Lithographic Mask Equi-Brightness Optimization
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Solution Overview
Problem
Current methods for mask optimization in projection nanolithography imaging systems fail to achieve uniform intensity distribution across bright and dark regions, leading to suboptimal performance in semiconductor processing.
Innovation Solution
The system and method involve generating joint eigenvectors and natural sampling points to optimize the mask design, ensuring a uniform intensity distribution by selecting a predetermined number of joint eigenvectors and adjusting thresholds to achieve equi-brightness optimization, which involves projecting bright and dark patterns on a substrate and transforming the mask diffraction order space to a joint eigenvector space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional mask optimization methods are used, then the mask design process is simpler, but the intensity distribution across bright and dark regions is non-uniform
Solution Approach 1:
The patent transforms the mask optimization problem from diffraction order space to joint eigenvector space, changing the parameter representation. This transformation enables equi-brightness optimization by working with eigenvectors that naturally provide uniform intensity distribution across bright and dark regions, resolving the contradiction between intensity uniformity and optimization complexity
Solution Approach 2:
The patent introduces joint eigenvectors as an intermediary representation between the mask design and the intensity distribution. These eigenvectors serve as a mediator that connects the mask aperture function to the desired equi-brightness pattern, allowing optimization in a transformed space that naturally achieves uniform intensity
2Manufacturing precision
If conventional optimization methods are used, then the computing process is faster, but the spatial coverage and image contrast are suboptimal
Solution Approach 1:
By changing the optimization parameters from diffraction orders to joint eigenvectors, the patent achieves better spatial coverage and image contrast. The eigenvector-based approach naturally distributes intensity uniformly across the pattern space, improving spatial coverage without requiring excessive computing iterations
3Manufacturing precision
If conventional mask designs are used, then the fabrication process is simpler, but the image contrast and intensity uniformity are poor
Solution Approach 1:
The patent replaces the conventional mechanical/fabricsation-simplified mask design approach with an optical/mathematical optimization approach. By using joint eigenvector transformation and equi-brightness optimization, the system achieves superior image contrast and intensity uniformity, substituting mathematical optimization for traditional fabrication-simplified designs
Data Source
AI summary
A structure and a method for an equi-brightness optimization. The method may include projecting a plurality of bright patterns having a plurality of bright points and a plurality of dark patterns having a plurality of dark points on a substrate, generating a plurality of joint eigenvectors of the plurality of bright points and a plurality of dark points, selecting a predetermined number of joint eigenvectors to project the plurality of bright patterns, generating a plurality of natural sampling points from the plurality of bright points, wherein the plurality of natural sampling points has a substantially equal intensity, and obtaining a representation of an aperture from the plurality of natural sampling points, wherein an image of the representation of the aperture has a substantially uniform intensity.