CMOS RF Power Amplifier Variability and Reliability Resilient Biasing Design and Analysis

Stochastic macromodeling for hierarchical uncertainty quantification of nonlinear electronic systems

“…Hence, the number of initial samples in the stochastic space K depends heavily on the number of random variables Z considered and the maximum degree of the polynomials P used in (1). Consequently, the efficiency of a PC-TFT model can be greatly undermined if the computational cost to generate such a model becomes too high.…”

“…The increasing complexity, density and bandwidth of modern electronic circuits require an efficient and accurate modeling of the effects of geometrical or electrical parameter variability on the system performances [1], [2]. The variability has several sources, ranging from the manufacturing process to temperature fluctuations and aging, and requires the electrical response to be addressed from a statistical standpoint in order to properly characterize signal integrity issues.…”

Stochastic Macromodeling of Nonlinear Systems Via Polynomial Chaos Expansion and Transfer Function Trajectories

“…T HE evaluation of the effects of geometrical or electrical parameter variability on the performance of modern microwave components and circuits is fundamental due to the increasing complexity, density, and bandwidth of these circuits [1], [2]. The Monte Carlo (MC) method is the standard for the variability analysis due to its accuracy and robustness.…”

Process, Voltage and Temperature Compensation Technique for Cascode Modulated PAs

“…A common approach is to design a customized bias circuit to compensate for the effects that cause the PA output power variation (e.g., threshold voltage and carrier mobility changes) [3]. Another approach is to design a bias circuit that keeps the drain current of the PA constant [1], [4], [5].…”