Effects of alternating current voltage amplitude and oxide capacitance on mid-gap interface state defect density extractions in In0.53Ga 0.47As capacitors

This work looks at the effect on mid-gap interface state defect density estimates for In0.53Ga0.47As semiconductor capacitors when different AC voltage amplitudes are selected for a fixed voltage bias step size (100 mV) during room temperature only electrical characterization. Results are presented for Au/Ni/Al2O3/In0.53Ga0.47As/InP metal–oxide–semiconductor capacitors with (1) n-type and p-type semiconductors, (2) different Al2O3 thicknesses, (3) different In0.53Ga0.47As surface passivation concentrations of ammonium sulphide, and (4) different transfer times to the atomic layer deposition chamber after passivation treatment on the semiconductor surface—thereby demonstrating a cross-section of device characteristics. The authors set out to determine the importance of the AC voltage amplitude selection on the interface state defect density extractions and whether this selection has a combined effect with the oxide capacitance. These capacitors are prototypical of the type of gate oxide material stacks that could form equivalent metal–oxide–semiconductor field-effect transistors beyond the 32 nm technology node. The authors do not attempt to achieve the best scaled equivalent oxide thickness in this work, as our focus is on accurately extracting device properties that will allow the investigation and reduction of interface state defect densities at the high-k/III–V semiconductor interface. The operating voltage for future devices will be reduced, potentially leading to an associated reduction in the AC voltage amplitude, which will force a decrease in the signal-to-noise ratio of electrical responses and could therefore result in less accurate impedance measurements. A concern thus arises regarding the accuracy of the electrical property extractions using such impedance measurements for future devices, particularly in relation to the mid-gap interface state defect density estimated from the conductance method and from the combined high–low frequency capacitance–voltage method. The authors apply a fixed voltage step of 100 mV for all voltage sweep measurements at each AC frequency. Each of these measurements is repeated 15 times for the equidistant AC voltage amplitudes between 10 mV and 150 mV. This provides the desired AC voltage amplitude to step size ratios from 1:10 to 3:2. Our results indicate that, although the selection of the oxide capacitance is important both to the success and accuracy of the extraction method, the mid-gap interface state defect density extractions are not overly sensitive to the AC voltage amplitude employed regardless of what oxide capacitance is used in the extractions, particularly in the range from 50% below the voltage sweep step size to 50% above it. Therefore, the use of larger AC voltage amplitudes in this range to achieve a better signal-to-noise ratio during impedance measurements for future low operating voltage devices will not distort the extracted interface state defect density.

Resist-substrate interface tailoring for generating high density arrays of Ge and Bi2Se3 nanowires by electron beam lithography

The authors report a chemical process to remove the native oxide on Ge and Bi2Se3 crystals, thus facilitating high-resolution electron beam lithography (EBL) on their surfaces using a hydrogen silsesquioxane (HSQ) resist. HSQ offers the highest resolution of all the commercially available EBL resists. However, aqueous HSQ developers such as NaOH and tetramethylammonium hydroxide have thus far prevented the fabrication of high-resolution structures via the direct application of HSQ to Ge and Bi2Se3, due to the solubility of components of their respective native oxides in these strong aqueous bases. Here we provide a route to the generation of ordered, high-resolution, high-density Ge and Bi2Se3 nanostructures with potential applications in microelectronics, thermoelectric, and photonics devices.                         

The object binary interface: C++ objects for evolvable shared class libraries

Object-oriented design and object-oriented languages support the development of independent software components such as class libraries. When using such components, versioning becomes a key issue. While various ad-hoc techniques and coding idioms have been used to provide versioning, all of these techniques have deficiencies - ambiguity, the necessity of recompilation or re-coding, or the loss of binary compatibility of programs. Components from different software vendors are versioned at different times. Maintaining compatibility between versions must be consciously engineered. New technologies such as distributed objects further complicate libraries by requiring multiple implementations of a type simultaneously in a program. This paper describes a new C++ object model called the Shared Object Model for C++ users and a new implementation model called the Object Binary Interface for C++ implementors. These techniques provide a mechanism for allowing multiple implementations of an object in a program. Early analysis of this approach has shown it to have performance broadly comparable to conventional implementations.

Investigation of electrically active defects at the interface of high-k dielectrics and compound semiconductors

As silicon based devices in integrated circuits reach the fundamental limits of dimensional scaling there is growing research interest in the use of high electron mobility channel materials, such as indium gallium arsenide (InGaAs), in conjunction with high dielectric constant (high-k) gate oxides, for Metal-Oxide-Semiconductor Field Effect Transistor (MOSFET) based devices. The motivation for employing high mobility channel materials is to reduce power dissipation in integrated circuits while also providing improved performance. One of the primary challenges to date in the field of III-V semiconductors has been the observation of high levels of defect densities at the high-k/III-V interface, which prevents surface inversion of the semiconductor. The work presented in this PhD thesis details the characterization of MOS devices incorporating high-k dielectrics on III-V semiconductors. The analysis examines the effect of modifying the semiconductor bandgap in MOS structures incorporating InxGa1-xAs (x: 0, 0.15. 0.3, 0.53) layers, the optimization of device passivation procedures designed to reduce interface defect densities, and analysis of such electrically active interface defect states for the high-k/InGaAs system. Devices are characterized primarily through capacitance-voltage (CV) and conductance-voltage (GV) measurements of MOS structures both as a function of frequency and temperature. In particular, the density of electrically active interface states was reduced to the level which allowed the observation of true surface inversion behavior in the In0.53Ga0.47As MOS system. This was achieved by developing an optimized (NH4)2S passivation, minimized air exposure, and atomic layer deposition of an Al2O3 gate oxide. An extraction of activation energies allows discrimination of the mechanisms responsible for the inversion response. Finally a new approach is described to determine the minority carrier generation lifetime and the oxide capacitance in MOS structures. The method is demonstrated for an In0.53Ga0.47As system, but is generally applicable to any MOS structure exhibiting a minority carrier response in inversion.

A next application prediction service using the BaranC framework

Predicting user behaviour enables user assistant services provide personalized services to the users. This requires a comprehensive user model that can be created by monitoring user interactions and activities. BaranC is a framework that performs user interface (UI) monitoring (and collects all associated context data), builds a user model, and supports services that make use of the user model. A prediction service, Next-App, is built to demonstrate the use of the framework and to evaluate the usefulness of such a prediction service. Next-App analyses a user's data, learns patterns, makes a model for a user, and finally predicts, based on the user model and current context, what application(s) the user is likely to want to use. The prediction is pro-active and dynamic, reflecting the current context, and is also dynamic in that it responds to changes in the user model, as might occur over time as a user's habits change. Initial evaluation of Next-App indicates a high-level of satisfaction with the service.