Supply Current Modeling and Analysis of Deep Sub-Micron Cmos Circuits

Continued technology scaling has introduced many new challenges in VLSI design. Instantaneous switching of the gates yields high current flow through them that causes large voltage drop at the supply lines. Such high instantaneous currents and voltage drop cause reliability and performance degradation. Reliability is an issue as high magnitude of current can cause electromigration, whereas, voltage drop can slow down the circuit performance. Therefore, designing power supply lines emphasizes the need of computing maximum current through them. However, the development of digital integrated circuits in short design cycle requires accurate and fast timing and power simulation. Unfortunately, simulators that employ device modeling methods, such as HSPICE are prohibitively slow for large designs. Therefore, methods which can produce good maximum current estimates in short times are critical. In this work a compact model has been developed for maximum current estimation that speeds up the computation by orders of magnitude over the commercial tools

Adaptive iterative working state prediction based on the double unscented transformation and dynamic functioning for unmanned aerial vehicle lithium-ion batteries.

In lithium-ion batteries, the accuracy of estimation of the state of charge is a core parameter which will determine the power control accuracy and management reliability of the energy storage systems. When using unscented Kalman filtering to estimate the charge of lithium-ion batteries, if the pulse current change rate is too high, the tracking effects of algorithms will not be optimal, with high estimation errors. In this study, the unscented Kalman filtering algorithm is improved to solve the above problems and boost the Kalman gain with dynamic function modules, so as to improve system stability. The closed-circuit voltage of the system is predicted with two non-linear transformations, so as to improve the accuracy of the system. Meanwhile, an adaptive algorithm is developed to predict and correct the system noises and observation noises, thus enhancing the robustness of the system. Experiments show that the maximum estimation error of the second-order Circuit Model is controlled to less than 0.20V. Under various simulation conditions and interference factors, the estimation error of the unscented Kalman filtering is as high as 2%, but that of the improved Kalman filtering algorithm are kept well under 1.00%, with the errors reduced by 0.80%, therefore laying a sound foundation for the follow-up research on the battery management system

Analysis and Estimation of the Maximum Switch Current during Battery System Reconfiguration

Batteries are interconnected in series and/or parallel to meet wide-range power or energy demands in various industrial applications. To pursue the benefits of multiple connection structures in one system, reconfigurable battery systems (RBSs) have recently emerged for safe and efficient operation, extended energy storage and delivery, etc. Switches are the essential elements to enable the battery system reconfiguration, but selecting appropriate switches for RBS designs has not been systematically investigated. To bridge this gap, analytical expressions are derived in this paper to estimate the maximum switch current and its upper limit to facilitate the selection of RBS switches. An RBS prototype based on H-bridges is set up and experimental results verify the effectiveness and advantage of the proposed estimation method. These analytical expressions, relying only on resistances of batteries and switches, are readily applicable to practical RBS design and much more efficient than conducting numerous circuit experiments, simulation tests, or circuit analyses, especially for large-scale systems. Moreover, the analysis framework and estimation method proposed for series-parallel mutual conversion can be adaptively extended to other complex system reconfigurations to facilitate various RBS designs

Performance Comparison of 112 Gb/s DMT, Nyquist PAM4 and Partial-Response PAM4 for Future 5G Ethernet-based Fronthaul Architecture

For a future 5G Ethernet-based fronthaul architecture, 100G trunk lines of a transmission distance up to 10 km over a standard single-mode fiber (SSMF) in combination with cheap gray optics to daisy chain cell site network interfaces are a promising cost- and power-efficient solution. For such a scenario, different intensity modulation and direct detect formats at a data rate of 112 Gb/s, namely Nyquist four-level pulse amplitude modulation (PAM4), discrete multitone transmission (DMT), and partial-response (PR) PAM4, are experimentally investigated, using a low-cost electroabsorption modulated laser, a 25G driver, and current state-of-the-art high-speed 84-GS/s CMOS digital-to-analog converter and analog-to-digital converter test chips. Each modulation format is optimized independently for the desired scenario, and their digital signal processing requirements are investigated. The performance of Nyquist PAM4 and PR PAM4 depends very much on the efficiency of pre- and postequalization. We show the necessity for at least 11 feedforward equalizer (FFE) taps for pre-emphasis and up to 41 FFE coefficients at the receiver side. In addition, PR PAM4 requires a maximum likelihood sequence estimation with four states to decode the signal back to a PAM4 signal. On the contrary, bit loading and power loading are crucial for DMT, and an FFT length of at least 512 is necessary. With optimized parameters, all modulation formats result in a very similar performances, demonstrating a transmission distance of up to 10 km over an SSMF with bit error rates below an FEC threshold of 4.4E-3, allowing error-free transmission

