Vertical MOSFETs are gaining importance for VLSI circuit integration and for reducing the feature size. They are continuously scaled down in channel length due to the increasing need for higher packing density and higher device speed. Also 3D compaction of circuits is possible using these transistors. In order to achieve as dense and fast as possible circuits several vertical MOSFETs using different technologies have been fabricated. In this paper, 120nm vertical n-channel MOSFET uniformly doped in silicon substrate and channel region is simulated using the ISE_TCAD software, developed by the Integrated Systems Engineering and compared with one of similar fabricated transistors from the literature . The results show more than 92% match between the simulated and the practical devices in terms of terminal characteristics considering the fact that the ideal mobility models as well as the most suitable mesh condition are applied to the simulation flow. Tending to scale down the length of the vertical MOSFETs and observe the short channel effects, transistors with 80nm and 100nm channel length were also simulated. As expected, shrinking the channel length results in increasing the current and decreasing the threshold voltage as part of short channel effects. Other effects such as hot-carrier and substrate current for the three devices were investigated under the certain values of gate and source voltages.
An automated classification technique is desirable to identify the different stages of sleep. In this paper a technique for differentiating the characteristics of each sleep phase has been developed. This is an ideal pre-processor stage for classifying systems such as neural networks. A wavelet based continuous Morlet transform was developed to analyse the EEG signal in both the time and frequency domain. Test results using two 100 epoch EEG test data sets from pre-recorded EEG data are presented. Key rhythms in the EEG signal were identified and classified using the continuous wavelet transform. The wavelet results indicated each sleep phase contained different rhythms and artefacts (noise from muscle movement in the EEG); providing proof that an EEG can be classified accordingly. The coefficients founded by the wavelet transform have been emphasised by statistical techniques. Hypothesis testing was used to highlight major differences between adjacent sleep stages. Various signal processing methods such as power spectrum density and the discrete wavelet transform have been used to emphasise particular characteristics in an EEG. By implementing signal processing methods on an EEG data set specific rules for each sleep stage have been developed suitable for a neural network classification solution.
Traditional ECG viewing techniques use a flat file structure and the relationship of the leads to physical structure is not clear. State space allows a 3D representation that is more representative of anatomical structure and electrical activity. This paper demonstrates how novel visualisation techniques allow easier identification of anomalies. The methods employed use Taken’s state-space theory to plot the amplitude of user selected leads on the relative axes in the state space domain. By plotting the combined values of separate leads, the direct relationship between the different viewing angles of the electrodes can be seen. A graphical user interface (GUI) was developed to view MIT-BIH database files, and files from a cardiology clinic, in various state-space formats. This software allows the user to rotate the 3D models and provides a cross-sectional view of the plots at user selected coordinates. The usefulness of these models were determined by combining the orthogonal views of leads I, aVF, and V2. This enabled the user to collaborate the vector values of the lead locations with the conventional ECG characteristics.