Multiple degradation mechanisms limit the lifetime of integrated circuits (ICs). Different aspects can be tackled with Design for Reliability approaches already during circuit design to avoid serious implications of degradation for ICs in the field. For example, aging simulations can be performed to investigate the impact of the degradation of integrated transistors onto circuit performance. Since these simulations cause a significant verification effort, we work on approaches to efficiently feed reliability information back to circuit designers. This article discusses these approaches with their application scenarios and benefits.

Process variations and atomic-level fluctuations increasingly pose challenges to the design and analysis of integrated circuits by introducing variability. Although several approaches have been proposed to deal with the inherent statistical nature of circuit design, we consider them incomplete with two important aspects often being insufficiently addressed: 1) non-Gaussian distributions and 2) highly correlated parameters. To address these points, we propose a fully multivariate and non-Gaussian approach based on an arbitrary model. A subset of the model parameters is treated as a multidimensional random variable, which is represented by a combination of generalized lambda distributions and Spearman rank correlation matrices-a very general approach with nearly arbitrary freedom in distribution shapes and parameter correlations. In our application scenarios, we show that such a model is able to fully and accurately capture variability in device compact models and standard cell performance models. Finally, we present adapted analysis methods making use of these models in circuit simulations and in efficient gate level analyses of digital circuits with high accuracy.

Analog behavioral models are widely used to reduce the complexity in hierarchical analog circuit design and verification. In the presence of process variations and atomic-level fluctuations, however, these models have to be extended to take variability into account. In this paper, we present a probabilistic solution that treats the behavioral model coefficients as multidimensional random variables and supports non-Gaussian as well as correlated parameters. A voltage divider and a bandgap voltage reference demonstrate the capabilities of our modeling approach in terms of accuracy and efficiency.

Variability continues to pose challenges to integrated circuit design. With statistical static timing analysis and high-yield estimation methods, solutions to particular problems exist, but they do not allow a common view on performance variability including potentially correlated and non-Gaussian parameter distributions. In this paper, we present a probabilistic approach for variability modeling as an alternative: model parameters are treated as multi-dimensional random variables. Such a fully mul-tivariate statistical description formally accounts for correlations and non-Gaussian random components. Statistical characterization and model application are introduced for standard cells and gate-level digital circuits. Example analyses of circuitry in a 28 nm industrial technology illustrate the capabilities of our modeling approach.

It is well known that random fluctuations in integrated circuit manufacturing introduce variations in circuit performance. While a lot of effort has been spent on circuit variability, fitting performance parameter distributions has not been extensively examined. Our work analyzes whether the Generalized Lambda Distribution suits approximating circuit performance characteristics. We focus on statistical standard cell characterization as an important step towards statistical gate-level and system-level analyses. Our results show that the Generalized Lambda Distribution is not applicable to raw leakage power data. However, timing data and dynamic power consumption may be approximated well. The high characterization effort has to be overcome to achieve industrial application.