Modelling of advanced submicron gate InGaAs/InAIAs pHEMTS and RTD devices for very high frequency applications

InP based InAlAs/InGaAs pseudomorphic High Electron Mobility Transistors (pHEMTs) have shown outstanding performances, which makes them prominent in high frequency mm-wave and submillimeter-wave applications. However, conventional InGaAs/InAlAs pHEMTs have major drawbacks, i.e., very low breakdown voltage and high gate leakage current. These disadvantages degrade device performance, especially in Monolithic Microwave Integrated Circuit (MMIC) low noise amplifiers (LNAs). The optimisation of InAlAs/InGaAs epilayer structures through advanced bandgap engineering together with gate length reduction from 1 m into deep sub-μm regime is the key solution to enabled high breakdown and ultra-high speed, low noise pHEMT devices to be fabricated. Concurrently, device modelling plays a vital role in the design and analysis of pHEMT device and circuit performance. Physical modeling becomes essential to fully characterise and understand the underlying physical phenomenon of the device, while empirical modelling is significant in circuit design and predicts device’s characteristic performance. In this research, the main objectives to accurately model the DC and RF characteristics of the two-dimensional (2D) physical modelling for sub-μm gate length for strained channel InAlAs/InGaAs/InP pHEMT has been accomplished and developed in ATLAS Silvaco. All modelled devices were optimised and validated by experimental devices which were fabricated at the University of Manchester; the sub-micrometer devices were developed with T-gate using I-line optical lithography. The underlying device physics insight are gained, i.e, the effects of changes to the device’s physical structure, theoretical concepts and its general operation, hence a reliable pHEMT model is obtained. The kink anomalies in I-V characteristics was reproduced and the 2D simulation results demonstrate an outstanding agreement with measured DC and RF characteristics. The aims to develop linear and nonlinear models for sub-μm transistors and their implementation in MMIC LNA design is achieved with the 0.25 m In0.7Ga0.3As/In0.52Al0.48As/InP pHEMT. An accurate technique for the extraction of empirical models for the fabricated active devices has been developed and optimised using Advance Design System (ADS) software which demonstrate excellent agreement between experimental and modelled DC and RF data. A precise models for MMIC passive devices have also been obtained and incorporated in the proposed design for a single and double stage MMIC LNAs in C- and X-band frequency. The single stage LNA is designed to achieve maximum gain ranging from 9 to 13 dB over the band of operation while the gain is increased between 20 dB and 26 dB for the double stage LNA designs. A noise figure of less than 1.2 dB and 2 dB is expected respectively, for the C- and X-band LNA designed while retaining stability across the entire frequency bands. Although the RF performance of pHEMT is being vigorously pushed towards terahertz region, novel devices such as Resonant Tunnelling Diode (RTD) are needed to support future ultra-high speed, high frequency applications especially when it comes to THz frequencies. Hence, the study of physical modelling is extended to quantum modelling of an advanced In0.8Ga0.2As/AlAs RTD device to effectively model both large size and submicron RTD using Silvaco’s ATLAS software to reproduce the peak current density, peak-to-valley-current ratio (PVCR), and negative differential resistance (NDR) voltage range. The simple one-dimensional physical modelling for the RTD devices is optimised to achieve an excellent match with the fabricated RTD devices with variations in the spacer thickness, barrier thickness, quantum well thickness and doping concentration

Exploiting Map Topology Knowledge for Context-predictive Multi-interface Car-to-cloud Communication

While the automotive industry is currently facing a contest among different communication technologies and paradigms about predominance in the connected vehicles sector, the diversity of the various application requirements makes it unlikely that a single technology will be able to fulfill all given demands. Instead, the joint usage of multiple communication technologies seems to be a promising candidate that allows benefiting from characteristical strengths (e.g., using low latency direct communication for safety-related messaging). Consequently, dynamic network interface selection has become a field of scientific interest. In this paper, we present a cross-layer approach for context-aware transmission of vehicular sensor data that exploits mobility control knowledge for scheduling the transmission time with respect to the anticipated channel conditions for the corresponding communication technology. The proposed multi-interface transmission scheme is evaluated in a comprehensive simulation study, where it is able to achieve significant improvements in data rate and reliability

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

Empirical exploration of air traffic and human dynamics in terminal airspaces

Air traffic is widely known as a complex, task-critical techno-social system, with numerous interactions between airspace, procedures, aircraft and air traffic controllers. In order to develop and deploy high-level operational concepts and automation systems scientifically and effectively, it is essential to conduct an in-depth investigation on the intrinsic traffic-human dynamics and characteristics, which is not widely seen in the literature. To fill this gap, we propose a multi-layer network to model and analyze air traffic systems. A Route-based Airspace Network (RAN) and Flight Trajectory Network (FTN) encapsulate critical physical and operational characteristics; an Integrated Flow-Driven Network (IFDN) and Interrelated Conflict-Communication Network (ICCN) are formulated to represent air traffic flow transmissions and intervention from air traffic controllers, respectively. Furthermore, a set of analytical metrics including network variables, complex network attributes, controllers' cognitive complexity, and chaotic metrics are introduced and applied in a case study of Guangzhou terminal airspace. Empirical results show the existence of fundamental diagram and macroscopic fundamental diagram at the route, sector and terminal levels. Moreover, the dynamics and underlying mechanisms of "ATCOs-flow" interactions are revealed and interpreted by adaptive meta-cognition strategies based on network analysis of the ICCN. Finally, at the system level, chaos is identified in conflict system and human behavioral system when traffic switch to the semi-stable or congested phase. This study offers analytical tools for understanding the complex human-flow interactions at potentially a broad range of air traffic systems, and underpins future developments and automation of intelligent air traffic management systems.Comment: 30 pages, 28 figures, currently under revie