M.2 NVMe SSD (Non-Volatile Memory express Solid-State Drive), which have higher computational speed and reliability than conventional devices, have come to be widely used. Recent studies have reported that M.2 NVMe SSD are beginning to have thermal issues due to the increasing heat generation occurring with the high chip density and high-performance operation in a limited space. Thermal issues in the controller and memory units of M.2 NVMe SSD lead to increased failure rates and decreased data retention times. In this study, we propose a compact and optimized thermal solution for commercial M.2 NVMe SSD installed between the mainboard and GPU (Graphic Processing Unit). A thermal and fluid dynamics simulation of an M.2 NVMe SSD, including the heatsink, was performed, and the Genetic Algorithm method was used to optimize the heatsink size.

As the size of semiconductor devices gradually decreases, it is important to measure and analyze semiconductor devices, to improve the image quality of semiconductors. We use VDSR, one of the Super-Resolution methods to improve the quality of semiconductor devices’ SEM images. VDSR is also a convolutional neural network that can be optimized with various parameters. In this study, a VDSR model for semiconductor devices’ SEM images was optimized using parameters such as depth of layers and amount of training data. Meanwhile, the quantitative evaluation and the qualitative evaluation did not match at the low scale factor. To solve this problem, we proposed an MTF measurement method using the slanted edge for better quantitative evaluation. This method was verified by comparing the results with the PSNR and SSIM index results, which are known as quality indicators. Based on the results, it was confirmed that using the MTF value could be a better approach for the evaluation of SEM images of the semiconductor device than using PSNR and SSIM.

Recently, the requirement of automation in the cell production system is increasing due to a decrease of skilled workers who are the key point of a cell production system. This paper proposes a dual-arm robot designed and implemented with consideration of being applied to a cell production line of cellular phone. A specification was derived from the analysis of production process and the consideration of configuration for human-robot cooperation. Design and implementation results of the proposed dual-arm robot were suggested and the feasibility was verified through the demonstration of the proposed robot in some of packaging job of cellular phone.

The introduction of industrial intelligent robot using vision sensor has been interested in automated factory. 2D and 3D vision sensors have used to recognize object and to estimate object pose, which is for packaging parts onto a complete whole. But it is not trivial task due to illumination and various types of objects. Object image has distorted due to illumination that has caused low reliability in recognition. In this paper, recognition method of complex shape object has been proposed. An accurate object region has detected from combined binary image, which has achieved using DoG filter and local adaptive binarization. The object has recognized using neural network, which is trained with sub-divided object class according to object type and rotation angle. Predefined shape model of object and maximal slope have used to estimate the pose of object. The performance has evaluated on ETRI database and recognition rate of 96% has obtained.