Abstract: The original aim when creating the Raspberry Pi was to encourage “kids”—pre-university learners—to engage with programming, and to develop an interest in and understanding of programming and computer science concepts. The method to achieve this was to give them their own, low cost computer that they could use to program on, as a replacement for a family PC that often did not allow this option. With the original release, the Raspberry Pi included two programming environments in the standard distribution software: Scratch and IDLE, a Python environment. In this paper, we describe two programming environments that we developed and recently ported and optimised for the Raspberry Pi, Greenfoot and BlueJ, both using the Java programming language. Greenfoot and BlueJ are both now included in the Raspberry Pi standard software distribution, and they differ in many respects from IDLE; they are more graphical, more interactive, more engaging, and illustrate concepts of object orientation more clearly. Thus, they have the potential to support the original aim of the Raspberry Pi by creating a deeper engagement with programming. This paper describes these two environments and how they may be used, and discusses their differences and relationships to the two previously available systems.
Abstract: The purpose of this work was to investigate the validity of Arrhenius accelerated-life testing when applied to gallium nitride (GaN) high electron mobility transistors (HEMT) lifetime assessments, where the standard assumption is that only critical stressor is temperature, which is derived from operating power, device channel-case, thermal resistance, and baseplate temperature. We found that power or temperature alone could not explain difference in observed degradation, and that accelerated life tests employed by industry can benefit by considering the impact of accelerating factors besides temperature. Specifically, we found that the voltage used to reach a desired power dissipation is important, and also that temperature acceleration alone or voltage alone (without much power dissipation) is insufficient to assess lifetime at operating conditions.
Abstract: We report on the electron transport properties of two-dimensional electron gas confined in a quaternary barrier InAlGaN/AlN/GaN heterostructure down to cryogenic temperatures for the first time. A state-of-the-art electron mobility of 7340 cm2·V−1·s−1 combined with a sheet carrier density of 1.93 × 1013 cm−2 leading to a remarkably low sheet resistance of 44 Ω/□ are measured at 4 K. A strong improvement of Direct current (DC) and Radio frequency (RF) characteristics is observed at low temperatures. The excellent current and power gain cutoff frequencies (fT/fmax) of 65/180 GHz and 95/265 GHz at room temperature and 77 K, respectively, using a 0.12 μm technology confirmed the outstanding 2DEG properties.
Abstract: Nowadays, Internet-of-Things (IoT) devices generate data at high speed and large volume. Often the data require real-time processing to support high system responsiveness which can be supported by localised Cloud and/or Fog computing paradigms. However, there are considerably large deployments of IoT such as sensor networks in remote areas where Internet connectivity is sparse, challenging the localised Cloud and/or Fog computing paradigms. With the advent of the Raspberry Pi, a credit card-sized single board computer, there is a great opportunity to construct low-cost, low-power portable cloud to support real-time data processing next to IoT deployments. In this paper, we extend our previous work on constructing Raspberry Pi Cloud to study its feasibility for real-time big data analytics under realistic application-level workload in both native and virtualised environments. We have extensively tested the performance of a single node Raspberry Pi 2 Model B with httperf and a cluster of 12 nodes with Apache Spark and HDFS (Hadoop Distributed File System). Our results have demonstrated that our portable cloud is useful for supporting real-time big data analytics. On the other hand, our results have also unveiled that overhead for CPU-bound workload in virtualised environment is surprisingly high, at 67.2%. We have found that, for big data applications, the virtualisation overhead is fractional for small jobs but becomes more significant for large jobs, up to 28.6%.
Abstract: This paper reports the successful fabrication of a GaN-on-Si high electron mobility transistor (HEMT) with a 1702 V breakdown voltage (BV) and low current collapse. The strain and threading dislocation density were well-controlled by 100 pairs of AlN/GaN superlattice buffer layers. Relative to the carbon-doped GaN spacer layer, we grew the AlGaN back barrier layer at a high temperature, resulting in a low carbon-doping concentration. The high-bandgap AlGaN provided an effective barrier for blocking leakage from the channel to substrate, leading to a BV comparable to the ordinary carbon-doped GaN HEMTs. In addition, the AlGaN back barrier showed a low dispersion of transiently pulsed ID under substrate bias, implying that the buffer traps were effectively suppressed. Therefore, we obtained a low-dynamic on-resistance with this AlGaN back barrier. These two approaches of high BV with low current collapse improved the device performance, yielding a device that is reliable in power device applications.