Abstract. We present a software implementation of arithmetic operations in a finite field GF(2~), based on an alternative representation of the field elements. An important application is in elliptic curve cryptosystems. Whereas previously reported implementations of elliptic curve cryptosystems use a standard basis or an optimal normal basis to perform field operations, we represent the field elements as polynomials with coefficients in the smaller field GF(216). Calculations in this smaller field are carried out using pre-calculated lookup tables. This results in rather simple routines matching the structure of computer memory very well. The use of an irreducible trinomial as the field polynomial, as was proposed at Crypto'95 by R. Schroeppel et al., can be extended to this representation. In our implementation, the resulting routines are slightly faster than standard basis routines.

Abstract-To model nonlinear device behavior at microwave frequencies, accurate large-signal models are required. However, the standard procedure to estimate model parameters is often cumbersome, as it involves several measurement systems (dc, vector network analyzer, etc.). Therefore, we propose a new nonlinear modeling technique, which reduces the complexity of the model generation tremendously and only requires full two-port vectorial large-signal measurements. This paper reports on the results obtained with this new modeling technique applied to both empirical and artificial-neural-network device models. Experimental results are given for high electron-mobility transistors and MOSFETs. We also show that realistic signal excitations can easily be included in the optimization process.

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Non-linear MOSFET models are mostly derived indirectly from DC, low-frequency C-V and/or high-frequency S-parameter measurements. We developed non-linear modelling techniques that determine the state functions of MOSFETs directly from high-frequency vectorial large-signal measurements and thus eliminate the small-signal detour. Two methods are proposed to determine these state functions: parameter optimisation and extraction. Both approaches yield accurate results. Hence, the main difference is the application range. The method of parameter optimisation quickly generates accurate non-linear models for particular applications, while the extraction method is preferred to determine general non-linear models.

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