Validation by Measurements of a IC Modeling Approach for SiP Applications

The growing importance of signal integrity (SI) analysis in integrated circuits (ICs), revealed by modern systemin-package methods, is demanding for new models for the IC sub-systems which are both accurate, efficient and extractable by simple measurement procedures. This paper presents the contribution for the establishment of an integrated IC modeling approach whose performance is assessed by direct comparison with the signals measured in laboratory of two distinct memory IC devices. Based on the identification of the main blocks of a typical IC device, the modeling approach consists of a network of system-level sub-models, some of which with already demonstrated accuracy, which simulated the IC interfacing behavior. Emphasis is given to the procedures that were developed to validate by means of laboratory measurements (and not by comparison with circuit-level simulations) the model performance, which is a novel and important aspect that should be considered in the design of IC models that are useful for SI analysi

FDTD modeling of heatsink RF characteristics for EMC mitigation

Due to their size and complex geometry, large heatsinks such as those used in the power electronics industry may enhance the radiated emissions produced by the circuits employing them. Such enhancement of the radio frequency (rf) radiation could cause the equipment to malfunction or to contravene current EMC regulations. In this paper, the electromagnetic resonant effects of heatsinks are examined using the finite-difference time-domain (FDTD) method and recommendations are made concerning the optimum geometry of heatsinks and the placement of components so as to mitigate potential EMC effects

A hybrid EKF and switching PSO algorithm for joint state and parameter estimation of lateral flow immunoassay models

This is the post-print version of the Article. The official published can be accessed from the link below - Copyright @ 2012 IEEEIn this paper, a hybrid extended Kalman filter (EKF) and switching particle swarm optimization (SPSO) algorithm is proposed for jointly estimating both the parameters and states of the lateral flow immunoassay model through available short time-series measurement. Our proposed method generalizes the well-known EKF algorithm by imposing physical constraints on the system states. Note that the state constraints are encountered very often in practice that give rise to considerable difficulties in system analysis and design. The main purpose of this paper is to handle the dynamic modeling problem with state constraints by combining the extended Kalman filtering and constrained optimization algorithms via the maximization probability method. More specifically, a recently developed SPSO algorithm is used to cope with the constrained optimization problem by converting it into an unconstrained optimization one through adding a penalty term to the objective function. The proposed algorithm is then employed to simultaneously identify the parameters and states of a lateral flow immunoassay model. It is shown that the proposed algorithm gives much improved performance over the traditional EKF method.This work was supported in part by the International Science and Technology Cooperation Project of China under Grant 2009DFA32050, Natural Science Foundation of China under Grants 61104041, International Science and Technology Cooperation Project of Fujian Province of China under Grant 2009I0016

Principles of Neuromorphic Photonics

In an age overrun with information, the ability to process reams of data has become crucial. The demand for data will continue to grow as smart gadgets multiply and become increasingly integrated into our daily lives. Next-generation industries in artificial intelligence services and high-performance computing are so far supported by microelectronic platforms. These data-intensive enterprises rely on continual improvements in hardware. Their prospects are running up against a stark reality: conventional one-size-fits-all solutions offered by digital electronics can no longer satisfy this need, as Moore's law (exponential hardware scaling), interconnection density, and the von Neumann architecture reach their limits. With its superior speed and reconfigurability, analog photonics can provide some relief to these problems; however, complex applications of analog photonics have remained largely unexplored due to the absence of a robust photonic integration industry. Recently, the landscape for commercially-manufacturable photonic chips has been changing rapidly and now promises to achieve economies of scale previously enjoyed solely by microelectronics. The scientific community has set out to build bridges between the domains of photonic device physics and neural networks, giving rise to the field of \emph{neuromorphic photonics}. This article reviews the recent progress in integrated neuromorphic photonics. We provide an overview of neuromorphic computing, discuss the associated technology (microelectronic and photonic) platforms and compare their metric performance. We discuss photonic neural network approaches and challenges for integrated neuromorphic photonic processors while providing an in-depth description of photonic neurons and a candidate interconnection architecture. We conclude with a future outlook of neuro-inspired photonic processing.Comment: 28 pages, 19 figure

Neuro-memristive Circuits for Edge Computing: A review

The volume, veracity, variability, and velocity of data produced from the ever-increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure. Increasing the data processing capability of edge computing devices at lower power requirements can reduce several overheads for cloud computing solutions. This paper provides the review of neuromorphic CMOS-memristive architectures that can be integrated into edge computing devices. We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks and open problems in the field of neuro-memristive circuits for edge computing

Power Beacon-Assisted Millimeter Wave Ad Hoc Networks

Deployment of low cost power beacons (PBs) is a promising solution for dedicated wireless power transfer (WPT) in future wireless networks. In this paper, we present a tractable model for PB-assisted millimeter wave (mmWave) wireless ad hoc networks, where each transmitter (TX) harvests energy from all PBs and then uses the harvested energy to transmit information to its desired receiver. Our model accounts for realistic aspects of WPT and mmWave transmissions, such as power circuit activation threshold, allowed maximum harvested power, maximum transmit power, beamforming and blockage. Using stochastic geometry, we obtain the Laplace transform of the aggregate received power at the TX to calculate the power coverage probability. We approximate and discretize the transmit power of each TX into a finite number of discrete power levels in log scale to compute the channel and total coverage probability. We compare our analytical predictions to simulations and observe good accuracy. The proposed model allows insights into effect of system parameters, such as transmit power of PBs, PB density, main lobe beam-width and power circuit activation threshold on the overall coverage probability. The results confirm that it is feasible and safe to power TXs in a mmWave ad hoc network using PBs.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Analysis of an On-Line Stability Monitoring Approach for DC Microgrid Power Converters

An online approach to evaluate and monitor the stability margins of dc microgrid power converters is presented in this paper. The discussed online stability monitoring technique is based on the Middlebrook's loop-gain measurement technique, adapted to the digitally controlled power converters. In this approach, a perturbation is injected into a specific digital control loop of the converter and after measuring the loop gain, its crossover frequency and phase margin are continuously evaluated and monitored. The complete analytical derivation of the model, as well as detailed design aspects, are reported. In addition, the presence of multiple power converters connected to the same dc bus, all having the stability monitoring unit, is also investigated. An experimental microgrid prototype is implemented and considered to validate the theoretical analysis and simulation results, and to evaluate the effectiveness of the digital implementation of the technique for different control loops. The obtained results confirm the expected performance of the stability monitoring tool in steady-state and transient operating conditions. The proposed method can be extended to generic control loops in power converters operating in dc microgrids