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A smart noise- and RTN-removal method for parameter extraction of CMOS aging compact models
J. Diaz-Fortuny, J. Martin-Martinez, R. Rodriguez, R. Castro-Lopez, E. Roca, F.V. Fernandez and M. Nafria
Journal Paper - Solid-State Electronics, first online, 2019
ELSEVIER    DOI: 10.1016/j.sse.2019.03.045    ISSN: 0038-1101    » doi
[abstract]
In modern nanometer-scale CMOS technologies, time-zero and time-dependent variability (TDV) effects, the latter coming from aging mechanisms like Bias Temperature Instability (BTI), Hot Carrier Injection (HCI) or Random Telegraph Noise (RTN), have re-emerged as a serious threat affecting the performance of analog and digital integrated circuits. Variability induced by the aging phenomena can lead circuits to a progressive malfunction or failure. In order to understand the effects of the mentioned variability sources, a precise and sound statistical characterization and modeling of these effects should be done. Typically, transistor TDV characterization entails long, and typically prohibitive, testing times, as well as huge amounts of data, which are complex to post-process. In order to face these limitations, this work presents a new method to statistically characterize the emission times and threshold voltage shifts (ΔVth) related to oxide defects in nanometer CMOS transistors during aging tests. At the same time, the aging testing methodology significantly reduces testing times by parallelizing the stress. The method identifies the Vth drops associated to oxide trap emissions during BTI and HCI aging recovery traces while removing RTN and background noise contributions, to avoid artifacts during data analysis.

Flexible Setup for the Measurement of CMOS Time-Dependent Variability with Array-Based Integrated Circuits
J. Diaz-Fortuny, P. Saraza-Canflanca, R. Castro-Lopez, E. Roca, J. Martin-Martinez, R. Rodriguez, F.V. Fernandez and M. Nafria
Journal Paper - IEEE Transactions on Instrumentation and Measurement, first online, 2019
IEEE    DOI: 10.1109/TIM.2019.2906415    ISSN: 0018-9456    » doi
[abstract]
This paper presents an innovative and automated measurement setup for the characterization of variability effects in CMOS transistors using array-based integrated circuits (ICs), through which a better understanding of CMOS reliability could be attained. This setup addresses the issues that come with the need for a trustworthy statistical characterization of these effects: testing a very large number of devices accurately but, also, in a timely manner. The setup consists of software and hardware components that provide a user-friendly interface to perform the statistical characterization of CMOS transistors. Five different electrical tests, comprehending time-zero and time-dependent variability effects, can be carried out. Test preparation is, with the described setup, reduced to a few seconds. Moreover, smart parallelization techniques allow reducing the typically time-consuming aging characterization from months to days or even hours. The scope of this paper thus encompasses the methodology and practice of measurement of CMOS time-dependent variability, as well as the development of appropriate measurement systems and components used in efficiently generating and acquiring the necessary electrical signals.

A Sub- µW Reconfigurable Front-End for Invasive Neural Recording
J.L. Valtierra, R. Fiorelli, M. Delgado-Restituto and A. Rodriguez-Vazquez
Conference - IEEE Latin American Symposium on Circuits and Systems LASCAS 2019
[abstract]
This paper presents a sub-microwatt ac-coupled neural amplifier for the purpose of neural signal sensing. A proposed reconfigurable topology embeds in it filtering capabilities allowing it to select among different frequency bands inside the neural signal spectrum. Power consumption is optimized by designing for bandwidth-specific noise targets that take into account the spectral characteristics of the input signal as well as the noise bandwidths of the noise generators in the circuit itself. An experimentally verified prototype designed in a 180nm CMOS process draws 803nW from a 1V source. The measured input-referred spot-noise at 150Hz is 130nV / Hz while the integrated noise in the 200Hz-5kHz band is 3.6µV rms.

