We have studied electromiyation (EM) and stress-induced voiding (SV) behaviors based on our 90nm-node Cuilow-k interconnect processes, and demonstrated successful improvement of the interconnect reliability. In EM study wide bimodal failure distribution was found only in the particular EM test structure. We identified that it caused by the lack of wettability between Cu and the barrier metal in the vias, and demonstrated that the ,optimization of the barrier metal thickness could suppress it. In SV behavior, we revealed a mechanism of the voiding under the vias that was due to the initial existence of the nuclei of the void before high temperature storage test. The failure mode was suppressed by optimizing preheat temperature of M2 barrier metal deposition.interconnects for 90nm-node system LSIs. Dimensions of both minimum line width and space are 120nm for M I, and 140nm for M2 to M5 with minimum via size of 140ntn. Here, the via-first trench-last Cu dual-damascene process was applied with low-k dielectrics patterned by high NA ArF lithography. Dielectrics structures were Si02iSiC for the MI inna-level and the SiOCiSi02 hybrid sttucture with S i c diffusion barrier dielectric for the M2 to M5 intra-and inter-level. The electrochemical deposited (ECE) Cu on physical vapor deposited (PVD) Cu seed that lies on Td?
Sintering is a fundamental technology for powder metallurgy, the ceramics industry, and additive manufacturing processes such as three-dimensional printing. Improvement of the properties of sintered materials requires prediction of their microstructure using numerical simulations. However, the physical values and material parameters used for such predictions are generally unknown. Data assimilation (DA) enables the estimation of unobserved states and unknown material parameters by integrating simulation results and observational data. In this paper, we develop a new model that couples an ensemble-based four-dimensional variational (En4DVar) DA with a phase-field model of solid-state sintering (En4DVar-PF model) to estimate the state of the sintered material and multiple unknown material parameters. The developed En4DVar-PF model is validated by numerical experiments called twin experiments, in which a priori assumed-true initial state and multiple material parameters are estimated. The results of the twin experiments demonstrate that, using only three-dimensional morphological data of the sintered microstructure, our developed En4DVar-PF model can simultaneously and accurately estimate the particle shape, distribution of grain boundaries, and material parameters, including diffusion coefficients and mobilities related to grain boundary migration. Furthermore, our work identifies criteria for determining appropriate DA conditions such as the observational time interval required to accurately estimate the material parameters using our developed model. The developed En4DVar-PF model provides a promising framework to obtain unobservable states and difficult-to-measure material parameters in sintering, which is crucial for the accurate prediction of sintering processes and for the development of superior materials.
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