Development of inversion-mode and junctionless Indium-Gallium-Arsenide MOSFETs

This PhD covers the development of planar inversion-mode and junctionless Al2O3/In0.53Ga0.47As metal-oxidesemiconductor field-effect transistors (MOSFETs). An implant activation anneal was developed for the formation of the source and drain (S/D) of the inversionmode MOSFET. Fabricated inversion-mode devices were used as test vehicles to investigate the impact of forming gas annealing (FGA) on device performance. Following FGA, the devices exhibited a subthreshold swing (SS) of 150mV/dec., an ION/IOFF of 104 and the transconductance, drive current and peak effective mobility increased by 29%, 25% and 15%, respectively. An alternative technique, based on the fitting of the measured full-gate capacitance vs gate voltage using a selfconsistent Poisson-Schrödinger solver, was developed to extract the trap energy profile across the full In0.53Ga0.47As bandgap and beyond. A multi-frequency inversion-charge pumping approach was proposed to (1) study the traps located at energy levels aligned with the In0.53Ga0.47As conduction band and (2) separate the trapped charge and mobile charge contributions. The analysis revealed an effective mobility (μeff) peaking at ~2850cm2/V.s for an inversion-charge density (Ninv) = 7*1011cm2 and rapidly decreasing to ~600cm2/V.s for Ninv = 1*1013 cm2, consistent with a μeff limited by surface roughness scattering. Atomic force microscopy measurements confirmed a large surface roughness of 1.95±0.28nm on the In0.53Ga0.47As channel caused by the S/D activation anneal. In order to circumvent the issue relative to S/D formation, a junctionless In0.53Ga0.47As device was developed. A digital etch was used to thin the In0.53Ga0.47As channel and investigate the impact of channel thickness (tInGaAs) on device performance. Scaling of the SS with tInGaAs was observed for tInGaAs going from 24 to 16nm, yielding a SS of 115mV/dec. for tInGaAs = 16nm. Flat-band μeff values of 2130 and 1975cm2/V.s were extracted on devices with tInGaAs of 24 and 20nm, respectively

Mobile cloud application models facilitated by the CPA

This paper describes implementations of two mobile cloud applications, file synchronisation and intensive data processing, using the Context Aware Mobile Cloud Services middleware, and the Cloud Personal Assistant. Both are part of the same mobile cloud project, actively developed and currently at the second version. We describe recent changes to the middleware, along with our experimental results of the two application models. We discuss challenges faced during the development of the middleware and their implications. The paper includes performance analysis of the CPA support for the two applications in respect to existing solutions.

The mobile cloud - more than a cloud

Nearly one billion smart mobile devices are now used for a growing number of tasks, such as browsing the web and accessing online services. In many communities, such devices are becoming the platform of choice for tasks traditionally carried out on a personal computer. However, despite the advances, these devices are still lacking in resources compared to their traditional desktop counterparts. Mobile cloud computing is seen as a new paradigm that can address the resource shortcomings in these devices with the plentiful computing resources of the cloud. This can enable the mobile device to be used for a large range of new applications hosted in the cloud that are too resource demanding to run locally. Bringing these two technologies together presents various difficulties. In this paper, we examine the advantages of the mobile cloud and the new approaches to applications it enables. We present our own solution to create a positive user experience for such applications and describe how it enables these applications.

Mobile cloud contextual awareness with the cloud personal assistant

This paper presents our efforts to bridge the gap between mobile context awareness, and mobile cloud services, using the Cloud Personal Assistant (CPA). The CPA is a part of the Context Aware Mobile Cloud Services (CAMCS) middleware, which we continue to develop. Specifically, we discuss the development and evaluation of the Context Processor component of this middleware. This component collects context data from the mobile devices of users, which is then provided to the CPA of each user, for use with mobile cloud services. We discuss the architecture and implementation of the Context Processor, followed by the evaluation. We introduce context profiles for the CPA, which influence its operation by using different context types. As part of the evaluation, we present two experimental context-aware mobile cloud services to illustrate how the CPA works with user context, and related context profiles, to complete tasks for the user.

Delivering mobile cloud services to the user: description, discovery, and consumption

The mobile cloud computing paradigm can offer relevant and useful services to the users of smart mobile devices. Such public services already exist on the web and in cloud deployments, by implementing common web service standards. However, these services are described by mark-up languages, such as XML, that cannot be comprehended by non-specialists. Furthermore, the lack of common interfaces for related services makes discovery and consumption difficult for both users and software. The problem of service description, discovery, and consumption for the mobile cloud must be addressed to allow users to benefit from these services on mobile devices. This paper introduces our work on a mobile cloud service discovery solution, which is utilised by our mobile cloud middleware, Context Aware Mobile Cloud Services (CAMCS). The aim of our approach is to remove complex mark-up languages from the description and discovery process. By means of the Cloud Personal Assistant (CPA) assigned to each user of CAMCS, relevant mobile cloud services can be discovered and consumed easily by the end user from the mobile device. We present the discovery process, the architecture of our own service registry, and service description structure. CAMCS allows services to be used from the mobile device through a user's CPA, by means of user defined tasks. We present the task model of the CPA enabled by our solution, including automatic tasks, which can perform work for the user without an explicit request.