. Instead of relying on accumulated experimental experience and manual rule tables, the system uses optical models and algorithms to automatically generate SRAF, substituting the mechanical process of experimental accumulation with a computational model that directly calculates optimal SRAF parameters.
Solution Approach 2:
The model-based method enables the system to self-determine SRAF parameters without requiring external experimental validation at each step. The optimization model automatically adjusts SRAF size, position, and shape based on the main pattern geometry and lithographic parameters, making the system self-sufficient rather than dependent on external rule tables.
2Productivity
If model-based SRAF generation method is used, then SRAF parameters can be adjusted based on simulated exposure imaging results, but experience is still needed and requires continuous adjustments
Solution Approach 1:
The patent performs preliminary calculation of optimal SRAF parameters using the optimization model before actual lithography production. By pre-determining the optimal SRAF configuration based on the main pattern and lithographic conditions, the system eliminates the need for continuous adjustments and manual experience-based tuning during operation.
Solution Approach 2:
The system uses simulated exposure imaging results as feedback to the optimization model, which automatically adjusts SRAF parameters to achieve optimal imaging outcomes. This closed-loop feedback mechanism replaces manual experience-based adjustments with automated model-driven optimization.
3Manufacturing precision
If SRAF is added to improve mask optimization effect, then process windows and depth of focus uniformity are enhanced, but the generation process requires extensive experimental experience and is time-consuming
Solution Approach 1:
The patent replaces time-consuming experimental trial-and-error with a computational optimization model that directly calculates optimal SRAF parameters. This substitution of mechanical experimentation with computational modeling dramatically reduces the time required to achieve high-quality mask optimization results.
Solution Approach 2:
The optimization model systematically varies SRAF parameters (size, position, shape) to find the optimal configuration that maximizes process windows and depth of focus uniformity. By automating parameter optimization through computational methods, the system achieves high manufacturing precision without the time penalty of manual experimentation.
Data Source
AI summary
The disclosure provides a method and system for mask feature optimization and belongs to the field of computational lithography. The method includes: acquiring a new contour after the edges of the main pattern of a mask are moved inward or outward by a predetermined distance; setting a region inside the new contour as the shadow region when the movement is an inward movement and setting a region outside the new contour as the shadow region when the movement is an outward movement; screening an acquired mask gradient field solved by inverse lithography based on the shadow region to keep only the mask gradient field inside the region; and generating a sub resolution assist/inverse feature of the mask based on the screened mask gradient field.