We executed an electronic conductance code, TRANSEC, on the supercomputing cluster Hyak to perform real-space atomic-scale electronic transport calculations. TRANSEC is based on the Density Functional Theory (DFT) code PARSEC, with the added capability of computing conductance in nano-scale chemical structures. We used DFT to predict the behavior of valence electrons in these structures with first-principles quantum mechanical computations. We then computed the quantum mechanical electron transmission probability through these structures in order to predict their conductance. Furthermore, the highly parallelizable nature of TRANSEC enables efficient large-scale calculations, thereby reducing computation time. We anticipate this research will result in an enhanced understanding of nano-scale devices relevant to electronics and semiconductor technology.

For investigations into nanoscopic properties of matter, computational simulation is a key tool. In particular, simulations of the electronic configuration and conductance of nanoscale devices facilitate the continued miniaturization of semiconductor devices used in integrated circuits and computer processors. However, due to the importance of quantum mechanics at these scales, accurate calculations can be highly costly. In our research we consider improvements to one such parallelizable electronic transport code, TRANSEC. We seek to better understand how a Monte Carlo technique, combined with a special polynomial expansion, may improve the scaling of TRANSEC’s computing time with the size of the simulation. We apply the Monte Carlo technique within a simplified tight-binding model of a TRANSEC calculation. We test how the Monte Carlo technique facilitates the calculation of the electronic transmission probability, given an initial Hamiltonian energy matrix along with absorbing boundary conditions (referred to as complex absorbing potentials, or CAPs). We expect that the results of this study may provide algorithms which allow for faster calculation times when integrated at scale into TRANSEC. Improvements to computing time for determining parameters such as nanoscopic conduction helps to advance computer modeling of nanoscopic structures (such as transistors, interconnect, or molecular electronics), which could benefit semiconductor technology.

 Nano-devices, nanoscale metals, and other molecular-scale electronics are key components in most modern electronics, and the semiconductor industry is continuously looking for ways to improve on existing technology. Innovation of this kind can be aided by computer simulations, such as electronic conductance simulations, which can be used to predict the performance of prospective devices without manufacturing a prototype, or for explanatory modeling of existing devices. To simulate open boundary conditions in a finite space, one such electronic conductance code, TRANSEC, uses complex absorbing potentials (CAPs) to keep electrons that interact with the simulation boundaries from reflecting and interfering with results. These CAPs take the form of complex-valued functions placed at the boundaries of the simulation, and must be tuned to successfully absorb electrons. The larger the CAP width is, the more space needs to be simulated to accommodate it, which increases computing time. The goal of our research has been to optimize the CAP form and volume so as to reduce the CAP’s impact on computing time. To do this, we have used a simplified tight-binding model of an electronic transport calculation, allowing us to efficiently perform evaluations of CAP accuracy for numerous CAP forms and widths. Our results thus far indicate that the gaussian CAP form performs at least as well as monomial forms of order 1 through 5, and that CAP width can be decreased significantly by increasing the CAP height parameter. This research pertains particularly to the existing electronic transport code, TRANSEC, as well as to other approaches that make use of CAPs.