Versatile surrogate models for IC buffers

In previous papers [1,2] the authors have investigated the use of Volterra series in the identification of IC buffer macro-models. While the approach benefited from some of the inherent qualities of Volterra series it preserved the two-state paradigm of earlier methods (see [3] and its references) and was thus limited in its versatility. In the current paper the authors tackle the challenge of going beyond an application or device-oriented approach and build versatile surrogate models that mimic the behavior of IC buffers over a wide frequency band and for a variety of loads thus achieving an unprecedented degree of generality. This requires the use of a more general system identification paradig

Worst-Case Optimization of a Digital Link for Wearable Electronics in a Stochastic Framework

This paper demonstrates an optimization strategy for systems affected by uncertainties in the case of a textile interconnect line. Rather than simply conducting stochastic analysis at the end of the design process, tolerances are accounted for from the early stages of the flow. An unsupervised approach, used to describe the stochastic behavior of the line, isintegrated within a heuristic optimization algorithm with the aim of selecting the optimal parameters of a passive equalizer

Stochastic Time-Domain Mapping for Comprehensive Uncertainty Assessment in Eye Diagrams

The eye diagram is one of the most common tools used for quality assessment in high-speed links. This article proposes a method of predicting the shape of the inner eye for a link subject to uncertainties. The approach relies on machine learning regression and is tested on the very challenging example of flexible link for smart textiles. Several sources of uncertainties are taken into account related to both manufacturing tolerances and physical deformation. The resulting model is fast and accurate. It is also extremely versatile: rather than focusing on a specific metric derived from the eye diagram, its aim is to fully reconstruct the inner eye and enable designers to use it as they see fit. This article investigates the features and convergence of three alternative machine learning algorithms, including the single-output support vector machine regression, together with its least squares variant, and the vector-valued kernel ridge regression. The latter method is arguably the most promising, resulting in an accurate, fast and robust tool enabling a complete parametric stochastic map of the eye

Present and future of I/O-buffer behavioral macromodels

Signal and Power Integrity (SI/PI) verification flows rely on accurate models for complex I/O-buffers that drive and receive electrical signals on high-speed channels. The sheer density of modern integrated circuits makes detailed transistor-level descriptions computationally cumbersome to the point where they become unusable for system level simulations. Fortunately, transistor-level descriptions may be replaced with more compact representations that approximate the input/output buffers behavior with considerable accuracy while providing a simulation speedup of several orders of magnitude. Known as behavioral models, surrogate models or macromodels, these computationally efficient equivalents have become a de-facto industry standard in SI/ PI simulations. This paper presents an overview of the state-of-the-art in I/O-buffer behavioral modeling, introducing the main features of both standard and emerging solutions. Open issues and future research directions are also discusse

Model-Order Reduction of VLSI Circuit Interconnects via a Laguerre Representation” dans

The VLSI (Very Large Scale Integration) industry has the tendency to decrease circuit size, increase speed, assuring ever lower energy consumption and ever higher integration density of analogical components accompanied by digital blocs. With this tendency circuit designers are faced with a new challenge: the analysis and modeling of logical and analogical signals propagating between two circuit points. The search for high speed applications makes the effects of interconnects, usually neglected in the past, an important issue; noise, delay, distortion, reflections and cross talk are just some of these effects. High integration density, miniaturization, high working frequencies are three great factors which prevent interconnects to be considered small independent circuits. Thus, simulation becomes a rathe

SOAs and Digital Linearization in Optical Networks-A Stochastic Investigation

Digital predistortion has recently spurred interest in photonics. In this paper, the authors perform a sensitivity analysis of three digital predistortion algorithms and demonstrate an increase in performance and, in some cases, robustness to uncertainties