This letter presents a step-mode voltammetry method which uses ion diffusivity to characterize pore structure in both dense and porous low dielectric constant materials (low k) in patterned interconnect structures. Findings reveal that the intramolecular space in dense low k acts like a small physical pore network. It is determined that electrolyte ions can migrate through such space in dense low k, but with higher activation energy than in porous low k or the bulk solution, 0.31eV vs 0.18–0.19eV. Also, this study finds that the pores in ultralow k are not stable but can either coalesce or collapse depending on stress conditions.

The establishment of confined concrete strength is an important issue in fiber reinforced polymer (FRP)-confined concrete column. This paper explores the use of Radial Basis Function Neural Network(RBFNN) in predicting the confinedment efficiency of FRP-confined concrete. Based on 362 experimental datas, the RBFNN model with highly non-linear reflection relationship was found and tested by the experimental data. A comparison study between the RBFNN model and four well-known models is carried out, it was found that the RBFNN model could reasonably capture the underlying behavior of FRP-confined concrete and provide better results than other models. The sensitivity analysis of the influential factor is also discussed, it shows that RBFNN-based modeling is a practical method for predicting the confinement efficiency of FRP-confined concrete.

Because the ambient excitation is difficult to test, it is necessary to study the damage detection method only with structure responses. In this paper, two node structure responses under white noise are used to calculate the virtual impulse response function, the amplitude of the virtual impulse response function is decomposed by wavelet packet to calculate the node energy. The wavelet packet node energy change pre and post damage is used as the damage characteristic vector, and the pattern classification function of BP neural network is employed to determine the structure damage location. The numerical simulation and model experiment results of the offshore platform show the effectives of the method, whereas which is easy to be influenced by the noises.

The electrochemical mechanism behind voltammograms produced by defective barriers in low-k/Cu interconnect structures is investigated using simulation cells which mimic various barrier conditions. The findings reveal that the Cu reaction peak current used as an indicator for barrier defects represents oxidation of Cu at the anodic electrode. When both electrodes have similar defect (Cu) fraction, the voltammogram is symmetric, but when the electrodes have different Cu fraction, the voltammogram is asymmetric and changes during repeated cycling until a stable form is reached.

When the AR model is used to identify the structural damage, one problem is often met, that is the method can only make a decision whether the structure is damaged, however, the damage location can not be identified exactly. A structural damage localization method based on AR model in combination with BP neural network is proposed in this paper. The AR time series models are used to describe the acceleration responses. The changes of the first 3-order AR model parameters are extracted and composed as damage characteristic vectors which are put into BP neural network to identify the damage location. The effectiveness of the method is validated by the results of numerical simulation and experiment for a four-layer offshore platform. Only the acceleration responses can be used adequately to localize the structural damage, without the usage of modal parameter and excitation force. Thus the dependence on the modal parameter and excitation can be avoided in this method.