Optical Proximity Correction Model for Non-Right Angles
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Solution Overview
Problem
Existing semiconductor manufacturing technologies face challenges in collecting proximity data for Optical Proximity Correction (OPC) assistant patterns with non-right turning-angles, which are necessary for advanced semiconductor chip design due to the increasing complexity and smaller Critical Dimensions of semiconductor devices.
Innovation Solution
A method involving the creation of test lines with non-right turning-angles, followed by Optical Proximity Correction and simulation or actual exposure to generate formed lines, allowing for the identification and analysis of redundant or missing parts in the assistant lines at turning-angles, thereby collecting necessary proximity data to correct and improve the OPC model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If non-right turning-angles are used in pattern lines to meet advanced semiconductor design requirements, then the adaptability of pattern layout is improved, but the difficulty of collecting and measuring proximity data increases
Solution Approach 1:
The patent applies preliminary action by pre-generating test patterns with various non-right turning-angles and their corresponding assistant patterns before actual production. These test patterns are exposed and measured in advance to build a proximity effect database, which is then used to guide OPC corrections in real manufacturing without needing to measure each complex pattern individually during production.
Solution Approach 2:
The patent uses copying by creating test patterns that replicate the geometric features (non-right turning-angles) of actual device patterns. By measuring the proximity effects on these copied test patterns, the system obtains data that can be applied to correct the original complex patterns without directly measuring them, thus reducing measurement difficulty.
2Manufacturing precision
If assistant patterns are added to correct optical proximity effects, then the manufacturing precision of semiconductor patterns is improved, but the device complexity of the photographic mask increases
Solution Approach 1:
The patent applies local quality by adding assistant patterns only in specific locations where optical proximity effects are problematic, rather than uniformly across the entire mask. The assistant patterns are strategically placed near features with non-right turning-angles where proximity effects are most significant, leaving other areas unchanged and thus minimizing overall mask complexity while improving local precision.
Solution Approach 2:
The patent segments the mask correction problem by treating different geometric features (straight lines, right-angled corners, non-right turning-angles) separately. Each feature type has its own set of proximity effect measurements and corresponding assistant pattern rules, allowing the complex correction task to be divided into manageable segments that can be handled independently.
3Productivity
If the Critical Dimension of semiconductor devices is reduced to increase device integrity, then the functionality and storage capacity are improved, but the optical proximity effects become more significant and harder to control
Solution Approach 1:
The patent applies preliminary action by pre-characterizing the proximity effects for various pattern geometries at the reduced critical dimension scale. Test patterns with non-right turning-angles are exposed and measured in advance to build a database of proximity effects specific to the smaller dimensions, which is then used to guide OPC corrections before actual device manufacturing.
Solution Approach 2:
The patent applies parameter changes by adjusting the assistant pattern dimensions and positions based on the specific non-right turning-angle parameters. The OPC system uses the measured proximity data to calculate optimal assistant pattern parameters (size, shape, location) that compensate for the enhanced proximity effects at reduced critical dimensions.
Data Source
AI summary
This invention relates to a method for collecting an Optical Proximity Correction parameter, which includes: performing an Optical Proximity Correction for a test line containing a non-right turning-angle to generate a to-be-exposed pattern having an assistant line; obtaining, by way of simulation or actual exposure, a formed line generated from the to-be-exposed pattern being exposed; and comparing the formed line with the test line to determine a difference there between so as to determine whether there is a redundant part and/or a missing part in the assistant line at location of the turning-angle of the test line. Being compared with the prior art, this invention sets the non-right turning-angle in the to-be-exposed test line. By comparing the formed line (which is generated by simulation or actual exposure) of the above test line with the test line itself, the redundant part and/or the missing part of the assistant line, as well as proximity data such as the position of the redundant part and/or the missing part and the like, can be obtained. Then, these proximity data can be used to correct and perfect the OPC model having a line containing a non-right turning-angle.