Conversion of Synchronous Artificial Neural Network to Asynchronous Spiking Neural Network using sigma-delta quantization
A. Yousefzadeh, S. Hosseini, P. Holanda, S. Leroux, T. Werner, T. Serrano-Gotarredona and B. Linares-Barranco, B. Dhoedt and P. Simoens
Conference - IEEE International Conference on Artificial Intelligence Circuits and Systems AICAS 2019
[abstract]
Artificial Neural Networks (ANNs) show great performance in several data analysis tasks including visual and auditory applications. However, direct implementation of these algorithms without considering the sparsity of data requires high processing power, consume vast amounts of energy and suffer from scalability issues. Inspired by biology, one of the methods which can reduce power consumption and allow scalability in the implementation of neural networks is asynchronous processing and communication by means of action potentials, so-called spikes. In this work, we use the wellknown sigma-delta quantization method and introduce an easy and straightforward solution to convert an Artificial Neural Network to a Spiking Neural Network which can be implemented asynchronously in a neuromorphic platform. Briefly, we used asynchronous spikes to communicate the quantized output activations of the neurons. Despite the fact that our proposed mechanism is simple and applicable to a wide range of different ANNs, it outperforms the state-of-the-art implementations from the accuracy and energy consumption point of view. All source code for this project is available upon request for the academic purpose.

A Digital Neuromorphic Realization of the 2-D Wilson Neuron Model
M. Nouri, M. Hayati, T. Serrano-Gotarredona and D. Abbot
Journal Paper - IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 66, no. 1, 2019
IEEE    DOI: 10.1109/TCSII.2018.2852598    ISSN: 1549-7747    » doi
[abstract]
This brief presents a piecewise linear approximation of the nonlinear Wilson (NW) neuron model for the realization of an efficient digital circuit implementation. The accuracy of the proposed piecewise Wilson (PW) model is examined by calculating time domain signal shaping errors. Furthermore, bifurcation analyses demonstrate that the approximation follows the same bifurcation pattern as the NW model. As a proof of concept, both models are hardware synthesized and implemented on field programmable gate arrays, demonstrating that the PW model has a range of neuronal behaviors similar to the NW model with considerably higher computational performance and a lower hardware overhead. This approach can be used in hardware-based large scale biological neural network simulations and behavioral studies. The mean normalized root mean square error and maximum absolute error of the PW model are 6.32% and 0.31%, respectively, as compared to the NW model.

Compressive Imaging using RIP-compliant CMOS Imager Architecture and Landweber Reconstruction
M. Trevisi, A. Akbari, M. Trocan, Á. Rodríguez-Vázquez and R. Carmona-Galán
Journal Paper - IEEE Transactions on Circuits and Systems for Video Technology, first online, 2019
IEEE    DOI: 10.1109/TCSVT.2019.2892178    ISSN: 1051-8215    » doi
[abstract]
In this paper we present a new image sensor architecture for fast and accurate compressive sensing (CS) of natural images. Measurement matrices usually employed in compressive sensing CMOS image sensors (CS-CIS) are recursive pseudo-random binary matrices. We have proved that the restricted isometry property (RIP) of these matrices is limited by a low sparsity constant. The quality of these matrices is also affected by the non-idealities of pseudo-random numbers generators (PRNG). To overcome these limitations, we propose a hardware-friendly pseudo-random ternary measurement matrix generated on-chip by means of class III elementary cellular automata (ECA). These ECA present a chaotic behaviour that emulates random CS measurement matrices better than other PRNG. We have combined this new architecture with a block-based CS smoothed-projected Landweber (BCS-SPL) reconstruction algorithm. By means of single value decomposition (SVD) we have adapted this algorithm to perform fast and precise reconstruction while operating with binary and ternary matrices. Simulations are provided to qualify the approach.

