Multi-Scale Modeling of Tunneling in Nanoscale Atomically Precise Si:P Tunnel Junctions

Controlling electron tunneling is of fundamental importance in the design and operation of semiconductor nanostructures such as field effect transistors (FETs) and quantum computing device architectures. The exponential sensitivity of tunneling with distance requires precise fabrication techniques to engineer the desired device dimensions to achieve the appropriate tunneling resistances/tunnel rates. This is particularly important for high fidelity spin readout and qubit exchange in quantum computing architectures. Here, it is shown by combining precision fabrication techniques with accurate atomistic modeling, predictive device design criteria are achieved at atomic length scales. Such a tool is useful when devices become more complex or have arbitrary shapes/geometries. In particular, in this study, atomic precision patterning of monolayer degenerately phosphorus-doped silicon tunnel junctions patterned by scanning tunnelling microscopy lithography and tight-binding non-equilibrium Green's function (TB-NEGF) modeling is combined to describe the dependence of tunnel junction resistance R_T on junction length. An agreement with experiment to within a factor of 2 over 4 orders of magnitude in R_T is found, and this model allows to accurately determine the barrier height V₀ = 57.5 ± 1 meV and lateral seam s_xy = 0.39 ± 0.01 nm in these nanoscale junctions. This study confirms the use of the TB-NEGF formalism to accurately model highly doped atomically precise tunnel junctions in silicon. Further applications of this model will enable improved device performance at the nanoscale.     

Flow regime identification in aerated stirred vessel using passive acoustic emission and machine learning

Smart at- or online process sensors, which employ machine learning (ML) to process data, have been the subject of extensive research in recent years, due to their potential for real-time process control. In this paper, a passive acoustic emission process sensor has been used to detect gas–liquid regimes within a stirred, aerated vessel using novel ML approaches. Pressure fluctuations (acoustic emissions) in an air-water system were recorded using a piezoelectric sensor installed on the external wall of three identical cylindrical tanks of diameter, T = 160 mm, filled to a volume of 5 L (height, H = 1.5 T). The tanks were made of either glass, steel, or aluminium, and each tank was equipped with a Rushton turbine of diameter, D = 0.35 T. The investigated flow regimes, flooding, loading, complete dispersion, and un-gassed, were obtained by changing the air feed flow rates and by varying the impeller speed. The acoustic spectra obtained were processed to select an optimal number of features characterizing each of the regimes, and these were used to train three different ML algorithms. The pre-processing includes a principal component analysis (PCA) step, which reduces the volume of data fed to the ML algorithms, saving computational time up to a factor of 5. The algorithms (decision tree, k-nearest neighbour, and support vector machines) were challenged to use these features to identify the correct flow regime. Accurate predictions of the three gas–liquid regimes of interest have been achieved. The accuracy of the prediction ranges from 90% to 99%, and this difference is related to the material used for the vessel.     

Current waveform quality from grid‐connected photovoltaic inverters and its dependence on operating conditions

The increasing installation of grid‐connected photovoltaics (PV) in the urban environment will lead to a significant penetration into the low voltage electricity supply network of small power electronic generators. Inevitably some disturbance to the electricity supply quality will result from these embedded generators. It is shown that the inverters used to grid connect PV arrays are susceptible to minor distortion of the network waveform and that this can result in higher levels of current waveform distortion, or harmonic disturbance, being sourced into the supply than would be expected from analyses which assume an ideal voltage waveform. The level of current distortion is shown to be very dependent on the type of inverter control used. Inverter operation is also a function of operating point; clearly a device at part load cannot be expected to deliver the same quality of current waveform as when operating under its rated design conditions. The impedance of the grid connection also has an impact on the inverter's operation. Copyright © 2000 John Wiley & Sons, Ltd.