Samsung Electronics Co., Ltd.

Kyenhee Lee

Kyungsoo Kim

Sooryong Lee

Mincheol Kang

An integrated circuit semiconductor element includes: a substrate; a complementary field effect transistor (FET) (cFET) formed over the substrate and having a quadruple-gate structure, in which nano sheet stacked structures are sequentially stacked; and a planar FET having a mono-gate structure or a zebra fin FET (ZE FINFET) having a triple-gate structure, which are formed over the substrate.

Integrated circuit semiconductor element having heterogeneous gate structures and method of fabricating integrated circuit semiconductor element

A static random-access memory (SRAM) device including a three-dimensional structured (3DS) field-effect transistor (FET) having a minimized planar area and a simple wiring connection structure includes a semiconductor substrate, a first fin active region extending on the semiconductor substrate in a first direction, a second fin active region extending on the semiconductor substrate in the first direction and apart from the first fin active region in a second direction perpendicular to the first direction, and four gates extending in the second direction and intersecting part of the first fin active region or the second fin active region. Each of the first fin active region and the second fin active region includes a first region in which only a lower layer is arranged and a second region in which an upper layer is arranged on the lower layer.

Static random-access memory (SRAM) device including three-dimensional stacked (3DS) field-effect transistor (FET) and layout thereof

The inventive concept provides a defect detection method of a semiconductor element, capable of promptly and accurately detecting a defect, and predicting a type of the defect with respect to various defects of the semiconductor element, and a semiconductor element manufacturing method including the defect detection method. The defect detection method is capable of promptly and accurately detecting the defect, and predicting the type of the defect with respect to various defects of the semiconductor element, by generating a first segmentation image and a second segmentation image; converting the first segmentation image and the second segmentation image into an image of a first color and a second color, respectively; generating a combination image; classifying the type of a defect; generating a defect detection model by using deep learning, and detecting a defect of the semiconductor element by using a defect detection process using the defect detection model.

Defect detection method of deep learning-based semiconductor device and semiconductor element manufacturing method including the defect detection method

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Seoul, South Korea

Kyenhee Lee