Quantum simulation investigation of work-function variation in nanowire tunnel FETs.

The variability induced by the work-function variation (WFV) in p-type ultra-scaled nanowire tunnel FET (TFET) has been studied by using the Non-Equilibrium Green's Function module implemented in University of Glasgow quantum transport simulator called NESS. To provide a thorough insight into the influence of WFV, we have simulated 250 atomistically different nanowire TFETs and the obtained results are compared to nanowire MOSFETs first. Our statistical simulations reveal that the threshold voltage (V ) variations of MOSFETs and TFETs are comparable, whereas the on-current (I ) and off-current (I ) variations of TFETs are smaller and higher, respectively in comparison to the MOSFET.

VLSI Design of SVM-Based Seizure Detection System With On-Chip Learning Capability.

Portable automatic seizure detection system is very convenient for epilepsy patients to carry. In order to make the system on-chip trainable with high efficiency and attain high detection accuracy, this paper presents a very large scale integration (VLSI) design based on the nonlinear support vector machine (SVM). The proposed design mainly consists of a feature extraction (FE) module and an SVM module.

Automatic Detection of Epilepsy and Seizure Using Multiclass Sparse Extreme Learning Machine Classification.

An automatic detection system for distinguishing normal, ictal, and interictal electroencephalogram (EEG) signals is of great help in clinical practice. This paper presents a three-class classification system based on discrete wavelet transform (DWT) and the nonlinear sparse extreme learning machine (SELM) for epilepsy and epileptic seizure detection. Three-level lifting DWT using Daubechies order 4 wavelet is introduced to decompose EEG signals into delta, theta, alpha, and beta subbands.