Samsung Electronics Co., Ltd.

Eunseog Cho

Sangmo Kim

Yooseong Yang

Byung-hee Sohn

Youngsuk Jung

Seungwoo Seo

Kyongmin Min

A method of generating a 3D microstructure using a neural network includes configuring an initial 3D microstructure; obtaining a plurality of cross-sectional images by disassembling the initial 3D microstructure in at least one direction of the initial 3D microstructure; obtaining first output feature maps with respect to at least one layer that constitutes the neural network by inputting each of the cross-sectional images to the neural network; obtaining second output feature maps with respect to at least one layer by inputting a 2D original image to the neural network; generating a 3D gradient by applying a loss value to a back-propagation algorithm after calculating the loss value by comparing the first output feature maps with the second output feature maps; and generating a final 3D microstructure based on the 2D original image by reflecting the 3D gradient to the initial 3D microstructure.

Method and apparatus for reconstructing 3D microstructure using neural network

Polyamic acid including structural units of the following Chemical Formulae 1 and 2 is provided: In Chemical Formulae 1 and 2, each substituent is as defined in the detailed description.

Polymer, composition and film

A polyimide precursor composition includes an oligomer or polymer having a substituted or unsubstituted carbonyl group only in a side chain and a diamine compound. A method of manufacturing a polyimide, and a polyimide film, are also disclosed.

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Seoul, South Korea

Eunseog Cho