Faraday induction effect applied to the detection of defects in a moving plate

2013 ◽  
Author(s):  
H. G. Ramos ◽  
T. Rocha ◽  
D. Pasadas ◽  
A. L. Ribeiro
Keyword(s):  
Induction Effect ◽  
Moving Plate ◽  
Faraday Induction ◽  
Detection Of Defects

Electron excess and electromagnetic induction effect of extensive air showers

2010 ◽  
Vol 16 (3) ◽  
pp. 3-14 ◽  
Author(s):  
V.M. Kartashev ◽  
◽  
P.S. Kizim ◽  
V.E. Kovtun ◽  
S.N. Stervoiedov ◽  
...  
Keyword(s):  
Electromagnetic Induction ◽  
Extensive Air Showers ◽  
Induction Effect ◽  
Air Showers

Thermal Imaging Product Power-up: Metrology for Capturing Thermal Defects on Live Units Not Visible on Any Other Technique

2005 ◽  
Author(s):  
Nova T. Zamora ◽  
Kam Meng Chong ◽  
Ashish Gupta
Keyword(s):  
Thermal Imaging ◽  
Thermal Response ◽  
Recent Application ◽  
Ir Camera ◽  
Thermal Failure ◽  
Response Data ◽  
Startup Process ◽  
Customer Returns ◽  
Actual Computer ◽  
Detection Of Defects

Abstract This paper presented the recent application of die powerup in Thermal Imaging as applied to the detection of defects causing thermal failure on revenue products or units not being captured using other available techniques. Simulating the condition on an actual computer setup, the infrared (IR) camera should capture images simultaneously as the entire bootup process is being executed by the processor, thus revealing a series of images and thermal information on each and every step of the startup process. This metrology gives the failure analyst a better approach to acquire a set of information that substantiate in the conduct of rootcause analysis of thermal-related failure in revenue units, especially on customer returns. Defective units were intentionally engineered in order to collect the thermal response data and eventually come up with a plot of all known thermal-related defects.

Failure and Stress Analysis of Cu TSVs Using GHz-Scanning Acoustic Microscopy and Scanning Infrared Polariscopy

2015 ◽  
Author(s):  
Ingrid De Wolf ◽  
Ahmad Khaled ◽  
Martin Herms ◽  
Matthias Wagner ◽  
Tatjana Djuric ◽  
...  
Keyword(s):  
Failure Analysis ◽  
Stress Analysis ◽  
Rayleigh Waves ◽  
Acoustic Microscopy ◽  
Scanning Acoustic Microscopy ◽  
Through Silicon Vias ◽  
Stress Anisotropy ◽  
Silicon Vias ◽  
Ic Technology ◽  
Detection Of Defects

Abstract This paper discusses the application of two different techniques for failure analysis of Cu through-silicon vias (TSVs), used in 3D stacked-IC technology. The first technique is GHz Scanning Acoustic Microscopy (GHz- SAM), which not only allows detection of defects like voids, cracks and delamination, but also the visualization of Rayleigh waves. GHz-SAM can provide information on voids, delamination and possibly stress near the TSVs. The second is a reflection-based photoelastic technique (SIREX), which is shown to be very sensitive to stress anisotropy in the Si near TSVs and as such also to any defect affecting this stress, such as delamination and large voids.

scholarly journals Asymmetric Catalysis with MOFs Prepared via Chiral Induction Effect

Synfacts ◽  
2010 ◽  
Vol 2011 (01) ◽  
pp. 0105-0105
Author(s):  
C. Duan ◽  
D. Dang ◽  
P. Wu ◽  
C. He ◽  
Z. Xie
Keyword(s):  
Asymmetric Catalysis ◽  
Induction Effect ◽  
Chiral Induction

scholarly journals X-SDD: A New Benchmark for Hot Rolled Steel Strip Surface Defects Detection

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 706
Author(s):  
Xinglong Feng ◽  
Xianwen Gao ◽  
Ling Luo
Keyword(s):  
Surface Defect ◽  
Surface Defects ◽  
Steel Strip ◽  
Rolled Strip ◽  
Rolled Steel ◽  
Strip Surface ◽  
Hot Rolled Strip ◽  
Hot Rolled ◽  
Hot Rolled Steel ◽  
Detection Of Defects

