20240085777.PROCESS PROXIMITY EFFECT CORRECTION METHOD AND PROCESS PROXIMITY EFFECT CORRECTION DEVICE simplified abstract (samsung electronics co., ltd.)
Contents
- 1 PROCESS PROXIMITY EFFECT CORRECTION METHOD AND PROCESS PROXIMITY EFFECT CORRECTION DEVICE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 PROCESS PROXIMITY EFFECT CORRECTION METHOD AND PROCESS PROXIMITY EFFECT CORRECTION DEVICE - 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
- 1.9.1 Unanswered Questions
- 1.9.2 How does this method compare to traditional proximity effect correction techniques in terms of accuracy and efficiency?
- 1.9.3 What are the potential limitations or challenges in implementing this process proximity effect correction method in real-world manufacturing environments?
- 1.10 Original Abstract Submitted
PROCESS PROXIMITY EFFECT CORRECTION METHOD AND PROCESS PROXIMITY EFFECT CORRECTION DEVICE
Organization Name
Inventor(s)
Dae Young Park of Suwon-si (KR)
Jeong Hoon Ko of Suwon-si (KR)
Seong Ryeol Kim of Suwon-si (KR)
Hyun Joong Kim of Suwon-si (KR)
PROCESS PROXIMITY EFFECT CORRECTION METHOD AND PROCESS PROXIMITY EFFECT CORRECTION DEVICE - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240085777 titled 'PROCESS PROXIMITY EFFECT CORRECTION METHOD AND PROCESS PROXIMITY EFFECT CORRECTION DEVICE
Simplified Explanation
The process proximity effect correction method described in the abstract involves training a sensitivity model using a machine learning module to improve the dispersion of patterns in a layout image. The method estimates a sensitivity prediction value for the patterns and determines a correction rate for the layout critical dimension based on this prediction.
- Training a sensitivity model using a machine learning module
- Estimating a sensitivity prediction value for the patterns
- Determining a correction rate for the layout critical dimension
Potential Applications
This technology can be applied in the semiconductor industry for improving the accuracy and quality of pattern dispersion in layouts.
Problems Solved
This technology solves the problem of inaccuracies and inefficiencies in pattern dispersion correction processes.
Benefits
The benefits of this technology include increased accuracy in pattern dispersion, improved overall quality of layouts, and enhanced efficiency in correction processes.
Potential Commercial Applications
A potential commercial application of this technology could be in semiconductor manufacturing for optimizing pattern dispersion in layouts.
Possible Prior Art
One possible prior art in this field could be traditional proximity effect correction methods that may not utilize machine learning for sensitivity modeling.
Unanswered Questions
How does this method compare to traditional proximity effect correction techniques in terms of accuracy and efficiency?
This article does not provide a direct comparison between this method and traditional techniques.
What are the potential limitations or challenges in implementing this process proximity effect correction method in real-world manufacturing environments?
The article does not address the potential limitations or challenges in implementing this method in practical manufacturing settings.
Original Abstract Submitted
provided is a process proximity effect correction method capable of efficiently improving the dispersion of patterns. there is a process proximity effect correction method according to some embodiments, the process proximity effect correction method of a process proximity effect correction device for performing process proximity effect correction (ppc) of a plurality of patterns using a machine learning module executed by a processor, comprising: training a sensitivity model by inputting a layout image of the plurality of patterns and a layout critical dimension (cd) of the plurality of patterns into the machine learning module; estimating an after cleaning inspection critical dimension (aci-cd) sensitivity prediction value of the plurality of patterns by inferring an aci-cd prediction value of the plurality of patterns; and determining a correction rate of the layout cd of the plurality of patterns using the estimated sensitivity prediction value.
