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Impact of TFET Reverse Currents Into Circuit Operation: A Case Study
J. Nuñez
Conference - Joint Int. EUROSOI Workshop and Int. Conf. on Ultimate Integration on Silicon EUROSOI-ULIS 2018
[abstract]
Tunnel FET transistors (TFETs) are one of the most promising candidates to replace CMOS transistors for future integrated circuits. However TFET-based circuit design can exhibit significant limitations due to their reverse conduction currents caused by the direct bias of the intrinsic diode of these transistors. In this paper we analyze in depth this issue through the design of charge pump (DC-DC step up converters) circuits for energy harvesting applications. The proposed solution mitigates the impact of reverse conduction currents and, thus, improves power conversion efficiencies (PCE) compared to previous designs.

A two-step surrogate modeling strategy for single-objective and multi-objective optimization of radiofrequency circuits
F. Passos, R. González-Echevarría, E. Roca, R. Castro-López and F.V. Fernández
Journal Paper - Soft Computing, first online, 2018
SPRINGER    DOI: 10.1007/s00500-018-3150-9    ISSN: 1432-7643    » doi
[abstract]
The knowledge-intensive radiofrequency circuit design and the scarce design automation support play against the increasingly stringent time-to-market demands. Optimization algorithms are starting to play a crucial role; however, their effectiveness is dramatically limited by the accuracy of the evaluation functions of objectives and constraints. Accurate performance evaluation of radiofrequency passive elements, e.g., inductors, is provided by electromagnetic simulators, but their computational cost makes their use within iterative optimization loops unaffordable. Surrogate modeling strategies, e.g., Kriging, support vector machines, artificial neural networks, etc., arise as a promising modeling alternative. However, their limited accuracy in this kind of applications has prevented a widespread use. In this paper, inductor performance properties are exploited to develop a two-step surrogate modeling strategy in order to evaluate the behavior of inductors with high efficiency and accuracy. An automated design flow for radiofrequency circuits using this surrogate modeling of passive components is presented. The methodology couples a circuit simulator with evolutionary computation algorithms such as particle swarm optimization, genetic algorithm or non-dominated sorting genetic algorithm (NSGA-II). This methodology ensures optimal performances within short computation times by avoiding electromagnetic simulations of inductors during the entire optimization process and using a surrogate model that has less than 1% error in inductance and quality factor when compared against electromagnetic simulations. Numerous real-life experiments of single-objective and multi-objective low-noise amplifier design demonstrate the accuracy and efficiency of the proposed strategies.

Embedding MATLAB Optimizers in SIMSIDES for the High-Level Design of ΣΔ Modulators
B. Cortés-Delgadillo, P.A. Rodríguez-Navas, L.I. Guerrero-Linares and J.M. de la Rosa
Journal Paper - IEEE Transactions on Circuits and Systems II: Express Briefs, first online, 2018
IEEE    DOI: 10.1109/TCSII.2018.2820900    ISSN: 1549-7747    » doi
[abstract]
This brief shows how to combine SIMSIDES, a SIMULINK-based time-domain behavioral simulator, with different optimization engines available in MATLAB for the automated high-level design of ΣΔ modulators. To this purpose, an updated version of SIMSIDES has been developed, which includes a user-friendly interface that links the simulator with the optimizers, and guides designers through the main steps required to set the design variables, constraints and select the most suitable algorithm to maximize the performance of an arbitrary modulator topology for a given set of specifications. Several examples and results of the optimization procedure are shown to illustrate the benefits of the presented tool for the high-level synthesis of ΣΔ modulators.

