Enabling fast power integrity transient analysis through parameterized small-signal macromodels

In this paper, we present an automated strategy for extracting behavioral small-signal macromodels of biased nonlinear circuit blocks. We discuss in detail the case study of a Low DropOut (LDO) voltage regulator, which is an essential part of the power distribution network in electronic systems. We derive a compact yet accurate surrogate model of the LDO, which enables fast transient power integrity simulations, including all parasitics due to the specific layout of the LDO realization. The model is parameterized through its DC input voltage and its output current and is thus available as a SPICE netlist. Numerical experiments show that a speedup up to 700X is achieved when replacing the extracted post-layout netlist with the surrogate model, with practically no loss in accuracy

Structured black-box parameterized macromodels of integrated passive components

A novel black-box model representation and identification process is introduced, specifically designed to extract layout-scalable behavioral macromodels of passive integrated devices from sampled frequency-domain responses. An automated choice of structured frequency-domain basis functions enables extremely accurate approximations for responses characterized by high dynamic ranges over extended frequency bands, overcoming the main limitations of standard approaches. Numerical results confirm that the proposed structured approach provides robust and reliable scalable models, with guaranteed stability and passivity over the frequency band and parameter space of interest

Fast Simulation of Analog Circuit Blocks under Nonstationary Operating Conditions

This paper proposes a black-box behavioral modeling framework for analog circuit blocks operating under small-signal conditions around non-stationary operating points. Such variations may be induced either by changes in the loading conditions or by event-driven updates of the operating point for system performance optimization, e.g., to reduce power consumption. An extension of existing data-driven parameterized reduced-order modeling techniques is proposed that considers the time-varying bias components of the port signals as non-stationary parameters. These components are extracted at runtime by a lowpass filter and used to instantaneously update the matrices of the reduced-order state-space model realized as a SPICE netlist. Our main result is a formal proof of quadratic stability of such Linear Parameter Varying (LPV) models, enabled by imposing a specific model structure and representing the transfer function in a basis of positive functions whose elements constitute a partition of unity. The proposed quadratic stability conditions are easily enforced through a finite set of small-size Linear Matrix Inequalities (LMI), used as constraints during model construction. Numerical results on various circuit blocks including voltage regulators confirm that our approach not only ensures the model stability, but also provides speedup in runtime up to 2 orders of magnitude with respect to full transistor-level circuits

Spatio-temporal variability in underwater light climate in a turbid river-floodplain system. Driving factors and estimation using Secchi disc

The underwater light climate has important effects on primary producers. The aim of this research was to evaluate its variability in a turbid river-floodplain system. Photosynthetically active radiation (PAR) was measured in the Middle Paraná River during different hydrological phases to (a) analyse the photosynthetically active radiation attenuation coefficient (k) and euphotic depth (Zeu) as well as their associations with optically active components and (b) develop and evaluate indices and regression models based on Secchi disc (SD) measurements to estimate k and Zeu. Values of k were higher in the fluvial system than in the floodplain and during low-water stage than high-water stage. Particulate components controlled the light climate variability. Chromophoric dissolved organic matter and chlorophyll-a had significant effects during floods. The estimation of k and Zeu was sensitive to temporal but not to spatial variations. The highest prediction accuracy was observed when using specific non-linear regressions for each hydrological phase, especially for Zeu estimation (low stage: k = 1.76 × SD−0.80, Zeu = 2.62 × 1/SD−0.80; high stage: k = 2.04 × SD−0.53, Zeu = 2.26 × 1/SD−0.53). The indices k × SD and Zeu/SD were significantly different from those proposed for clear water environments. It is concluded that temporal variations should be considered when estimating k and Zeu in turbid river-floodplain systems because of the temporal heterogeneity in optically active components. Considering that ecological implication of the light climate depends on Zeu:depth ratio, we propose to estimate Zeu instead of k. Finally, indices proposed for clear water environments are not recommended to be applied to turbid environments.Fil: Mayora, Gisela Paola. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto Nacional de Limnología. Universidad Nacional del Litoral. Instituto Nacional de Limnología; ArgentinaFil: Devercelli, Melina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto Nacional de Limnología. Universidad Nacional del Litoral. Instituto Nacional de Limnología; Argentin

Animal Models of Dyssynchrony

Cardiac resynchronization therapy (CRT) is an important therapy for patients with heart failure and conduction pathology, but the benefits are heterogeneous between patients and approximately a third of patients do not show signs of clinical or echocardiographic response. This calls for a better understanding of the underlying conduction disease and resynchronization. In this review, we discuss to what extent established and novel animal models can help to better understand the pathophysiology of dyssynchrony and the benefits of CRT