公开(公告)号：US20200294760A1

公开(公告)日：2020-09-17

申请号：US16669274

申请日：2019-10-30

Applicant: THE NCS TESTING TECHNOLOGY CO., LTD.

Inventor： Haizhou WANG ,  Xing YU ,  Xuejing SHEN ,  Yunhai JIA ,  Xiaojia LI ,  Yuhua LU ,  Weihao WAN ,  Jianqiu LUO ,  Dongling LI ,  Lei ZHAO

IPC: H01J37/22 ,  H01J37/28 ,  H01J37/34 ,  H01J37/20 ,  G06T15/08 ,  G06T11/20 ,  C23C14/34

Abstract: An apparatus and method for a large-scale high-throughput quantitative characterization and three-dimensional reconstruction of a material structure. The apparatus having a glow discharge sputtering unit, a sample transfer device, a scanning electron microscope unit and a GPU computer workstation. The glow discharge sputtering unit can achieve large size (cm order), nearly flat and fast sample preparation, and controllable achieve layer-by-layer ablation preparation along the depth direction of the sample surface; rapid scanning electron microscopy (SEM) can achieve large-scale and high-throughput acquisition of sample characteristic maps. The sample transfer device is responsible for transferring the sample between the glow discharge sputtering source and the scanning electron microscope in an accurately positioning manner. The GPU computer workstation performs splicing, processing, recognition and quantitative distribution characterization on the acquired sample characteristic maps, and carries out three-dimensional reconstruction of the structure of the sample prepared by layer-by-layer sputtering.

公开(公告)号：US20210063376A1

公开(公告)日：2021-03-04

申请号：US17009117

申请日：2020-09-01

Applicant: The NCS Testing Technology Co., Ltd.

Inventor： Dongling LI ,  Weihao WAN ,  Jie LI ,  Haizhou WANG ,  Lei ZHAO ,  Xuejing SHEN ,  Yunhai JIA

IPC: G01N33/204 ,  G01N1/32 ,  G06T7/11 ,  G01N1/40 ,  G06T7/62

Abstract: The invention belongs to the technical field of quantitative statistical distribution analysis for micro-structures of metal materials, and relates to a method for automatic quantitative statistical distribution characterization of dendrite structures in a full view field of metal materials. According to the method based on deep learning in the present invention, dendrite structure feature maps are marked and trained to obtain a corresponding object detection model, so as to carry out automatic identification and marking of dendrite structure centers in a full view field; and in combination with an image processing method, feature parameters in the full view field such as morphology, position, number and spacing of all dendrite structures within a large range are obtained quickly, thereby achieving quantitative statistical distribution characterization of dendrite structures in the metal material. The method is accurate, automatic and efficient, involves a large amount of quantitative statistical distribution information, and is statistically more representative as compared with the traditional measurement of feature sizes of dendrite structures in a single view field.