Optical Proximity Correction Using Region-Specific Resist Models
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Solution Overview
Problem
Current optical proximity correction (OPC) methods face challenges in achieving consistent pattern formation on semiconductor wafers, particularly in edge and corner regions, due to variations in resist characteristics and optical effects during the exposure process.
Innovation Solution
The proposed OPC method generates a first model reflecting optical effects and a second model reflecting photoresist characteristics, using a combination of kernel functions differently applied to each pattern region. This approach enhances the accuracy of OPC modeling by region-based resist models, improving consistency across the pattern.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a single uniform OPC model is used for the entire pattern, then the modeling process is simple and fast, but the accuracy and consistency of pattern formation deteriorates, especially in edge and corner regions
Solution Approach 1:
The patent divides the pattern into multiple regions (e.g., center region, edge region, corner region) and creates separate OPC models for each region. This segmentation allows each region to be modeled with appropriate parameters specific to its characteristics, thereby improving overall pattern formation consistency without requiring a completely complex unified model.
Solution Approach 2:
The patent applies different OPC models tailored to local region characteristics. Edge regions use models optimized for edge effects, corner regions use models for corner effects, and center regions use appropriate models for their characteristics. This local quality approach ensures high accuracy in each region while maintaining manageable model complexity through specialization.
2Measurement precision
If region-based resist models with multiple kernel functions are applied, then the OPC model accuracy and consistency are improved, but the computational time and model generation complexity increase
Solution Approach 1:
The patent segments the pattern into distinct regions and applies different kernel function combinations to each region based on its specific characteristics. This allows the system to use computationally intensive region-based modeling only where necessary (edges and corners) while using simpler models for less critical areas, thereby balancing accuracy improvement with computational time management.
Solution Approach 2:
The patent applies region-based resist models with multiple kernel functions selectively rather than uniformly across the entire pattern. By applying complex modeling only to regions that require it (edges and corners) and using simpler models elsewhere, the system achieves high accuracy where needed while reducing overall computational time compared to uniform complex modeling.
Data Source
AI summary
Some example embodiments provide an optical proximity correction (OPC) method using an OPC model having improved performance and/or a method of manufacturing a mask by using the OPC method. An OPC method includes receiving a design layout of a target pattern, generating a first OPC model on the design layout, in which an optical effect of an exposure process is reflected, generating a second OPC model in which a characteristic of a photoresist in the exposure process is reflected, and performing a simulation using the first and second OPC models to obtain an OPC-performed design layout. The generating the second OPC model includes differently applying a combination of kernel functions, used in the second OPC model, to each pattern region.