Stochastic RUL calculation enhanced with TDNN-based IGBT failure modeling

Power electronics are widely used in the transport and energy sectors. Hence, the reliability of these power electronic components is critical to reducing the maintenance cost of these assets. It is vital that the health of these components is monitored for increasing the safety and availability of a system. The aim of this paper is to develop a prognostic technique for estimating the remaining useful life (RUL) of power electronic components. There is a need for an efficient prognostic algorithm that is embeddable and able to support on-board real-time decision-making. A time delay neural network (TDNN) is used in the development of failure modes for an insulated gate bipolar transistor (IGBT). Initially, the time delay neural network is constructed from training IGBTs' ageing samples. A stochastic process is performed for the estimation results to compute the probability of the health state during the degradation process. The proposed TDNN fusion with a statistical approach benefits the probability distribution function by improving the accuracy of the results of the TDDN in RUL prediction. The RUL (i.e., mean and confidence bounds) is then calculated from the simulation of the estimated degradation states. The prognostic results are evaluated using root mean square error (RMSE) and relative accuracy (RA) prognostic evaluation metrics

Lithium-ion battery thermal-electrochemical model-based state estimation using orthogonal collocation and a modified extended Kalman filter

This paper investigates the state estimation of a high-fidelity spatially resolved thermal- electrochemical lithium-ion battery model commonly referred to as the pseudo two-dimensional model. The partial-differential algebraic equations (PDAEs) constituting the model are spatially discretised using Chebyshev orthogonal collocation enabling fast and accurate simulations up to high C-rates. This implementation of the pseudo-2D model is then used in combination with an extended Kalman filter algorithm for differential-algebraic equations to estimate the states of the model. The state estimation algorithm is able to rapidly recover the model states from current, voltage and temperature measurements. Results show that the error on the state estimate falls below 1 % in less than 200 s despite a 30 % error on battery initial state-of-charge and additive measurement noise with 10 mV and 0.5 K standard deviations.Comment: Submitted to the Journal of Power Source

Mobile Communications Beyond 52.6 GHz: Waveforms, Numerology, and Phase Noise Challenge

In this article, the first considerations for the 5G New Radio (NR) physical layer evolution to support beyond 52.6GHz communications are provided. In addition, the performance of both OFDM based and DFT-s-OFDM based networks are evaluated with special emphasis on the phase noise (PN) induced distortion. It is shown that DFT-s-OFDM is more robust against PN under 5G NR Release 15 assumptions, namely regarding the supported phase tracking reference signal (PTRS) designs, since it enables more effective PN mitigation directly in the time domain. To further improve the PN compensation capabilities, the PTRS design for DFT-s-OFDM is revised, while for the OFDM waveform a novel block PTRS structure is introduced, providing similar link performance as DFT-s-OFDM with enhanced PTRS design. We demonstrate that the existing 5G NR Release 15 solutions can be extended to support efficient mobile communications at 60GHz carrier frequency with the enhanced PTRS structures. In addition, DFT-s-OFDM based downlink for user data could be considered for beyond 52.6GHz communications to further improve system power efficiency and performance with higher order modulation and coding schemes. Finally, network link budget and cell size considerations are provided, showing that at certain bands with specific transmit power regulation, the cell size can eventually be downlink limited.Comment: This manuscript has been submitted to IEEE Wireless Communications Magazine (WCM). 8 pages, 4 figures, and 2 table

Model-based observer proposal for surface roughness monitoring

Comunicación presentada a MESIC 2019 8th Manufacturing Engineering Society International Conference (Madrid, 19-21 de Junio de 2019)In the literature, many different machining monitoring systems for surface roughness and tool condition have been proposed and validated experimentally. However, these approaches commonly require costly equipment and experimentation. In this paper, we propose an alternative monitoring system for surface roughness based on a model-based observer considering simple relationships between tool wear, power consumption and surface roughness. The system estimates the surface roughness according to simple models and updates the estimation fusing the information from quality inspection and power consumption. This monitoring strategy is aligned with the industry 4.0 practices and promotes the fusion of data at different shop-floor levels