scholarly journals Numerical Verification of Dielectric Contactor as Auxiliary Loads for Measuring the Multi-Port Network Parameter of Vertical Interconnection Array

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 117997-118004
Author(s):  
Byungjin Bae ◽  
Jingook Kim ◽  
Ki Jin Han
Keyword(s):  
Numerical Verification ◽  
Network Parameter ◽  
Vertical Interconnection

60-GHz Band Copper Ball Vertical Interconnection for MMW 3-D System-in-Package Front-End Modules

2012 ◽  
Vol E95.C (7) ◽  
pp. 1276-1284 ◽  
Author(s):  
Satoshi YOSHIDA ◽  
Shoichi TANIFUJI ◽  
Suguru KAMEDA ◽  
Noriharu SUEMATSU ◽  
Tadashi TAKAGI ◽  
...  
Keyword(s):  
60 Ghz ◽  
Front End ◽  
Vertical Interconnection

Acoustic and Photoacoustic Inspection of Through-Silicon Vias in the GHz-Frequency Band

2017 ◽  
Author(s):  
Sebastian Brand ◽  
Michael Kögel ◽  
Frank Altmann ◽  
Ingrid DeWolf ◽  
Ahmad Khaled ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Frequency Band ◽  
Process Development ◽  
Three Dimensional ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Vertical Interconnection ◽  
Testing Techniques ◽  
Silicon Vias ◽  
Non Destructive

Abstract Through Silicon Via (TSV) is the most promising technology for vertical interconnection in novel three-dimensional chip architectures. Reliability and quality assessment necessary for process development and manufacturing require appropriate non-destructive testing techniques to detect cracks and delamination defects with sufficient penetration and imaging capabilities. The current paper presents the application of two acoustically based methods operating in the GHz-frequency band for the assessment of the integrity of TSV structures.

scholarly journals Sensitivity-informed Bayesian Inference for Home PLC Network Models with Unknown Parameters

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2402
Author(s):  
David S. Ching ◽  
Cosmin Safta ◽  
Thomas A. Reichardt
Keyword(s):  
Bayesian Inference ◽  
Transfer Function ◽  
Network Topology ◽  
Random Search ◽  
Network Models ◽  
Training Data ◽  
Unknown Parameters ◽  
Network Parameter ◽  
Dimensional Parameter ◽  
Discrete Random Variables

Bayesian inference is used to calibrate a bottom-up home PLC network model with unknown loads and wires at frequencies up to 30 MHz. A network topology with over 50 parameters is calibrated using global sensitivity analysis and transitional Markov Chain Monte Carlo (TMCMC). The sensitivity-informed Bayesian inference computes Sobol indices for each network parameter and applies TMCMC to calibrate the most sensitive parameters for a given network topology. A greedy random search with TMCMC is used to refine the discrete random variables of the network. This results in a model that can accurately compute the transfer function despite noisy training data and a high dimensional parameter space. The model is able to infer some parameters of the network used to produce the training data, and accurately computes the transfer function under extrapolative scenarios.

Experimental study and numerical verification of silted-up dam break

2020 ◽  
Vol 590 ◽  
pp. 125267
Author(s):  
Foad Vosoughi ◽  
Gholamreza Rakhshandehroo ◽  
Mohammad Reza Nikoo ◽  
Mojtaba Sadegh
Keyword(s):  
Experimental Study ◽  
Dam Break ◽  
Numerical Verification
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