Spanish National Research Council · University of Seville
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Low-Power Compensated Modified Comb Decimation Structure for Power-of-Two Decimation Factors
G.J. Dolecek and J.M. de la Rosa
Conference - IEEE Latin American Symposium on Circuits and Systems LASCAS 2021
This paper presents a low power non-recursive compensated modified comb decimation structure. A new simple wideband compensator for a modified comb is proposed. The compensator has only three coefficients, presented in a Signed Power of Two (SPT) form, which can be implemented by adders and shifts. As a result, we get a multiplierless compensator. Since the modified comb is a multiplierless filter, the overall filter is, like a comb filter, also a multiplierless filter. The compensated modified comb decreases comb-filter passband droop and improves its alias rejection. The benefits of the compensated modified comb are proven by the comparisons with the state of the art.

Effects of Electrical Fields on Neuroblastoma (N2A) Cell Differentiation: Preliminary Results
D. Martin-Fernández, P. Pérez-García, M.E. Martín, P. Daza, J.A. Serrano-Viseas, G. Huertas and A. Yúfera
Conference - International Conference on Biomedical Electronics and Devices BIODEVICES 2021
This work describes Electrical Stimulations (ES) assays on stem cells. The neuroblastoma (N2A) cell linage was submitted to several electrical fields to enable and enhance its differentiation toward neurons. Both Direct Current (DC) and Alternated Current (AC) time dependent electric field protocols were applied to N2A cell culture under differentiation conditions, obtaining different responses. Control and electrically excited samples’ number of differentiated cells and neurite lengths were measure after differentiation. Results showed that DC fields have a strong influence on N2A differentiation since the percentage of differentiated cells and the neurites lengths were the highest. In addition, a significant alignment of neurites measured with the applied electrical field has been detected, which demonstrates the high sensitivity of differentiation processes to electrical field polarity.

Foveal-pit inspired filtering of DVS spike response
S.T.P. Gupta, P. Linares-Serrano, B.S. Bhattacharya and T. Serrano-Gottaredona
Conference - Annual Conference on Information Sciences and Systems CISS 2021
In this paper, we present results of processing Dynamic Vision Sensor (DVS) recordings of visual patterns with a retinal model based on foveal-pit inspired Difference of Gaussian (DoG) filters. A DVS sensor was stimulated with varying number of vertical white and black bars of different spatial frequencies moving horizontally at a constant velocity. The output spikes generated by the DVS sensor were applied as input to a set of DoG filters inspired by the receptive field structure of the primate visual pathway. In particular, these filters mimic the receptive fields of the midget and parasol ganglion cells (spiking neurons of the retina) that sub-serve the photo-receptors of the fovealpit. The features extracted with the foveal-pit model are used for further classification using a spiking convolutional neural network trained with a backpropagation variant adapted for spiking neural networks.

Oscillatory Neural Networks using VO2 based Phase Encoded Logic
J. Núñez, M.J. Avedillo, M. Jiménez, J.M. Quintana, A. Todri-Sanial, E. Corti, S. Karg and B. Linares-Barranco
Journal Paper - Frontiers in Neuroscience, vol. 15, article 655823, 2021
FRONTIERS MEDIA    DOI: 10.3389/fnins.2021.655823    ISSN: 1662-453X    » doi
Nano-oscillators based on phase-transition materials are being explored for the implementation of different non-conventional computing paradigms. In particular, vanadium dioxide (VO 2) devices are used to design autonomous non-linear oscillators from which oscillatory neural networks (ONNs) can be developed. In this work, we propose a new architecture for ONNs in which sub-harmonic injection locking (SHIL) is exploited to ensure that the phase information encoded in each neuron can only take two values. In this sense, the implementation of ONNs from neurons that inherently encode information with two-phase values has advantages in terms of robustness and tolerance to variability present in VO2 devices. Unlike conventional interconnection schemes, in which the sign of the weights is coded in the value of the resistances, in our proposal the negative (positive) weights are coded using static inverting (non-inverting) logic at the output of the oscillator. The operation of the proposed architecture is shown for pattern recognition applications.

The Use of High-Intensity Focused Ultrasound for the Rewarming of Cryopreserved Biological Material
A. Olmo, P. Barroso, F. Barroso and R. Risco
Journal Paper - IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control, vol. 68, no. 3, pp 599-607, 2021
IEEE    DOI: 10.1109/TUFFC.2020.3016950    ISSN: 0885-3010    » doi
High-intensity focused ultrasound (HIFU) has been used in different medical applications in the last years. In this work, we present for the first time the use of HIFU in the field of cryopreservation, the preservation of biological material at low temperatures. An HIFU system has been designed with the objective of achieving a fast and uniform rewarming in organs, key to overcome the critical problem of devitrification. The finite-element simulations have been carried out using COMSOL Multiphysics software. An array of 26 ultrasonic transducers was simulated, achieving an HIFU focal area in the order of magnitude of a model organ (ovary). A parametric study of the warming rate and temperature gradients, as a function of the frequency and power of ultrasonic waves, was performed. An optimal value for these parameters was found. The results validate the appropriateness of the technique, which is of utmost importance for the future creation of cryopreserved organ banks.

