US 12,462,533 B2
Classification device, image classification method, and pattern inspection device
Tatsuya Okano, Kanagawa (JP); and Ryo Nakazato, Kanagawa (JP)
Assigned to Semiconductor Energy Laboratory Co., Ltd., (JP)
Appl. No. 17/918,702
Filed by Semiconductor Energy Laboratory Co., Ltd., Kanagawa-ken (JP)
PCT Filed Apr. 9, 2021, PCT No. PCT/IB2021/052938
§ 371(c)(1), (2) Date Oct. 13, 2022,
PCT Pub. No. WO2021/209867, PCT Pub. Date Oct. 21, 2021.
Claims priority of application No. 2020-073779 (JP), filed on Apr. 17, 2020.
Prior Publication US 2023/0351730 A1, Nov. 2, 2023
Int. Cl. G06V 10/774 (2022.01); G06T 7/00 (2017.01); G06V 10/762 (2022.01); G06V 10/764 (2022.01); G06V 10/77 (2022.01); G06V 10/82 (2022.01)
CPC G06V 10/774 (2022.01) [G06T 7/0004 (2013.01); G06V 10/762 (2022.01); G06V 10/764 (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)] 15 Claims
OG exemplary drawing
 
1. A classification device comprising:
a memory unit, a processing unit, and a classifier,
wherein a plurality of pieces of image data and a discriminative model are stored in the memory unit,
wherein the discriminative model comprises a plurality of convolutional layers, a plurality of pooling layers, and a plurality of fully connected layers,
wherein the plurality of fully connected layers comprise a first fully connected layer, a second fully connected layer, a third fully connected layer, a fourth fully connected layer, and a fifth fully connected layer,
wherein first to n-th image data of the plurality of pieces of image data are supplied to the processing unit where n is an integer greater than or equal to 2,
wherein the processing unit is configured to output a k-th feature value of k-th image data on the basis of the discriminative model where k is an integer greater than or equal to 1 and less than or equal to n,
wherein the fifth fully connected layer is an output layer,
wherein the fourth fully connected layer is connected to the fifth fully connected layer,
wherein the third fully connected layer is connected to the fourth fully connected layer,
wherein the second fully connected layer is connected to the third fully connected layer,
wherein the first fully connected layer is connected to the second fully connected layer,
wherein a feature value output from the processing unit is any one of a numerical value of a neuron included in the first fully connected layer, a numerical value of a neuron included in the second fully connected layer, or a numerical value of a neuron included in the third fully connected layer,
wherein a first to an n-th feature value output from the processing unit are supplied to the classifier, and
wherein the classifier is configured to perform clustering of the first to the n-th image data on the basis of the first to the n-th feature value.