View source for Samsung electronics co., ltd. (20240320412). PROCESS PROXIMITY CORRECTION METHOD BASED ON DEEP LEARNING, AND SEMICONDUCTOR MANUFACTURING METHOD COMPRISING THE PROCESS PROXIMITY CORRECTION METHOD simplified abstract

=PROCESS PROXIMITY CORRECTION METHOD BASED ON DEEP LEARNING, AND SEMICONDUCTOR MANUFACTURING METHOD COMPRISING THE PROCESS PROXIMITY CORRECTION METHOD=


==Organization Name==

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


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

==Inventor(s)==

[[:Category:Sooyong Lee of Suwon-si (KR)|Sooyong Lee of Suwon-si (KR)]][[Category:Sooyong Lee of Suwon-si (KR)]]

==PROCESS PROXIMITY CORRECTION METHOD BASED ON DEEP LEARNING, AND SEMICONDUCTOR MANUFACTURING METHOD COMPRISING THE PROCESS PROXIMITY CORRECTION METHOD - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240320412 titled 'PROCESS PROXIMITY CORRECTION METHOD BASED ON DEEP LEARNING, AND SEMICONDUCTOR MANUFACTURING METHOD COMPRISING THE PROCESS PROXIMITY CORRECTION METHOD

==Simplified Explanation==
This patent application describes a method that uses deep learning to correct layouts associated with manufacturing semiconductor devices, improving the accuracy of inspections and predictions.

==Key Features and Innovation==
* Utilizes deep learning to generate a predictive model based on semiconductor device manufacturing patterns.
* Corrects layouts associated with after cleaning and after development inspections.
* Predicts after cleaning inspections using layouts from after development inspections.

==Potential Applications==
This technology can be applied in the semiconductor industry for improving manufacturing processes, quality control, and inspection accuracy.

==Problems Solved==
* Enhances the accuracy of inspections in semiconductor device manufacturing.
* Improves the efficiency of layout correction processes.
* Enables better predictions of inspection results.

==Benefits==
* Increased accuracy in semiconductor device manufacturing.
* Enhanced quality control processes.
* Improved efficiency in layout correction and inspection prediction.

==Commercial Applications==
This technology can be used by semiconductor manufacturers to streamline their manufacturing processes, reduce errors, and improve overall quality control. It can also be valuable for companies involved in semiconductor inspection equipment development.

==Prior Art==
Readers interested in prior art related to this technology can explore research papers, patents, and industry publications on deep learning in semiconductor manufacturing processes, layout correction methods, and inspection prediction techniques.

==Frequently Updated Research==
Researchers are continually exploring advancements in deep learning applications in semiconductor manufacturing, layout correction, and inspection prediction. Stay updated on the latest developments in this field to leverage cutting-edge technologies for improved manufacturing processes.

==Questions about Deep Learning-Based Process Proximity Correction Method==
===How does deep learning improve the accuracy of semiconductor device manufacturing processes?===
Deep learning enables the generation of predictive models based on manufacturing patterns, leading to more precise layout corrections and inspection predictions.

===What are the potential commercial implications of implementing this technology in the semiconductor industry?===
Implementing this technology can result in enhanced manufacturing efficiency, improved quality control, and reduced errors, benefiting semiconductor manufacturers and inspection equipment developers.


==Original Abstract Submitted==

a deep learning-based process proximity correction method includes receiving a first layout associated with an after cleaning inspection (aci), the first layout including a plurality of patterns associated with manufacturing a semiconductor device, generating a predictive model based on the plurality of patterns, through deep learning, generating a layout associated with an after development inspection (adi) by correcting the first layout, and predicting an aci using the layout of adi, through the predictive model.



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[[Category:G06F30/392]]
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