Brian J. Mulvaney

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Krylov subspace techniques in harmonic balance simulations become increasingly ineffective when applied to strongly nonlinear circuits. This limitation is particularly important in the simulation if the circu it has components being operated in a very nonlinear region. Ev en if the circuit contains only a few very nonlinear components, Krylov methods using(More)
A new method for transient noise analysis of nonlinear circuits is presented. The method is based on the presentation of a circuit as a linear timevarying system with modulated stationary noise models. The equations of the method are derived and their solution is described. The expression needed to compute the complete probabilistic characterization of a(More)
This paper proposes an improvement to the well-known oscillator nonlinear phase macromodel based on Floquet theory. A smoothed form of the nonlinear phase macromodel is derived by eliminating highly oscillatory terms in the macromodel, resulting in a significant speed-up in transient simulation. For an LC oscillator under sinusoidal excitation the new(More)
A new approach to analyze injection locking mode of oscillators under small external excitation is proposed. The proposed approach exploits existence conditions of the solution of HB linear system with degenerate matrix. The method allows one to obtain the locking range for an arbitrary oscillator circuit with an arbitrary periodic injection waveform. The(More)
A new computational concept of timing jitter is proposed that is suitable for exploitation in circuit simulators. It is based on the approximation of computed noise characteristics. To define jitter value the parameter representation is used. The desired parameters are obtained after noise simulation process in time domain by minimization of integral(More)
A new method for computation of timing jitter in a PLL is proposed. The computational method is based on the representation of the circuit as a linear time-varying system with modulated stationary noise models, spectral decomposition of stochastic process and decomposition of noise into orthogonal components i. e. phase and amplitude noise. The method is(More)