Hybrid Optical Proximity Correction for Lower Data Capacity
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Solution Overview
Problem
Existing optical proximity correction (OPC) methods face challenges in efficiently reducing data capacity while ensuring that layout patterns on a mask satisfy design rules, particularly when using curvilinear OPC, which can increase turnaround time and data capacity.
Innovation Solution
A hybrid OPC method that combines Manhattan OPC for edge sections and curvilinear OPC for corner sections, where the original target pattern is rounded into a curved shape, divided into linear and curved sections, and then modified and merged to create a corrected pattern.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If curvilinear OPC is applied to all sections of the pattern, then manufacturing precision is improved, but data capacity increases and turnaround time increases
Solution Approach 1:
The pattern is divided into corner sections and non-corner sections. Curvilinear OPC is applied only to corner sections where high precision is critical, while Manhattan OPC is applied to non-corner sections. This segmentation allows selective application of computationally intensive methods only where necessary, reducing overall data capacity requirements while maintaining manufacturing precision in critical areas.
Solution Approach 2:
Different OPC methods are applied to different regions of the pattern based on local requirements. Corner sections receive curvilinear OPC treatment for high precision, while non-corner sections use Manhattan OPC. This local quality approach optimizes the balance between precision and data capacity by matching method complexity to local pattern characteristics.
2Manufacturing precision
If curvilinear OPC is applied to all sections of the pattern, then manufacturing precision is improved, but turnaround time increases
Solution Approach 1:
The pattern processing is segmented into corner sections and non-corner sections. Curvilinear OPC is applied only to corner sections, which are fewer in number and require high precision, while Manhattan OPC handles the majority of non-corner sections. This segmentation significantly reduces the total computation time while maintaining precision where it matters most.
Solution Approach 2:
The solution applies different levels of processing quality to different regions: high-quality curvilinear OPC for corners and standard-quality Manhattan OPC for non-corners. This local quality differentiation reduces overall turnaround time while ensuring manufacturing precision is maintained in critical corner regions.
3Productivity
If Manhattan OPC is used for all sections, then data capacity is reduced and turnaround time is reduced, but manufacturing precision deteriorates
Solution Approach 1:
The solution enhances Manhattan OPC by detecting corner sections and applying curvilinear OPC specifically to those locations. This local quality enhancement maintains high processing efficiency for non-corner sections while improving manufacturing precision at critical corner regions through selective application of the more accurate curvilinear method.
Data Source
AI summary
An optical proximity correction (OPC) method for manufacturing a semiconductor chip includes: detecting corners of an original target pattern having a polygonal shape; rounding off the corners to generate a curved target pattern; dividing the curved target pattern into a linear section pattern and a curved section pattern; applying Manhattan OPC to the linear section pattern to generate a modified linear section pattern; applying curvilinear OPC to the curved section pattern to generate a modified curved section pattern; and merging the modified linear section pattern and the modified curved section pattern to generate a corrected pattern.