Offset-calibration with Time-Domain Comparators using Inversion-mode Varactors
R. Fiorelli, M. Delgado-Restituto and A Rodríguez-Vázquez
Journal Paper - IEEE Transactions on Circuits and Systems II: Express Brief, first online, 2019
IEEE    DOI: 10.1109/TCSII.2019.2904100    ISSN: 1549-7747    » doi
[abstract]
This paper presents a differential time-domain comparator formed by two voltage controlled delay lines, one per input terminal, and a binary phase detector for comparison solving. The propagation delay through the respective lines can be adjusted with a set of digitally-controlled inversion-mode varactors. These varactors provide tuning capabilities to the comparator; feature which can be exploited for offset calibration. This is demonstrated with the implementation of a differential 10-bit SAR-ADC. The design, fabricated in a 0.18μm CMOS process, includes an automatic mechanism for adjusting the capacitance of the varactors in order to calibrate the offset of the whole converter. Correct functionality was measured in all samples.

Fast adaptive comparator offset calibration in pipeline ADC with self-repairing thermometer to binary encoder
A.J. Ginés, E. Peralías, C. Aledo and A. Rueda
Journal Paper - International Journal of Circuit Theory and Applications, vol. 47, no. 3, pp 333-349, 2019
JOHN WILEY & SONS    DOI: 10.1002/cta.2594    ISSN: 0098-9886    » doi
[abstract]
This paper presents a fast background calibration method for comparator offsets in pipeline ADCs and analyzes the practical considerations in a 1.8 V 0.18 μm 100Msps pipeline ADC with 15-bit resolution (74 dB-Signal-to-noise plus Distortion Ratio [SNDR]). A self-repairing (SR) thermometer-to-binary encoder is developed to deal with malfunctioning in presence of high comparator offsets greater than one-half least-significant bit (LSB). In this situation, the effective thresholds between two adjacent comparators could be inverted leading to a faulty behavior with conventional architectures. The proposed solution allows a dynamic assignment of the calibration code associated to each comparator improving convergence speed. As demonstrator, its application to a 15-bit pipeline ADC using a novel calibrated dynamic-latch comparator (DLC) with internal threshold reference generation and no preamplifier is presented, showing a reduction on the total power consumption of 22% with respect to a design without calibration targeting the same specifications.

Assessing AMS-RF test quality by defect simulation
V. Gutierrez, A. Gines and G. Leger
Journal Paper - IEEE Transactions on Device and Materials Reliability, vol. 19, no. 1, pp 55-63, 2019
IEEE    DOI: 10.1109/TDMR.2019.2894534    ISSN: 1530-4388    » doi
[abstract]
In safety critical applications there is a demand for estimating defect coverage in order to meet stringent quality levels. However, defect simulation of complex AMS-RF circuits is computationally expensive since achieving good confidence interval requires sampling many defects. In this paper, we show on practical cases of study that it is beneficial to complement defect coverage with fault coverage and assess the severity of defect escapes to get a complete picture of test quality. The computational burden of defect and fault simulations is taken into account and accurate statistical estimates of defect and fault escapes are provided to allow safe early stopping of the simulations.

A Multilevel Bottom-up Optimization Methodology for the Automated Synthesis of RF Systems
F. Passos, E. Roca, J. Sieiro, R. Fiorelli, R. Castro-López, J.M. López-Villegas and F.V. Fernández
Journal Paper - IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, first online, 2019
IEEE    DOI: 10.1109/TCAD.2018.2890528    ISSN: 0278-0070    » doi
[abstract]
In recent years there has been a growing interest in electronic design automation methodologies for the optimizationbased design of radiofrequency circuits and systems. While for simple circuits several successful methodologies have been proposed, these very same methodologies exhibit significant deficiencies when the complexity of the circuit is increased. The majority of the published methodologies that can tackle radiofrequency systems are either based on high-level system specification tools or use models to estimate the system performances. Hence, such approaches do not usually provide the desired accuracy for RF systems. In this work, a methodology based on hierarchical multilevel bottom-up design approaches is presented, where multi-objective optimization algorithms are used to design an entire radiofrequency system from the passive component level up to the system level. Furthermore, each level of the hierarchy is simulated with the highest accuracy possible: electromagnetic simulation accuracy at device-level and electrical simulations at circuit/system-level.

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