Mask Topology Optimization for Surface Plasmon Lithography
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Solution Overview
Problem
Current surface plasmon near-field lithography technologies face challenges with poor contrast, fidelity, and non-uniformity in image transfer, particularly due to the optical proximity effect, stray light effect, and mask shadow effect, which are difficult to compensate for using conventional optical proximity-effect correction methods.
Innovation Solution
A mask topology optimization method that involves fuzzy processing and projection processing of initial mask data, followed by forward and adjoint calculations to determine imaging errors and update the mask data iteratively, using finite-difference time-domain algorithms to achieve optimized mask patterns that improve imaging fidelity and yield.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional optical proximity-effect correction (OPC) methods are used to compensate for image distortion, then manufacturing process simplicity is maintained, but imaging fidelity and resolution are insufficient due to nonlinear imaging model and photoresist effects in surface plasmon near-field lithography
Solution Approach 1:
The patent replaces conventional mechanical/optical OPC methods with a computational adjoint optimization system. The adjoint algorithm computes gradients of imaging error with respect to mask parameters, enabling precise compensation for nonlinear optical proximity effects, stray light effects, and mask shadow effects through iterative optimization rather than conventional rule-based correction.
Solution Approach 2:
The patent changes mask parameters (transparency, thickness, material composition) based on computed adjoint gradients to optimize imaging fidelity. The optimization process iteratively adjusts these parameters to minimize imaging error, achieving superior resolution and contrast compared to conventional OPC methods.
2Manufacturing precision
If pixel-based reverse lithography technology is used to increase degree of freedom in optimization, then imaging fidelity can be improved, but computational complexity increases significantly and running time of pattern optimization is extended
Solution Approach 1:
The patent replaces brute-force pixel-based optimization with an adjoint-based gradient optimization system. The adjoint algorithm efficiently computes sensitivities of imaging error to all mask parameters simultaneously, reducing computational complexity from O(N²) to O(N) where N is the number of mask parameters, thereby significantly reducing optimization time while maintaining high precision.
Solution Approach 2:
The patent focuses optimization on the most critical mask parameters that have the greatest impact on imaging fidelity, rather than optimizing all possible parameters equally. This selective approach reduces computational burden while achieving the necessary optimization precision for high-quality patterning.
3Productivity
If mask patterns are designed with high density integration, then productivity and throughput are improved, but image contrast and uniformity deteriorate due to optical proximity effect, stray light effect, and mask shadow effect
Solution Approach 1:
The patent applies preliminary compensation for optical proximity effects, stray light effects, and mask shadow effects during the mask design phase using adjoint optimization. By pre-correcting these adverse effects in the mask pattern design, the lithography process achieves high contrast and uniformity even when printing high-density integrated circuit patterns, thereby maintaining both productivity and image quality.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method effectively compensates for distortion and improves imaging fidelity and product yield by adjusting mask topology, ensuring manufacturability and reducing edge jaggy issues in pixelated mask patterns, thus enhancing the resolution and fidelity of surface plasmon near-field photolithography.
Implementation Method 1
surface plasmon near-field imaging lithography has the advantages of high throughput, high resolution, high aspect ratio, high fidelity
Implementation Method 2
solving by using a finite-difference time-domain algorithm to obtain the imaging data and the forward field data
Data Source
AI summary
A mask topology optimization method for surface plasmon near-field photolithography, including: acquiring first mask data and performing fuzzy processing and projection processing on same to obtain second mask data; performing forward calculation according to the second mask data and a preset surface plasmon near-field photolithography condition to obtain imaging data and forward field data; calculating an imaging error between the imaging data and expected imaging data; performing adjoint calculation on the second mask data to obtain adjoint field data; calculating a gradient matrix of the imaging error relative to the first mask data according to the forward field data and the adjoint field data; and updating the first mask data according to the gradient matrix, repeating the steps for iteration calculation until the optimized mask data is obtained, and outputting a final mask pattern.