With the scaling down of CMOS devices, process variation is becoming the leading cause of CMOS based analog circuit failures. For example, a mere 5% variation in feature size can trigger circuit failure. Various methods such as Monte-Carlo and corner-based verification help predict variation caused problems at the expense of thousands of simulations before capturing the problem. This paper presents a new methodology for analog circuit performance prediction. The new method first applies statistical uncertainty analysis on all associated devices in the circuit. By evaluating the uncertainty importance of parameter variability, it approximates the circuit with only components that are most critical to output results. Applying Chebyshev Affine Arithmetic (CAA) on the resulting system provides both performance bounds and probability information in time domain and frequency domain.

Array antennas are employed at the receiver for a variety of purposes such as to combat fading or to reduce co-channel interference. To evaluate the performance of a wireless communications system using antenna arrays it becomes necessary to have spatial channel models that describe the Angle of Arrival (AOA), Time of Arrival (TOA) and the Angle Spread (AS) of the multipath components. Among the most widely used radio propagation models is the single bounce scattering geometric model, where propagation between the transmitting and receiving antennas is assumed to take place via single scattering from an intervening obstacle. Currently, several geometric models are available such as circular and elliptical scattering models, with each model being applicable to a specific environment type. This paper addresses the modeling, simulation and evaluation of the angle spread in smart antenna systems taking into account the Gaussian density model, and proves that the model finds use both in a micro cell as well as in a macro cell environment. Moreover, we show statistics for the angle and time of arrival.