View source for 20240054634. RESIDUE CLASSIFICATION FROM MACHINE LEARNING BASED PROCESSING OF SUBSTRATE IMAGES simplified abstract (Applied Materials, Inc)

=RESIDUE CLASSIFICATION FROM MACHINE LEARNING BASED PROCESSING OF SUBSTRATE IMAGES=


==Organization Name==

[[:Category:Applied Materials, Inc|Applied Materials, Inc]]


[[Category:Applied Materials, Inc]]

==Inventor(s)==

[[:Category:Sivakumar Dhandapani of San Jose CA (US)|Sivakumar Dhandapani of San Jose CA (US)]][[Category:Sivakumar Dhandapani of San Jose CA (US)]]

[[:Category:Arash Alahgholipouromrani of San Jose CA (US)|Arash Alahgholipouromrani of San Jose CA (US)]][[Category:Arash Alahgholipouromrani of San Jose CA (US)]]

[[:Category:Dominic J. Benvegnu of La Honda CA (US)|Dominic J. Benvegnu of La Honda CA (US)]][[Category:Dominic J. Benvegnu of La Honda CA (US)]]

[[:Category:Jun Qian of Sunnyvale CA (US)|Jun Qian of Sunnyvale CA (US)]][[Category:Jun Qian of Sunnyvale CA (US)]]

[[:Category:Kiran Lall Shrestha of San Jose CA (US)|Kiran Lall Shrestha of San Jose CA (US)]][[Category:Kiran Lall Shrestha of San Jose CA (US)]]

==RESIDUE CLASSIFICATION FROM MACHINE LEARNING BASED PROCESSING OF SUBSTRATE IMAGES - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240054634 titled 'RESIDUE CLASSIFICATION FROM MACHINE LEARNING BASED PROCESSING OF SUBSTRATE IMAGES

==Simplified Explanation==

The abstract describes a neural network trained for substrate residue classification by obtaining ground truth residue measurements of a calibration substrate and converting color images of die regions to residue level measurements.

* Ground truth residue measurements obtained for a calibration substrate
* Color images of die regions converted to residue level measurements by neural network
* Training process for substrate residue classification system

== Potential Applications ==
* Quality control in semiconductor manufacturing
* Automated inspection of substrates for residue levels

== Problems Solved ==
* Manual inspection and measurement of substrate residues
* Inconsistencies in residue classification across different substrates

== Benefits ==
* Increased accuracy and efficiency in residue classification
* Reduction in human error in measurement process
* Cost savings through automation of inspection process


==Original Abstract Submitted==

a neural network is trained for use in a substrate residue classification system by obtaining ground truth residue level measurements of a top layer of a calibration substrate at a plurality of locations, each location at a defined position for a die being fabricated on the substrate. a plurality of color images of the calibration substrate are obtained, each color image corresponding to a region for a die being fabricated on the substrate. a neural network is trained to convert color images of die regions from an in-line substrate imager to residue level measurements for the top layer in the die region.



[[Category:G06T7/00]]
[[Category:G06T7/60]]
[[Category:B24B37/013]]