Photomask Database Preparation for Proximity Correction
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Solution Overview
Problem
As the critical dimensions of integrated circuits decrease, conventional optical proximity correction in photolithography faces challenges with managing large amounts of data, leading to increased processing time and error likelihood, and higher costs due to the need to handle extensive information for proximity effects correction.
Innovation Solution
A method for preparing a mask pattern database that involves generating and manipulating data subsets relevant to the proximity correction process, where irrelevant data is excluded or sorted to improve processing efficiency, allowing for more effective retargeting of photomask patterns and reducing data management burdens.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the mask pattern database includes all device feature data for comprehensive proximity correction, then the correction accuracy is improved, but the data management complexity and processing time increase
Solution Approach 1:
The patent extracts and separates only the relevant data subsets that are necessary for proximity correction calculations, removing irrelevant data from the database. This allows the system to maintain high correction accuracy for critical features while significantly reducing the overall data management complexity and processing burden.
Solution Approach 2:
The patent segments the complete device feature data into distinct subsets based on relevance to proximity correction. By organizing data into targeted subsets rather than handling a monolithic dataset, the system achieves both comprehensive correction where needed and reduced complexity where data is not required.
2Manufacturing precision
If the mask pattern database includes all device feature data for comprehensive proximity correction, then the correction accuracy is improved, but the processing time increases
Solution Approach 1:
The patent extracts only the essential data subsets required for proximity correction, eliminating unnecessary data that would increase processing time. This selective extraction maintains the accuracy needed for critical dimensions while dramatically reducing the time required to manage and process the database.
Solution Approach 2:
The patent performs preliminary organization and filtering of data into relevant subsets before the proximity correction process begins. This preliminary action ensures that when correction calculations are performed, the system already has only the necessary data readily available, avoiding time-consuming filtering during the correction process itself.
3Manufacturing precision
If the mask pattern database includes all device feature data for comprehensive proximity correction, then the correction accuracy is improved, but the error likelihood increases
Solution Approach 1:
The patent extracts and includes only the data subsets that are actually relevant to proximity correction calculations. By eliminating irrelevant data from the database, the system reduces the likelihood of errors in data processing and interpretation, while maintaining the accuracy needed for high-precision correction of critical features.
Solution Approach 2:
The patent applies different data handling qualities to different parts of the database. Relevant data is maintained with high precision and detailed information, while irrelevant data is excluded entirely. This localized approach ensures high reliability in the correction process by preventing errors from propagating through unrelated data.
4Manufacturing precision
If the mask pattern database includes all device feature data for comprehensive proximity correction, then the correction accuracy is improved, but the process costs increase
Solution Approach 1:
The patent extracts only the essential data subsets needed for proximity correction, removing unnecessary data that would increase processing costs. This selective extraction maintains the accuracy required for high-precision manufacturing while significantly reducing the computational resources and operational costs associated with data management.
Solution Approach 2:
The patent changes the parameter of data quantity and type to optimize the balance between correction accuracy and process costs. By adjusting which data are included and how they are organized, the system achieves high precision where needed while minimizing the costs associated with processing and managing the complete dataset.
Data Source
AI summary
The present application is directed a method for preparing a mask pattern database for proximity correction. The method comprises receiving data from a design database. Mask pattern data describing a first photomask pattern for forming first device features is generated. The first photomask pattern is to be corrected for proximity effects in a proximity correction process. A second set of data is accessed comprising information about second device features, wherein at least a portion of the second set of data is relevant to the proximity correction process. The second set of data is manipulated so as to improve the proximity correction process, as compared with the same proximity correction process in which the second set of data was included in the mask pattern database without being manipulated. At least a portion of the mask pattern data and at least a portion of the manipulated second set of data is included in the mask pattern database.