Flash-lamp annealing of semiconductor materials—Applications and process models

Flash-lamp annealing (FLA) on a millisecond time scale has been shown to be a promising tool in the preparation of high-quality semiconducting materials. The process imposes time varying through-thickness temperature profiles on the substrates being processed, and consequently thermal stresses. A combined thermal and optical model has been developed to predict the substrate temperature distribution and this model has been linked to a structural model to compute stresses and deflections. The paper shows how these models can be used to explore process conditions in flash lamp annealing, with particular regard to the annealing of ion implants in silicon and the crystallization of amorphous silicon layers on glass substrates.     

N-Type Field-Effect Transistors Using Multiple Mg-Doped ZnO Nanorods

Nanorod field-effect transistors (FETs) that use multiple Mg-doped ZnO nanorods and a SiO₂ gate insulator were fabricated and characterized. The use of multiple nanorods provides higher on-currents without significant degradation in threshold voltage shift and subthreshold slopes. It has been observed that the on-currents of the multiple ZnO nanorod FETs increase approximately linearly with the number of nanorods, with on-currents of ~1 muA per nanorod and little change in off-current (~4times10^-12). The subthreshold slopes and on-off ratios typically improve as the number of nanorods within the device channel is increased, reflecting good uniformity of properties from nanorod to nanorod. It is expected that Mg dopants contribute to high n-type semiconductor characteristics during ZnO nanorod growth. For comparison, nonintentionally doped ZnO nanorod FETs are fabricated, and show low conductivity to compare with Mg-doped ZnO nanorods. In addition, temperature-dependent current-voltage characteristics of single ZnO nanorod FETs indicate that the activation energy of the drain current is very low (0.05-0.16 eV) at gate voltages both above and below threshold     

Multishell Carrier Transport in Multiwalled Carbon Nanotubes

Understanding carrier transport in carbon nanotubes (CNTs) and their networks is important for harnessing CNTs for device applications. Here, we report multishell carrier transport in individual multiwalled CNTs, and films of randomly dispersed multiwalled CNTs, as a function of electric field and temperature. Electrical measurements and first-principles density functional theory calculations indicate transport across CNT shells. Intershell conduction occurs across an energy barrier range of 60-250 meV in individual CNTs, and ~ 60 meV in CNT networks. In both cases, the conductance behavior can be explained based upon field-enhanced carrier injection and defect-enhanced transport, as described by the Poole-Frenkel model.     

Vehicle Localization Using Sensors Data Fusion Via Integration of Covariance Intersection and Interval Analysis

Achieving an innovative integrated sensor fusion architecture with a robust vehicle navigation and localization using an extended Kalman filter, interval analysis and covariance intersection that can overcome the uncertainty in the system model and sensor noise statistics. There are various approaches to the problem, but here the focus is on an approach which can guaranteed performance of sensor-based navigation. The guaranteed performance is quantified by explicit bounds of position estimate of a ground vehicle. Ground vehicles generally carry dead reckoning sensors such as wheel encoders and inertial sensors, to measure acceleration and angle rate, while obstacle detection and mapmaking is done with time-of-flight ultrasonic sensors. Most of these sensors give overlapping or complementary information and sometimes are redundant as well, which offers scope for exploiting data fusion. The purpose here is to achieve data fusion for ground vehicles with low-cost sensors by forming an intelligent sensor system. This is accomplished by combining the sensors' measurements and processing these measurements with data fusion algorithms. The algorithms are complementary in the sense that they compensate for each other's limitations, so that the resulting performance of the sensor system is better than its individual components.     

New Noncontacting Inductive Analog Proximity and Inductive Linear Displacement Sensors for Industrial Automation

Noncontacting inductive sensors are applicable on a large scale for position detection or travel measurement in industrial applications. Reasons for such broad acceptance in many sectors of industry are noncontact and wear-free sensing of the target (any metal object), reliability and robustness, resistance to fouling, water tightness and compact size. The present work is intended to be a systematic, complete, and consistent presentation of the technological innovations, recent implementations and current trends regarding the analog distance and travel sensing offered by noncontacting inductive sensors for industrial applications. It starts with the fundamentals of inductive sensing and presents the physical basics gained by modern analytic and simulation methods, as well as high-level integrated circuits for inductive sensors. The following sections deal with present-day inductive analog proximity sensors and with the distinctive technological innovation offered by the new inductive linear displacement sensors and with miniaturization results achieved through consistent integration.