3D micro profile measurement with the method of spatial frequency domain analysis

2015 ◽  
Author(s):  
Yongxiang Xu
Keyword(s):  
Spatial Frequency ◽  
Frequency Domain ◽  
Domain Analysis ◽  
Frequency Domain Analysis ◽  
Profile Measurement ◽  
Spatial Frequency Domain

Spatial frequency domain analysis of a commercially available digital dental detector

Measurement ◽  
2020 ◽  
Vol 151 ◽  
pp. 107171
Author(s):  
A. Anastasiou ◽  
F. Papastamati ◽  
A. Bakas ◽  
C. Michail ◽  
V. Koukou ◽  
...  
Keyword(s):  
Spatial Frequency ◽  
Frequency Domain ◽  
Domain Analysis ◽  
Frequency Domain Analysis ◽  
Spatial Frequency Domain

Spatial Frequency Domain Analysis of Heat Transfer in Microelectronic Chips With Applications to Temperature Aware Computing

2007 ◽  
Author(s):  
Keivan Etessam-Yazdani ◽  
Hendrik F. Hamann ◽  
Mehdi Asheghi
Keyword(s):  
Transfer Function ◽  
Spatial Frequency ◽  
Frequency Domain ◽  
Thermal Power ◽  
Three Dimensional ◽  
Heat Removal ◽  
Frequency Domain Analysis ◽  
Heat Diffusion ◽  
The Impact ◽  
Spatial Frequency Domain

In this paper we present a novel analytical approach for obtaining the thermal transfer function of multi-layer chips in the spatial frequency domain. The behavior of the transfer function is used to address a number of key issues such as 1) the appropriate power granularity required for microarchitecture thermal-power analysis, and 2) the impact of packaging and cooling solutions on heat removal from chip hotspots. The merit of the presented method is in 1) simplicity, such that even for rather complicated multi-layer structures the analysis takes only a fraction of a second, and 2) accuracy, because the approach is based on the exact solution of three-dimensional heat diffusion equations.

A Spatial Frequency Domain Analysis of the Belousov–Zhabotinsky Reaction

2014 ◽  
Vol 24 (03) ◽  
pp. 1450031 ◽  
Author(s):  
Lingzhong Guo ◽  
Yuzhu Guo ◽  
Yifan Zhao ◽  
Stephen A. Billings ◽  
Daniel Coca ◽  
...  
Keyword(s):  
Spatial Frequency ◽  
Frequency Domain ◽  
Dynamical Behavior ◽  
Domain Analysis ◽  
Frequency Domain Analysis ◽  
Bz Reaction ◽  
Temporal Domain ◽  
Temporal System ◽  
Cml Model ◽  
Spatio Temporal

A nonlinear spatio-temporal system identification and analysis approach is used to study the dynamical behavior of the Belousov–Zhabotinsky (BZ) chemical reaction process. In our previous study [Guo et al., 2010],, the dynamical behavior of the BZ reaction in the spatial-temporal domain has been analyzed by identifying a coupled map lattice (CML) model of the process directly from experimental data from a real BZ reaction experiment. In this paper, the frequency domain analysis of the dynamics near equilibrium is carried out by mapping the obtained CML model into higher order spatial frequency response functions to reveal the nonlinear coupling and modulation between the various initial spectral components in the process. As far as we are aware, this is the first study of any real spatio-temporal system using a spatio-temporal domain identification and frequency domain analysis approach.

scholarly journals A Spatial-Frequency Domain Associated Image-Optimization Method for Illumination-Robust Image Matching

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6489
Author(s):  
Chun Liu ◽  
Shoujun Jia ◽  
Hangbin Wu ◽  
Doudou Zeng ◽  
Fanjin Cheng ◽  
...  
Keyword(s):  
Spatial Frequency ◽  
Frequency Domain ◽  
Image Matching ◽  
Optimization Method ◽  
Frequency Domain Analysis ◽  
Image Features ◽  
Practical Applications ◽  
Feature Enhancement ◽  
Image Optimization ◽  
Spatial Frequency Domain

Image matching forms an essential means of data association for computer vision, photogrammetry and remote sensing. The quality of image matching is heavily dependent on image details and naturalness. However, complex illuminations, denoting extreme and changing illuminations, are inevitable in real scenarios, and seriously deteriorate image matching performance due to their significant influence on the image naturalness and details. In this paper, a spatial-frequency domain associated image-optimization method, comprising two main models, is specially designed for improving image matching with complex illuminations. First, an adaptive luminance equalization is implemented in the spatial domain to reduce radiometric variations, instead of removing all illumination components. Second, a frequency domain analysis-based feature-enhancement model is proposed to enhance image features while preserving image naturalness and restraining over-enhancement. The proposed method associates the advantages of the spatial and frequency domain analyses to complete illumination equalization, feature enhancement and naturalness preservation, and thus acquiring the optimized images that are robust to the complex illuminations. More importantly, our method is generic and can be embedded in most image-matching schemes to improve image matching. The proposed method was evaluated on two different datasets and compared with four other state-of-the-art methods. The experimental results indicate that the proposed method outperforms other methods under complex illuminations, in both matching performances and practical applications such as structure from motion and multi-view stereo.

Design of Digital Filters for Si Wafer Surface Profile Measurement - Denoising by Total Variation

2012 ◽  
Vol 516 ◽  
pp. 332-336
Author(s):  
Hirotaka Ojima ◽  
Kazutaka Nonomura ◽  
Li Bo Zhou ◽  
Jun Shimizu ◽  
Teppei Onuki
Keyword(s):  
High Resolution ◽  
Spatial Frequency ◽  
Frequency Domain ◽  
Total Variation ◽  
Digital Filters ◽  
Measured Data ◽  
Design Rule ◽  
Profile Measurement ◽  
Amplitude Component ◽  
Spatial Frequency Domain

In the semiconductor industry, high resolution and high accuracy measurement is needed for the geometric evaluation of Si wafers. The flatness parameters are important to evaluate the wafer profile and are required to be the same level as the design rule of IC, and the tolerance for flatness is very tight. According to SEMI (Semiconductor Equipment and Materials International) standards, the required wafer flatness will be 22 nanometres by the year 2016. However, to obtain a higher resolution for sensors, the uncertainty becomes very large compared to the resolution and influences the measured data when the noise is increased. High resolution instruments always incorporate a certain degree of noise. In the presence of noise, form parameters are normally biased. Correction and compensation need a large population of measurements to analytically estimate both bias and uncertainty. The estimation is still far from perfect because of the nature of noise. Another approach is to extract a true profile by filtering noise from the measured data. For the purpose of noise reduction, low-pass filters by Gaussian smoothing and Fourier transform are often used. The noise is normally considered to be a component of small deviation (amplitude) with high frequency which also takes a normal distribution around zero. However these conventional filters can remove the noise in the spatial frequency domain only. So, it is essential to design a filter capable of removing the noise both in the spatial frequency domain and the amplitude component. Thus, we have designed and developed new type of digital filter for denoising. We introduce two new digital filters. One is wavelet transform capable of denoising in the spatial frequency domain and amplitude component, and the other is total variation that can be applied to discontinuous signals without introducing artificial Gibbs Effects.

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