Parameter sensitivity for optimal design of 65 nm node SOI transistors

Mixed-mode simulation, where device simulation is embedded directly within a circuit simulator, is used for the first time to provide scaling guidelines to achieve optimal digital circuit performance for double gate SOI MOSFETs. This significant advance overcomes the lack of availability of SPICE model parameters. The sensitivity of the gate delay and on-off current ratio to each of the key geometric and technological parameters of the transistor is quantified. The impact of the source-drain doping profile on circuit performance is comprehensively investigated.

Time operators for one parameter semigroups

We develop two simple approaches to the construction of time operators for semigroups of continuous linear operators in Hilbert spaces provided that the generators of these semigroups are normal operators. The first approach enables us to give explicit formulas (in the spectral representations) both for the time operators and for their eigenfunctions. The other approach provides no explicit formula. However, it enables us to find necessary and sufficient conditions for the existence of time operators for semigroups of continuous linear operators in separable Hilbert spaces with normal generators. Time superoperators corresponding to unitary groups are also discussed.

Extrinsic parameter extraction and RF modelling of CMOS

An analytical approach for CMOS parameter extraction which includes the effect of parasitic resistance is presented. The method is based on small-signal equivalent circuit valid in all region of operation to uniquely extract extrinsic resistances, which can be used to extend the industry standard BSIM3v3 MOSFET model for radio frequency applications. The verification of the model was carried out through frequency domain measurements of S-parameters and direct time domain measurement at 2.4 GHz in a large signal non-linear mode of operation. (C) 2003 Elsevier Ltd. All rights reserved.

Integrated structure selection and parameter optimisation for eng-genes neural models

The eng-genes concept involves the use of fundamental known system functions as activation functions in a neural model to create a 'grey-box' neural network. One of the main issues in eng-genes modelling is to produce a parsimonious model given a model construction criterion. The challenges are that (1) the eng-genes model in most cases is a heterogenous network consisting of more than one type of nonlinear basis functions, and each basis function may have different set of parameters to be optimised; (2) the number of hidden nodes has to be chosen based on a model selection criterion. This is a mixed integer hard problem and this paper investigates the use of a forward selection algorithm to optimise both the network structure and the parameters of the system-derived activation functions. Results are included from case studies performed on a simulated continuously stirred tank reactor process, and using actual data from a pH neutralisation plant. The resulting eng-genes networks demonstrate superior simulation performance and transparency over a range of network sizes when compared to conventional neural models. (c) 2007 Elsevier B.V. All rights reserved.