Inverse OPC for Super-Resolution Lithography Using Level Set Algorithm
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Solution Overview
Problem
The difficulty in applying traditional inverse optical proximity correction (OPC) methods to surface plasmon super-resolution photolithography due to the complexity of obtaining an accurate imaging model, and the high calculation burden of inverse photolithography technologies.
Innovation Solution
A method and system for inverse optical proximity correction based on a level set algorithm, which involves constructing a level set function, performing forward and adjoint simulations, calculating a level set gradient, and iteratively updating the mask data to achieve low-distortion photolithography patterns, reducing the reliance on analytical imaging models and minimizing calculation complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional inverse OPC method based on analytical imaging model is used, then the method is simple to apply, but it cannot accurately model surface plasmon super-resolution photolithography due to the complexity of high spatial frequency evanescent waves
Solution Approach 1:
The patent replaces the analytical imaging model (mathematical approximation system) with a level set algorithm-based numerical simulation system. The level set method uses numerical computations to model the complex interactions of high spatial frequency evanescent waves with the mask pattern, substituting the mechanical/mathematical approximation approach with a computational geometry approach that can handle complex wave interactions more accurately.
Solution Approach 2:
The patent changes the modeling parameters from analytical approximations to numerical simulation parameters. By using the level set algorithm, the method can dynamically adjust simulation parameters to accurately capture the effects of high spatial frequency evanescent waves, transforming the static analytical model into a flexible numerical framework that adapts to complex photolithography conditions.
2Manufacturing precision
If inverse photolithography technology is used to improve pattern fidelity, then the accuracy of photolithography patterns is improved, but the calculation amount and time are greatly increased
Solution Approach 1:
The patent segments the mask optimization problem into discrete level set iterations. Instead of optimizing the entire mask pattern at once, the level set method divides the optimization into successive iterations where the mask boundary is adjusted incrementally based on local errors, reducing the computational burden while maintaining pattern fidelity.
Solution Approach 2:
The patent introduces dynamic iteration to the mask optimization process. The level set algorithm performs iterative updates to the mask pattern, allowing the system to adaptively refine the mask design through multiple cycles of simulation and correction. This dynamic approach enables progressive improvement of pattern accuracy without requiring a single computationally expensive optimization step.
Data Source
AI summary
Method for inverse optical proximity correction of super-resolution lithography based on level set algorithm is provided, including: obtaining first mask data according to a target pattern, and constructing a level set function; performing forward simulation, so as to obtain an electric field distribution on a photoresist and a first structural vector electric field distribution on a mask; obtaining a photoresist pattern according to the electric field distribution on the photoresist, and calculating an imaging error between the photoresist pattern and the target pattern; performing accompanying simulation, so as to obtain a second structural vector electric field distribution; obtaining a level set gradient by means of performing calculation according to the first structural vector electric field distribution, the second structural vector electric field distribution and the imaging error; and evolving the level set function, performing update to obtain second mask data, and performing iterative calculation until mask data meeting a preset condition is obtained.