Patterning Device Pattern Optimization for Lithography
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Solution Overview
Problem
Current lithographic projection technologies face challenges in accurately reproducing small feature sizes and high feature densities due to proximity effects and resolution limitations, leading to defects and process window issues during the manufacturing of integrated circuits and other devices.
Innovation Solution
A method is introduced to determine a patterning device pattern by iteratively modifying feature sizes based on a patterning process model, using a combination of difference values and penalty values related to manufacturability, with image processing techniques such as binarization and convolution to optimize the pattern for reduced edge placement errors and manufacturability constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional lithographic projection technologies are used to transfer patterns, then the patterning process can be completed, but manufacturing precision deteriorates due to proximity effects and resolution limitations
Solution Approach 1:
The patent applies preliminary action by pre-calculating and pre-compensating for proximity effects during the pattern design phase. The system predicts how optical effects will distort the pattern and adjusts the mask design in advance to counteract these distortions, ensuring accurate feature reproduction on the substrate without requiring post-processing corrections.
Solution Approach 2:
The patent implements feedback through iterative optimization processes where the predicted pattern is compared with the desired pattern, and adjustments are made to the mask design based on the difference. This closed-loop approach continuously refines the pattern until manufacturing precision requirements are met, accounting for proximity effects at each iteration.
2Productivity
If feature sizes are reduced to increase device density, then productivity improves, but manufacturing precision deteriorates due to resolution limitations
Solution Approach 1:
The patent applies parameter changes by modifying multiple design parameters simultaneously to compensate for resolution limitations. This includes adjusting feature dimensions, adding assist features, modifying pattern densities, and changing mask materials or coatings to optimize the optical response. These parameter adjustments enable accurate fabrication of smaller features while maintaining manufacturing precision.
Solution Approach 2:
The system performs preliminary calculations to predict how reduced feature sizes will be affected by proximity effects and resolution limitations. By pre-compensating for these effects in the mask design, the patent enables higher device density while maintaining the manufacturing precision required for accurate feature reproduction.
3Productivity
If pattern complexity increases to achieve high feature densities, then productivity improves, but device complexity increases making manufacturability difficult
Solution Approach 1:
The patent applies segmentation by dividing the complex pattern into manageable components and regions. The system processes different areas of the pattern separately, applying optimized corrections for each region based on its specific complexity and proximity effect characteristics. This segmentation reduces the overall device complexity while maintaining high feature density and productivity.
Solution Approach 2:
The patent implements local quality by applying different correction strategies to different regions of the pattern based on their specific requirements. High-density regions receive different processing than low-density regions, with each area optimized for its local characteristics. This approach manages pattern complexity locally while achieving high overall feature density.
4Manufacturing precision
If iterative optimization is applied to reduce edge placement errors, then manufacturing precision improves, but loss of time increases due to multiple process steps
Solution Approach 1:
The patent replaces time-consuming manual iterative optimization with automated computational algorithms. The system uses computer-based prediction models and optimization software to automatically adjust pattern parameters, eliminating the need for repeated physical prototyping and measurement cycles. This substitution dramatically reduces optimization cycle time while maintaining high manufacturing precision.
Solution Approach 2:
The system creates virtual copies of the pattern through computational modeling, allowing iterative optimization to be performed on digital representations rather than physical structures. This virtual copying enables rapid prediction and adjustment of pattern characteristics without the time cost of physical iterations, achieving edge placement accuracy efficiently.
Data Source
AI summary
A method for determining a patterning device pattern. The method includes obtaining (i) an initial patterning device pattern having at least one feature, and (ii) a desired feature size of the at least one feature, obtaining, based on a patterning process model, the initial patterning device pattern and a target pattern for a substrate, a difference value between a predicted pattern of the substrate image by the initial patterning device and the target pattern for the substrate, determining a penalty value related the manufacturability of the at least one feature, wherein the penalty value varies as a function of the size of the at least one feature, and determining the patterning device pattern based on the initial patterning device pattern and the desired feature size such that a sum of the difference value and the penalty value is reduced.