The use of C-V techniques to investigate instability mechanisms in M-I-S structures

In the field of flat panel displays, the current leading technology is the Active Matrix liquid Crystal Display; this uses a-Si:H based thin film transistors (TFTs) as the switching element in each pixel. However, under gate bias a-Si:H TFTs suffer from instability, as is evidenced by a shift in the gate threshold voltage. The shift in the gate threshold voltage is generally measured from the gate transfer characteristics, after subjecting the TFT to prolonged gate bias. However, a major drawback of this measurement method is that it cannot distinguish whether the shift is caused by the change in the midgap states in the a-Si:H channel or by charge trapping in the gate insulator. In view of this, we have developed a capacitance-voltage (C-V) method to measure the shift in threshold voltage. We employ Metal-Insulator-Semiconductor (MIS) structures to investigate the threshold voltage shift as they are simpler to fabricate than TFTs. We have investigated a large of number Metal/a-Si:H/Si3N4/Si+n structures using our C-V technique. From, the C-V data for the MIS structures, we have found that the relationship between the thermal energy and threshold voltage shift is similar to that reported by Wehrspohn et. al in a-Si:H TFTs (J Appl. Phys, 144, 87, 2000). The a-Si:H and Si3N4 layers were grown using the radio-frequency plasma-enhanced chemical vapour deposition technique.

Different mechanisms involved in adaptation to stable and unstable dynamics.

Recently, we demonstrated that humans can learn to make accurate movements in an unstable environment by controlling magnitude, shape, and orientation of the endpoint impedance. Although previous studies of human motor learning suggest that the brain acquires an inverse dynamics model of the novel environment, it is not known whether this control mechanism is operative in unstable environments. We compared learning of multijoint arm movements in a "velocity-dependent force field" (VF), which interacted with the arm in a stable manner, and learning in a "divergent force field" (DF), where the interaction was unstable. The characteristics of error evolution were markedly different in the 2 fields. The direction of trajectory error in the DF alternated to the left and right during the early stage of learning; that is, signed error was inconsistent from movement to movement and could not have guided learning of an inverse dynamics model. This contrasted sharply with trajectory error in the VF, which was initially biased and decayed in a manner that was consistent with rapid feedback error learning. EMG recorded before and after learning in the DF and VF are also consistent with different learning and control mechanisms for adapting to stable and unstable dynamics, that is, inverse dynamics model formation and impedance control. We also investigated adaptation to a rotated DF to examine the interplay between inverse dynamics model formation and impedance control. Our results suggest that an inverse dynamics model can function in parallel with an impedance controller to compensate for consistent perturbing force in unstable environments.

Single trial learning of external dynamics: What can the brain teach us about learning mechanisms?

Humans have exceptional abilities to learn new skills, manipulate tools and objects, and interact with our environment. In order to be successful at these tasks, our brain has developed learning mechanisms to deal with and compensate for the constantly changing dynamics of the world. If this mechanism or mechanisms can be understood from a computational point of view, then they can also be used to drive the adaptability and learning of robots. In this paper, we will present a new technique for examining changes in the feedforward motor command due to adaptation. This technique can then be utilized for examining motor adaptation in humans and determining a computational algorithm which explains motor learning. © 2007.

Integrated billing mechanisms in the Internet of Things to support information sharing and enable new business opportunities

The Internet of Things (IOT) concept and enabling technologies such as RFID offer the prospect of linking the real world of physical objects with the virtual world of information technology to improve visibility and traceability information within supply chains and across the entire lifecycles of products, as well as enabling more intuitive interactions and greater automation possibilities. There is a huge potential for savings through process optimization and profit generation within the IOT, but the sharing of financial benefits across companies remains an unsolved issue. Existing approaches towards sharing of costs and benefits have failed to scale so far. The integration of payment solutions into the IOT architecture could solve this problem. We have reviewed different possible levels of integration. Multiple payment solutions have been researched. Finally we have developed a model that meets the requirements of the IOT in relation to openness and scalability. It supports both hardware-centric and software-centric approaches to integration of payment solutions with the IOT. Different requirements concerning payment solutions within the IOT have been defined and considered in the proposed model. Possible solution providers include telcos, e-payment service providers and new players such as banks and standardization bodies. The proposed model of integrating the Internet of Things with payment solutions will lower the barrier to invoicing for the more granular visibility information generated using the IOT. Thus, it has the potential to enable recovery of the necessary investments in IOT infrastructure and accelerate adoption of the IOT, especially for projects that are only viable when multiple benefits throughout the supply chain need to be accumulated in order to achieve a Return on Investment (ROI). In a long-term perspective, it may enable IT-departments to become profit centres instead of cost centres. © 2010 - IOS Press and the authors. All rights reserved.

Computational mechanisms of sensorimotor control.

In order to generate skilled and efficient actions, the motor system must find solutions to several problems inherent in sensorimotor control, including nonlinearity, nonstationarity, delays, redundancy, uncertainty, and noise. We review these problems and five computational mechanisms that the brain may use to limit their deleterious effects: optimal feedback control, impedance control, predictive control, Bayesian decision theory, and sensorimotor learning. Together, these computational mechanisms allow skilled and fluent sensorimotor behavior.

Acceleration of an unstructured hybrid mesh RANS solver by porting to GPU architectures

The modern CFD process consists of mesh generation, flow solving and post-processing integrated into an automated workflow. During the last several years we have developed and published research aimed at producing a meshing and geometry editing system, implemented in an end-to-end parallel, scalable manner and capable of automatic handling of large scale, real world applications. The particular focus of this paper is the associated unstructured mesh RANS flow solver and the porting of it to GPU architectures. After briefly describing the solver itself, the special issues associated with porting codes using unstructured data structures are discussed - followed by some application examples. Copyright © 2011 by W.N. Dawes.

Metastability mechanisms in thin film transistors quantitatively resolved using post-stress relaxation of threshold voltage

A new approach is presented to resolve bias-induced metastability mechanisms in hydrogenated amorphous silicon (a-Si:H) thin film transistors (TFTs). The post stress relaxation of threshold voltage (V(T)) was employed to quantitatively distinguish between the charge trapping process in gate dielectric and defect state creation in active layer of transistor. The kinetics of the charge de-trapping from the SiN traps is analytically modeled and a Gaussian distribution of gap states is extracted for the SiN. Indeed, the relaxation in V(T) is in good agreement with the theory underlying the kinetics of charge de-trapping from gate dielectric. For the TFTs used in this work, the charge trapping in the SiN gate dielectric is shown to be the dominant metastability mechanism even at bias stress levels as low as 10 V.