It is important to accurately classify the defects in hot rolled steel strip since the detection of defects in hot rolled steel strip is closely related to the quality of the final product. The lack of actual hot-rolled strip defect data sets currently limits further research on the classification of hot-rolled strip defects to some extent. In real production, the convolutional neural network (CNN)-based algorithm has some difficulties, for example, the algorithm is not particularly accurate in classifying some uncommon defects. Therefore, further research is needed on how to apply deep learning to the actual detection of defects on the surface of hot rolled steel strip. In this paper, we proposed a hot rolled steel strip defect dataset called Xsteel surface defect dataset (X-SDD) which contains seven typical types of hot rolled strip defects with a total of 1360 defect images. Compared with the six defect types of the commonly used NEU surface defect database (NEU-CLS), our proposed X-SDD contains more types. Then, we adopt the newly proposed RepVGG algorithm and combine it with the spatial attention (SA) mechanism to verify the effect on the X-SDD. Finally, we apply multiple algorithms to test on our proposed X-SDD to provide the corresponding benchmarks. The test results show that our algorithm achieves an accuracy of 95.10% on the testset, which exceeds other comparable algorithms by a large margin. Meanwhile, our algorithm achieves the best results in Macro-Precision, Macro-Recall and Macro-F1-score metrics.

scholarly journals Semi-analytical solutions of seismo-electromagnetic signals arising from the motional induction in 3-D multi-layered media: part II—numerical investigations

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Hengxin Ren ◽  
Ling Zeng ◽  
Yao-Chong Sun ◽  
Ken’ichi Yamazaki ◽  
Qinghua Huang ◽  
...  
Keyword(s):  
Point Source ◽  
Numerical Computation ◽  
Wave Velocity ◽  
Excellent Agreement ◽  
Seismic Waves ◽  
Averaging Method ◽  
Ground Surface ◽  
Induction Effect ◽  
Motional Induction ◽  
Electromagnetic Signals

AbstractIn this paper, numerical computations are carried out to investigate the seismo-electromagnetic signals arising from the motional induction effect due to an earthquake source embedded in 3-D multi-layered media. First, our numerical computation approach that combines discrete wavenumber method, peak-trough averaging method, and point source stacking method is introduced in detail. The peak-trough averaging method helps overcome the slow convergence problem, which occurs when the source–receiver depth difference is small, allowing us to consider any focus depth. The point source stacking method is used to deal with a finite fault. Later, an excellent agreement between our method and the curvilinear grid finite-difference method for the seismic wave solutions is found, which to a certain degree verifies the validity of our method. Thereafter, numerical computation results of an air–solid two-layer model show that both a receiver below and another one above the ground surface will record electromagnetic (EM) signals showing up at the same time as seismic waves, that is, the so-called coseismic EM signals. These results suggest that the in-air coseismic magnetic signals reported previously, which were recorded by induction coils hung on trees, can be explained by the motional induction effect or maybe other seismo-electromagnetic coupling mechanisms. Further investigations of wave-field snapshots and theoretical analysis suggest that the seismic-to-EM conversion caused by the motional induction effect will give birth to evanescent EM waves when seismic waves arrive at an interface with an incident angle greater than the critical angle θc = arcsin(Vsei/Vem), where Vsei and Vem are seismic wave velocity and EM wave velocity, respectively. The computed EM signals in air are found to have an excellent agreement with the theoretically predicted amplitude decay characteristic for a single frequency and single wavenumber. The evanescent EM waves originating from a subsurface interface of conductivity contrast will contribute to the coseismic EM signals. Thus, the conductivity at depth will affect the coseismic EM signals recorded nearby the ground surface. Finally, a fault rupture spreading to the ground surface, an unexamined case in previous numerical computations of seismo-electromagnetic signals, is considered. The computation results once again indicate the motional induction effect can contribute to the coseismic EM signals.

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