As the technology scaling approaching nano-scale region, variability in device performance becomes a major issue in the design of integrated circuits. Besides the growing amount of variability, the statistical nature of the variability is changing as the progress of technology generation. In the past, die-to-die variability, which is well managed by the worst case design technique, dominates over within-die variability. In present and the future, the amount of within-die variability is increasing and it casts a challenge in design methodology. This paper first shows measured results of variability in three different processes of 0.35, 0.18, and 0.13 µm technologies, and explains the above mentioned trend of variability. An example of modeling for the within-die variability is explained. The impact of within-die random variability on circuit performance is demonstrated using a simple numerical example. It shows that a circuit that is designed optimally under the assumption of deterministic delay is now most susceptible to random fluctuation in delay, which clearly indicates the requirement of statistical design methodology.

This paper proposes a statistical design approach for Non-Return-to-Zero (NRZ) 40 Gbit/s systems with Forward Error Correction (FEC); the approach considers Polarization Mode Dispersion (PMD). We introduce a fluctuating PMD emulator to experimentally clarify FEC performance in PMD-limited systems. By using the proposed design approach, and considering the FEC relaxation effect on PMD, the maximum transmission distance of an NRZ 40 Gbit/s system without PMD compensation is estimated as several hundreds of km depending on the number of cable concatenations per link and the probability threshold of system acceptance.

This paper treats the systematic design of chaos generators which are capable of generating continuous-time signals with prescribed probability density function and power density spectra. For a specific signal model a statistical analysis is performed such that the inverse problem, i. e. the calculation of the model parameters from prescribed signal characteristics, can be solved. Finally from the obtained model parameters and the model structure the signal generating system is constructed. The approach is illustrated by several examples.