18341124. METHODS OF TRAINING DEEP LEARNING MODELS FOR OPTICAL PROXIMITY CORRECTION, OPTICAL PROXIMITY CORRECTION METHODS, AND METHODS OF MANUFACTURING SEMICONDUCTOR DEVICES USING THE SAME simplified abstract (Samsung Electronics Co., Ltd.)
Contents
- 1 METHODS OF TRAINING DEEP LEARNING MODELS FOR OPTICAL PROXIMITY CORRECTION, OPTICAL PROXIMITY CORRECTION METHODS, AND METHODS OF MANUFACTURING SEMICONDUCTOR DEVICES USING THE SAME
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHODS OF TRAINING DEEP LEARNING MODELS FOR OPTICAL PROXIMITY CORRECTION, OPTICAL PROXIMITY CORRECTION METHODS, AND METHODS OF MANUFACTURING SEMICONDUCTOR DEVICES USING THE SAME - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
METHODS OF TRAINING DEEP LEARNING MODELS FOR OPTICAL PROXIMITY CORRECTION, OPTICAL PROXIMITY CORRECTION METHODS, AND METHODS OF MANUFACTURING SEMICONDUCTOR DEVICES USING THE SAME
Organization Name
Inventor(s)
METHODS OF TRAINING DEEP LEARNING MODELS FOR OPTICAL PROXIMITY CORRECTION, OPTICAL PROXIMITY CORRECTION METHODS, AND METHODS OF MANUFACTURING SEMICONDUCTOR DEVICES USING THE SAME - A simplified explanation of the abstract
This abstract first appeared for US patent application 18341124 titled 'METHODS OF TRAINING DEEP LEARNING MODELS FOR OPTICAL PROXIMITY CORRECTION, OPTICAL PROXIMITY CORRECTION METHODS, AND METHODS OF MANUFACTURING SEMICONDUCTOR DEVICES USING THE SAME
Simplified Explanation
The patent application describes a method of training a deep learning model for optical proximity correction, specifically focusing on corner rounding operations for semiconductor device layouts.
- Sample input images associated with sample layouts are obtained.
- Sample reference images are extracted from sample masks fabricated by performing optical proximity correction on the sample layouts.
- A training operation is performed on the deep learning model using the sample input images and sample reference images.
- The deep learning model is used to perform corner rounding operations on the corner portions of the sample layout patterns.
Potential Applications
This technology can be applied in the semiconductor industry for improving the accuracy and efficiency of optical proximity correction processes.
Problems Solved
This technology helps in automating and enhancing the corner rounding operations in semiconductor device layouts, reducing manual intervention and improving overall quality.
Benefits
The benefits of this technology include increased precision in corner rounding, faster processing times, and improved yield in semiconductor manufacturing.
Potential Commercial Applications
One potential commercial application of this technology is in semiconductor fabrication facilities to optimize the optical proximity correction process for better semiconductor device performance.
Possible Prior Art
Prior art in the field of optical proximity correction includes traditional rule-based methods and other machine learning approaches for improving semiconductor manufacturing processes.
Unanswered Questions
How does this technology compare to existing corner rounding techniques in terms of accuracy and efficiency?
This article does not provide a direct comparison with existing corner rounding techniques, leaving the reader to wonder about the specific advantages of this deep learning approach.
What are the limitations or challenges of implementing this deep learning model in a real-world semiconductor manufacturing environment?
The article does not address potential obstacles or difficulties that may arise when integrating this technology into existing semiconductor manufacturing processes, leaving room for further exploration of practical implications.
Original Abstract Submitted
In a method of training a deep learning model for optical proximity correction, sample input images associated with sample layouts may be obtained, where the sample layouts are targets of the optical proximity correction. Sample reference images that correspond to the sample input images may be extracted from sample masks that are fabricated by performing the optical proximity correction on the sample layouts. A training operation may be performed on the deep learning model used in the optical proximity correction based on the sample input images and the sample reference images. The sample layouts may include sample layout patterns to form process patterns of a semiconductor device. The sample input images may include images of corner portions of the sample layout patterns. The deep learning model may be used to perform a corner rounding operation on the corner portions of the sample layout patterns.