Efficient Spike-Driven Learning With Dendritic Event-Based Processing
S. Yang, T. Gao, J. Wang, B. Deng, B. Lansdell and B. Linares-Barranco
Journal Paper - Frontiers in Neuroscience, vol. 15, article 601109, 2021
FRONTIERS MEDIA    DOI: 10.3389/fnins.2021.601109    ISSN: 1662-453X    » doi
A critical challenge in neuromorphic computing is to present computationally efficient algorithms of learning. When implementing gradient-based learning, error information must be routed through the network, such that each neuron knows its contribution to output, and thus how to adjust its weight. This is known as the credit assignment problem. Exactly implementing a solution like backpropagation involves weight sharing, which requires additional bandwidth and computations in a neuromorphic system. Instead, models of learning from neuroscience can provide inspiration for how to communicate error information efficiently, without weight sharing. Here we present a novel dendritic event-based processing (DEP) algorithm, using a two-compartment leaky integrate-and-fire neuron with partially segregated dendrites that effectively solves the credit assignment problem. In order to optimize the proposed algorithm, a dynamic fixed-point representation method and piecewise linear approximation approach are presented, while the synaptic events are binarized during learning. The presented optimization makes the proposed DEP algorithm very suitable for implementation in digital or mixed-signal neuromorphic hardware. The experimental results show that spiking representations can rapidly learn, achieving high performance by using the proposed DEP algorithm. We find the learning capability is affected by the degree of dendritic segregation, and the form of synaptic feedback connections. This study provides a bridge between the biological learning and neuromorphic learning, and is meaningful for the real-time applications in the field of artificial intelligence.

Neuromorphic Low-power Inference on Memristive Crossbars with On-chip Offset Calibration
C. Mohan, L.A. Camuñas-Mesa, J.M. de la Rosa, E. Vianello, T. Serrano-Gotarredona and B. Linares-Barranco
Journal Paper - IEEE Access, first online, 2021
IEEE    DOI: 10.1109/ACCESS.2021.3063437    ISSN: 2169-3536    » doi
Monolithic integration of silicon with nano-sized Redox-based resistive Random-Access Memory (ReRAM) devices opened the door to the creation of dense synaptic connections for bio-inspired neuromorphic circuits. One drawback of OxRAM based neuromorphic systems is the relatively low ON resistance of OxRAM synapses (in the range of just a few kilo-ohms). This requires relatively large currents (many micro amperes per synapse), and therefore imposes strong driving capability demands on peripheral circuitry, limiting scalability and low power operation. After learning, however, a read inference can be made low-power by applying very small amplitude read pulses, which require much smaller driving currents per synapse. Here we propose and experimentally demonstrate a technique to reduce the amplitude of read inference pulses in monolithic neuromorphic CMOS OxRAM-synaptic crossbar systems. Unfortunately, applying tiny read pulses is non-trivial due to the presence of random DC offset voltages. To overcome this, we propose finely calibrating DC offset voltages using a bulk-based three-stage on-chip calibration technique. In this work, we demonstrate spiking pattern recognition using STDP learning on a small 4x4 proof-of-concept memristive crossbar, where on-chip offset calibration is implemented and inference pulse amplitude could be made as small as 2mV. A chip with pre-synaptic calibrated input neuron drivers and a 4x4 1T1R synapse crossbar was designed and fabricated in the CEA-LETI MAD200 technology, which uses monolithic integration of OxRAMs above ST130nm CMOS. Custom-made PCBs hosting the post-synaptic circuits and control FPGAs were used to test the chip in different experiments, including synapse characterization, template matching, and pattern recognition using STDP learning, and to demonstrate the use of on-chip offset-calibrated low-power amplifiers. According to our experiments, the minimum possible inference pulse amplitude is limited by offset voltage drifts and noise. We conclude the paper with some suggestions for future work in this direction.

