US 11,699,224 B2
Neural network training device, system and method
Laurent Bidault, Trets (FR)
Assigned to STMICROELECTRONICS (ROUSSET) SAS, Rousset (FR)
Filed by STMICROELECTRONICS (ROUSSET) SAS, Rousset (FR)
Filed on Nov. 9, 2021, as Appl. No. 17/522,541.
Application 17/522,541 is a continuation of application No. 16/687,349, filed on Nov. 18, 2019, granted, now 11,200,659.
Prior Publication US 2022/0067916 A1, Mar. 3, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G06T 7/00 (2017.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01); G06F 18/214 (2023.01); G06F 18/2431 (2023.01); G06V 10/764 (2022.01); G06V 10/77 (2022.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06V 10/82 (2022.01)
CPC G06T 7/0004 (2013.01) [G06F 18/214 (2023.01); G06F 18/2431 (2023.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06V 10/7715 (2022.01); G06V 10/82 (2022.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30148 (2013.01); G06V 2201/06 (2022.01)] 33 Claims
OG exemplary drawing
 
1. A device, comprising:
image generation circuitry, which, in operation, generates a digital image representation of a wafer defect map (WDM); and
convolutional-neural-network (CNN) circuitry, which, in operation, generates a defect classification associated with the WDM based on:
the digital image representation of the WDM; and
a data-driven model associating WDM images with classes of a defined set of classes of wafer defects and generated using a data set of images, wherein the data set of images includes training images and augmented images generated based on defect pattern orientation types associated with the training images.