Growth and characterisation of copper indium selenide films

Imperial Users onl

A P2P Botnet detection scheme based on decision tree and adaptive multilayer neural networks

In recent years, Botnets have been adopted as a popular method to carry and spread many malicious codes on the Internet. These malicious codes pave the way to execute many fraudulent activities including spam mail, distributed denial-of-service attacks and click fraud. While many Botnets are set up using centralized communication architecture, the peer-to-peer (P2P) Botnets can adopt a decentralized architecture using an overlay network for exchanging command and control data making their detection even more difficult. This work presents a method of P2P Bot detection based on an adaptive multilayer feed-forward neural network in cooperation with decision trees. A classification and regression tree is applied as a feature selection technique to select relevant features. With these features, a multilayer feed-forward neural network training model is created using a resilient back-propagation learning algorithm. A comparison of feature set selection based on the decision tree, principal component analysis and the ReliefF algorithm indicated that the neural network model with features selection based on decision tree has a better identification accuracy along with lower rates of false positives. The usefulness of the proposed approach is demonstrated by conducting experiments on real network traffic datasets. In these experiments, an average detection rate of 99.08 % with false positive rate of 0.75 % was observed

Characterisation of npn and pnp SiGe HBTs formed by Ge implantation

This work compares NPN and PNP SiGe HBTs fabricated simultaneously using Ge+ implantation in the base to achieve an average Ge concentration of 4at.%. Electrical measurements are presented and discussed with TEM and SIMS analysis. The results show that the PNP HBTs give superior performance to the NPNs, as they have more ideal collector characteristics. The NPN HBTs exhibit collector-emitter leakage. Both types of Ge+ implanted device have non-ideal base currents. TEM images show that both NPN and PNP devices have a high density of defects in the Ge+ implanted area. The electrical results are explained by the opposing effect of the Ge+ implant on the diffusion coefficients of boron and arsenic, as seen in the SIMS profiles. The increased emitter diffusion in the NPN HBTs creates a narrower base region, which is more prone to collector-emitter leakage. The hairpin dislocations in the base are thought to be the cause of the base current ideality deterioration

Characterisation of NPN and PNP SiGe heterojunction bipolar transistors formed by Ge⁺ implantation

This work compares NPN and PNP SiGe HBTs fabricated simultaneously using Ge+ implantation in the base to achieve an average Ge concentration of 4 at.%. Electrical measurements are presented and discussed with TEM and SIMS analysis. The results show that the PNP HBTs give superior performance to the NPNs, as they have more ideal collector characteristics. The NPN HBTs exhibit collector-emitter leakage. Both types of Ge+ implanted device have non-ideal base currents. TEM images show that both NPN and PNP devices have a high density of defects in the Ge+ implanted area. The electrical results are explained by the opposing effect of the Ge+ implant on the diffusion coefficients of boron and arsenic, as seen in the SIMS profiles. The increased emitter diffusion in the NPN HBTs creates a narrower base region, which is more prone to collector-emitter leakage. The hairpin dislocations in the base are thought to be the cause of the base current ideality deterioration.</p