In the development of materials for nano-scale devices, particularly those including impurities, defects, surfaces, and hetero-interfaces, estimation of their electrical conductivity based on quantum chemistry provides important information. Existing quantum mechanics-based methods for theoretically estimating the electrical conductivity require huge computational cost, which prohibit their applications to complex systems. We have already succeeded in the development of our original tight-binding quantum chemical molecular dynamics program “Colors”, which realizes a calculation speed over 5000 times faster than the conventional first principles molecular dynamics methods. Here we present a novel electrical conductivity estimation method based on “Colors” combined with Monte Carlo algorithm for estimating the value corresponding to the carrier mobility. We have applied the developed method to systems of C, Si, Ge, and Sn. Calculated results quantitatively agree with experimental data, which indicates the validity of the developed method. We have also applied the developed method to the study of Si surface.

Robustness, versatility, high level of performance and low cost are some of the characteristics that make capacitive sensors well suited for industrial applications. They consist only in electrodes and measurement circuits but give access to position information and/or material properties. The design of capacitive sensors is however not so obvious so that simple structures are generally used to avoid time-consuming calculations and developments. We propose an analytical method to determine the sensitivity distribution of any capacitive sensor structure. This method makes it possible to rapidly optimize structures in order to increase the sensor sensitivity to one parameter or to render it less sensitive to another. Comparisons between the sensitivity map of known sensors and those obtained with the analytical method proposed in this paper show a great accordance.

We present a novel study on electromigration (EM) phenomena in Cu interconnects using a newly developed multi-scale simulator that consists on a combination of a device scale simulator based on a kinetic Monte Carlo (KMC) method and an atomic scale simulator based on ultra accelerated quantum chemical molecular dynamics (UA-QCMD). We have firstly demonstrated the simulation of the lifetime of Cu interconnects using the newly developed device scale simulator setting some suitable KMC probabilities for the void movement according to the regions in which it can be divided, i.e., the crystal grain and the grain boundary. The simulated values are shown to be in good agreement with experimental values. In an attempt to connect the device scale studies to quantum chemical instances of the system -since the correlation of probability of the void movement with, for example, activation energies or diffusion coefficients is important -we have developed an atomic scale simulator based on our original UA-QCMD method. In this atomic scale simulation, the electron wind force was evaluated using our original electrical conductivity prediction simulator based on KMC method which uses the electronic states from tight-binding quantum chemical (TBQC) calculation. Using this atomic scale simulator under the conditions of 475 K of temperature and 2:5 Â 10 10 A/m 2 of current density, we were able to successfully simulate the migration of a Cu atom from a lattice site to a vacant site by evaluating the electron wind force.

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