Securing Minutia Cylinder Codes for Fingerprints through Physically Unclonable Functions: An Exploratory Study
R. Arjona, M.A. Prada-Delgado, I. Baturone and A. Ross
Conference - International Conference on Biometrics ICB 2018
[abstract]
A number of personal devices, such as smartphones, have incorporated fingerprint recognition solutions for user authentication purposes. This work proposes a dual-factor fingerprint matching scheme based on P-MCCs (Protected Minutia Cylinder-Codes) generated from fingerprint images and PUFs (Physically Unclonable Functions) generated from device SRAMs (Static Random Access Memories). Combining the fingerprint identifier with the device identifier results in a secure template satisfying the discriminability, irreversibility, revocability, and unlinkability properties, which are strongly desired for data privacy and security. Experiments convey the benefits of the proposed dual-factor authentication mechanism in enhancing the security of personal devices that utilize biometric authentication schemes.

Dynamic range considerations for neural recording channels
M. Delgado-Restituto
Conference - IEEE CAS Singapore Chapter Workshop, 2018
[abstract]
Neural readout microelectronic interfaces are essential in implanted central nerve system prostheses aimed for brain-machine interfaces, the amelioration of disease effects, or the development of robotic mechanisms for the restitution/rehabilitation of abilities lost after injury or disease. Neural signals which can be recorded and used as biomarkers of the brain activity include local field potentials (LFPs) and action potentials (APs). They exhibit small amplitude (typically, below 1mV for LFPs and 100V for APs) and narrow band characteristics (0.5-200Hz for LFPs and 200Hz-7kHz for APs). A priori, these signals can be easily digitized with low-to-medium resolution ADCs, thus paving the way for neural prostheses with small area and power consumptions. However, along with the biomarkers, strong in band artifacts, which can be much larger that the signals of interest, may contaminate the recording or even preclude it altogether if the front-end saturates. Different causes can be at the origin of artifacts; for instance, they can be motion related or generated by electrical stimulations close to the recording sites. Coping with these large artifacts would demand for high dynamic range (of about 75dB) front-ends and data converters with large effective resolutions (beyond 13-14 bits). However, recent proposals for ADC resolution reduction techniques have demonstrated that modest ADCs can still be used for neural recording even in the presence of artifacts. This work reviews these proposals and also presents state-of-the-art techniques for the suppression of differential and common-mode artifacts from neural recordings.

Spiking Hough for Shape Recognition
P. Negri, T. Serrano-Gotarredona and B. Linares-Barranco
Book Chapter - Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, pp 425-432, 2018
SPRINGER    DOI: 10.1007/978-3-319-75193-1_51    ISBN: 978-3-319-75192-4    » doi
[abstract]
The paper implements a spiking neural model methodology inspired on the Hough Transform. On-line event-driven spikes from Dynamic Vision Sensors are evaluated to characterize and recognize the shape of Poker signs. The multi-class system, referred as Spiking Hough, shows the good performance on the public POKER-DVS dataset.

A Miniaturized Two-Axis Ultra Low Latency and Low-Power Sun Sensor for Attitude Determination of Micro Space Probes
L. Farian, P. Häfliger and J.A. Leñero-Bardallo
Journal Paper - IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 65, no. 5, pp 1543-1554, 2018
IEEE    DOI: 10.1109/TCSI.2017.2763990    ISSN: 1549-8328    » doi
[abstract]
This paper describes design, fabrication process, and comprehensive experimental results of a first prototype two-axis miniaturized spiking sun sensor. The sun sensor is a fusion of analog and digital sensor types, such that it takes advantage of spatial selectivity of digital sensors, and is not limited by the global frame rate as in analog sun sensors. It is composed of spiking pixels, and uses a novel Time-to-First-n-Spikes with time-out readout mode to reduce bandwidth consumption and post-processing computation. A thin glass lid with a metal deposited pattern serves as a mask projecting a light pattern onto the sensor. The sun sensor is able to extract a profile of the incident light in the form of time-stamped events. Its latency depends on light intensity, and for medium radiance conditions is equal to 88μs. The sun sensor consumes 6.3μW in normal operation, and has a precision of 0.98°, and a field of view of 144°. The high temporal resolution, low power consumption, and small QFN64 package make this sun sensor suitable for space probe and sounding rocket applications, where low temporal latency and payload size are essential. This sun sensor is designed to be employed in the sounding rocket attitude determination system as part of the 4DSpace research initiative to study ionospheric plasma disturbances.

