View source for 20240085777.PROCESS PROXIMITY EFFECT CORRECTION METHOD AND PROCESS PROXIMITY EFFECT CORRECTION DEVICE simplified abstract (samsung electronics co., ltd.)

=PROCESS PROXIMITY EFFECT CORRECTION METHOD AND PROCESS PROXIMITY EFFECT CORRECTION DEVICE=


==Organization Name==

[[:Category:samsung electronics co., ltd.|samsung electronics co., ltd.]]


[[Category:samsung electronics co., ltd.]]

==Inventor(s)==

[[:Category:Dae Young Park of Suwon-si (KR)|Dae Young Park of Suwon-si (KR)]][[Category:Dae Young Park of Suwon-si (KR)]]

[[:Category:Jeong Hoon Ko of Suwon-si (KR)|Jeong Hoon Ko of Suwon-si (KR)]][[Category:Jeong Hoon Ko of Suwon-si (KR)]]

[[:Category:Seong Ryeol Kim of Suwon-si (KR)|Seong Ryeol Kim of Suwon-si (KR)]][[Category:Seong Ryeol Kim of Suwon-si (KR)]]

[[:Category:Young-Gu Kim of Suwon-si (KR)|Young-Gu Kim of Suwon-si (KR)]][[Category:Young-Gu Kim of Suwon-si (KR)]]

[[:Category:Tae Hoon Kim of Suwon-si (KR)|Tae Hoon Kim of Suwon-si (KR)]][[Category:Tae Hoon Kim of Suwon-si (KR)]]

[[:Category:Hyun Joong Kim of Suwon-si (KR)|Hyun Joong Kim of Suwon-si (KR)]][[Category:Hyun Joong Kim of Suwon-si (KR)]]

[[:Category:Young Ju Lee of Suwon-si (KR)|Young Ju Lee of Suwon-si (KR)]][[Category:Young Ju Lee 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.



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