Registration accuracy improvement of fiducial mark on EUVL mask with MIRAI EUV ABI prototype

2013 ◽  
Author(s):  
Tetsunori Murachi ◽  
Tsuyoshi Amano
Keyword(s):  
Registration Accuracy ◽  
Accuracy Improvement ◽  
Fiducial Mark

Accuracy Improvement of Terminal Voltage Self-coupled Laser Distance Sensor

2020 ◽  
Vol 140 (11) ◽  
pp. 1264-1269
Author(s):  
Tatsuya Ohba ◽  
Daisuke Mizushima ◽  
Keishiro Goshima ◽  
Norio Tsuda ◽  
Jun Yamada
Keyword(s):  
Accuracy Improvement ◽  
Terminal Voltage ◽  
Laser Distance Sensor ◽  
Distance Sensor

A Study of a Next Day Electric Load Curve Forecasting Method and its Accuracy Improvement

2014 ◽  
Vol 134 (1) ◽  
pp. 9-15 ◽  
Author(s):  
Hisatomo Miyata ◽  
Kazutoshi Miyashita ◽  
Takayuki Endo ◽  
Yuichi Shimasaki ◽  
Tatsuya Iizaka ◽  
...  
Keyword(s):  
Electric Load ◽  
Load Curve ◽  
Accuracy Improvement ◽  
Forecasting Method

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

2020 ◽  
Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Optical Image ◽  
Point Cloud Data ◽  
Feature Points ◽  
Lidar System ◽  
Registration Accuracy ◽  
Cloud Data ◽  
Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

Automated Emission Data Registration and Segmentation for IC Analysis

2016 ◽  
Author(s):  
Franco Stellari ◽  
Peilin Song
Keyword(s):  
Data Analysis ◽  
Registration Accuracy ◽  
Emission Data ◽  
Data Registration ◽  
Different Types ◽  
Test Chips

Abstract In this paper, the development of advanced emission data analysis methodologies for IC debugging and characterization is discussed. Techniques for automated layout to emission registration and data segmentations are proposed and demonstrated using both 22 nm and 14 nm SOI test chips. In particular, gate level registration accuracy is leveraged to compare the emission of different types of gates and quickly create variability maps automatically.

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