A Configurable Event-Driven Convolutional Node with Rate Saturation Mechanism for Modular ConvNet Systems Implementation
L.A. Camuñas-Mesa, Y.L. Domínguez-Cordero, A. Linares-Barranco, T. Serrano-Gotarredona and B. Linares-Barranco
Journal Paper - Frontiers in Neuroscience, vol. 12, Article 63, 2018
FRONTIERS RESEARCH FOUNDATION    DOI: 10.3389/fnins.2018.00063    ISSN: 1662-4548    » doi
[abstract]
Convolutional Neural Networks (ConvNets) are a particular type of neural network often used for many applications like image recognition, video analysis or natural language processing. They are inspired by the human brain, following a specific organization of the connectivity pattern between layers of neurons known as receptive field. These networks have been traditionally implemented in software, but they are becoming more computationally expensive as they scale up, having limitations for real-time processing of high-speed stimuli. On the other hand, hardware implementations show difficulties to be used for different applications, due to their reduced flexibility. In this paper, we propose a fully configurable event-driven convolutional node with rate saturation mechanism that can be used to implement arbitrary ConvNets on FPGAs. This node includes a convolutional processing unit and a routing element which allows to build large 2D arrays where any multilayer structure can be implemented. The rate saturation mechanism emulates the refractory behavior in biological neurons, guaranteeing a minimum separation in time between consecutive events. A 4-layer ConvNet with 22 convolutional nodes trained for poker card symbol recognition has been implemented in a Spartan6 FPGA. This network has been tested with a stimulus where 40 poker cards were observed by a Dynamic Vision Sensor (DVS) in 1 s time. Different slow-down factors were applied to characterize the behavior of the system for high speed processing. For slow stimulus play-back, a 96% recognition rate is obtained with a power consumption of 0.85 mW. At maximum play-back speed, a traffic control mechanism downsamples the input stimulus, obtaining a recognition rate above 63% when less than 20% of the input events are processed, demonstrating the robustness of the network.

Practical Characterization of Cell-Electrode Electrical Models in Bio-Impedance Assays
J.A. Serrano, P. Pérez, A. Maldonado, M. Martín, A. Olmo, P. Daza, G. Huertas and A. Yúfera
Conference - International Conference on Biomedical Electronics and Devices BIODEVICES 2018
[abstract]
This paper presents the fitting process followed to adjust the parameters of the electrical model associated to a cell-electrode system in Electrical Cell-substrate Impedance Spectroscopy (ECIS) technique, to the experimental results from cell-culture assays. A new parameter matching procedure is proposed, under the basis of both, mismatching between electrodes and time-evolution observed in the system response, as consequence of electrode fabrication processes and electrochemical performance of electrode-solution interface, respectively. The obtained results agree with experimental performance, and enable the evaluation of the cell number in a culture, by using the electrical measurements observed at the oscillation parameters in the test circuits employed.

Monitoring Muscle Stem Cell Cultures with Impedance Spectroscopy
Y. Yuste, J.A. Serrano, A. Olmo, A. Maldonado-Jacobi, P. Pérez, G. Huertas, S. Pereira, F. de la Portilla and A. Yúfera
Conference - International Conference on Biomedical Electronics and Devices BIODEVICES 2018
[abstract]
The aim of this work is to present a new circuit for the real-time monitoring the processes of cellular growth and differentiation of skeletal myoblast cell cultures. An impedance spectroscopy Oscillation-Based technique is proposed for the test circuit, converting the biological system into a voltage oscillator, and avoiding the use of very high performance circuitry or equipment. This technique proved to be successful in the monitoring of cell cultures growth levels and could be useful for determining the degree of differentiation achieved, of practical implications in tissue engineering.

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