Behavioral and Physical Unclonable Functions (BPUFs): SRAM Example
M.A. Prada-Delgado and I. Baturone
Journal Paper - IEEE Access, vol. 9, pp 23751-23763, 2021
IEEE    DOI: 10.1109/ACCESS.2021.3055493    ISSN: 2169-3536    » doi
Physical Unclonable Functions (PUFs) have gained a great interest for their capability to identify devices uniquely and to be a lightweight primitive in cryptographic protocols. However, several reported attacks have shown that virtual copies (mathematical clones) as well as physical clones of PUFs are possible, so that they cannot be considered as tamper-resistant or tamper-evident, as claimed. The solution presented in this article is to extend the PUFs reported until now, which are only physical, to make them Behavioral and Physical Unclonable Functions (BPUFs). Given a challenge, BPUFs provide not only a physical but also a behavioral distinctive response caused by manufacturing process variations. Hence, BPUFs are more difficult to attack than PUFs since physical and behavioral responses associated to challenges have to be predicted or cloned. Behavioral responses that are obtained from several measurements of the physical responses taken at several sample times are proposed. In this way, the behavioral responses can detect if the physical responses are manipulated. The analysis done for current PUFs is extended to allow for more versatility in the responses that can be considered in BPUFs. Particularly, Jaccard instead of Hamming distances are proposed to evaluate the similarity of behavioral responses. As example to validate the proposed solution, BPUFs based on Static Random-Access Memories (SRAM BPUFs), with one physical and one behavioral responses to given challenges, were analyzed experimentally using integrated circuits fabricated in a 90-nm CMOS technology. If an attacker succeeds in cloning the physical responses as reported, but does not attack the way to obtain the behavioral responses, the attacker fails on SRAM BPUFs. The highest probability to succeed in cloning the behavioral responses with a brute-force attack was estimated from experimental results as $1.5 \cdot 10^{-34}$ , considering the influence of changes in the operating conditions (power supply voltage, temperature, and aging).

Improving the reliability of SRAM-based PUFs under varying operation conditions and aging degradation
P. Saraza-Canflanca, H. Carrasco-Lopez, A. Santana-Andreo, P. Brox, R. Castro-Lopez, E. Roca and F.V. Fernandez
Journal Paper - Microelectronics Reliability, vol 118, article 114049, 2021
ELSEVIER    DOI: 10.1016/j.microrel.2021.114049    ISSN: 0026-2714    » doi
The utilization of power-up values in SRAM cells to generate PUF responses for chip identification is a subject of intense study. The cells used for this purpose must be stable, i.e., the cell should always power-up to the same value (either ‘0’ or ‘1’). Otherwise, they would not be suitable for the identification. Some methods have been presented that aim at increasing the reliability of SRAM PUFs by identifying the strongest cells, i.e., the cells that more consistently power-up to the same value. However, these methods present some drawbacks, in terms of either their practical realization or their actual effectiveness in selecting the strongest cells at different scenarios, such as temperature variations or when the circuits have suffered aging-related degradation. In this work, the experimental results obtained for a new method to classify the cells according to their power-up strength are presented and discussed. The method overcomes some of the drawbacks in previously reported methods. In particular, it is experimentally demonstrated that the technique presented in this work outstands in selecting SRAM cells that are very robust against circuit degradation and temperature variations, which ultimately translates into the construction of reliable SRAM-based PUFs.

Design of High-Efficiency SPADs for LiDAR Applications in 110nm CIS Technology
I. Vornicu, J.M. López-Martínez, F.N. Bandi, R. Carmona-Galán and A. Rodríguez-Vázquez
Journal Paper - IEEE Sensors Journal, vol. 21, no. 4, pp 4776-4785, 2021
IEEE    DOI: 10.1109/JSEN.2020.3032106    ISSN: 1530-437X    » doi
Single photon avalanche diodes (SPADs) featuring a high detection rate of near-IR photons are much desired for outdoor LiDAR based on direct time-of-flight (ToF). This article presents the complete design flow of a SPAD detector for LiDAR. First, the selection of the emitter wavelength is discussed, considering the maximum allowed power underlying eye safety regulations, solar irradiance, and reflected signal power. Then, the choice of the SPAD structure is discussed based on the TCAD simulation of quantum efficiency and crosstalk. Next, the proposed P-well/Deep N-well SPAD is explained. The electro-optical characterization of the detectors is presented as well. The performance of the time-of-flight image sensors is determined by the characteristics of the individual SPADs. To fully characterize this technology, devices with various sizes, shapes, and guard ring widths have been fabricated and tested. The measured mean breakdown voltage is 18 V. The proposed structure has a 0.4 Hz/µ m2 dark count rate and 0.5% afterpulsing. The FWHM (total) jitter and photon detection probability at 850nm wavelength are of 92 ps and 10%. All figures have been measured at 3 V excess voltage. Finally, the performance of the SPAD detector is analyzed by evaluating the signal-to-noise ratio at different acquisition times. Distance ranging measurements have been performed, achieving a depth resolution of 1 cm up to 6.3 m range.

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