Laboratoire de l'Intégration du Matériau au Système

Hardware implementations of spiking neurons can be extremely useful for a large variety of applications, ranging from high-speed modeling of large-scale neural systems to real-time behaving systems, to bidirectional brain-machine interfaces. The specific circuit solutions used to implement silicon neurons depend on the application requirements. In this paper we describe the most common building blocks and techniques used to implement these circuits, and present an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance-based Hodgkin-Huxley models to bi-dimensional generalized adaptive integrate and fire models. We compare the different design methodologies used for each silicon neuron design described, and demonstrate their features with experimental results, measured from a wide range of fabricated VLSI chips.

International audience

International audience

In the brain, learning is achieved through the ability of synapses to reconfigure the strength by which they connect neurons (synaptic plasticity). In promising solid-state synapses called memristors, conductance can be finely tuned by voltage pulses and set to evolve according to a biological learning rule called spike-timing-dependent plasticity (STDP). Future neuromorphic architectures will comprise billions of such nanosynapses, which require a clear understanding of the physical mechanisms responsible for plasticity. Here we report on synapses based on ferroelectric tunnel junctions and show that STDP can be harnessed from inhomogeneous polarization switching. Through combined scanning probe imaging, electrical transport and atomic-scale molecular dynamics, we demonstrate that conductance variations can be modelled by the nucleation-dominated reversal of domains. Based on this physical model, our simulations show that arrays of ferroelectric nanosynapses can autonomously learn to recognize patterns in a predictable way, opening the path towards unsupervised learning in spiking neural networks.

OBJECTIVE: This paper tackles the problem of transfer learning in the context of electroencephalogram (EEG)-based brain-computer interface (BCI) classification. In particular, the problems of cross-session and cross-subject classification are considered. These problems concern the ability to use data from previous sessions or from a database of past users to calibrate and initialize the classifier, allowing a calibration-less BCI mode of operation. METHODS: Data are represented using spatial covariance matrices of the EEG signals, exploiting the recent successful techniques based on the Riemannian geometry of the manifold of symmetric positive definite (SPD) matrices. Cross-session and cross-subject classification can be difficult, due to the many changes intervening between sessions and between subjects, including physiological, environmental, as well as instrumental changes. Here, we propose to affine transform the covariance matrices of every session/subject in order to center them with respect to a reference covariance matrix, making data from different sessions/subjects comparable. Then, classification is performed both using a standard minimum distance to mean classifier, and through a probabilistic classifier recently developed in the literature, based on a density function (mixture of Riemannian Gaussian distributions) defined on the SPD manifold. RESULTS: The improvements in terms of classification performances achieved by introducing the affine transformation are documented with the analysis of two BCI datasets. CONCLUSION AND SIGNIFICANCE: Hence, we make, through the affine transformation proposed, data from different sessions and subject comparable, providing a significant improvement in the BCI transfer learning problem.

This book combines theoretical concepts with experimental results on thermal microwave radiation to increase the understanding of the complex nature of terrestrial media. With the emphasis on radiative transfer models, this book covers the urgent needs for the transition from the experimental phase of microwave remote sensing to operational applications.

An air-filled substrate integrated waveguide (SIW) made of a multilayer printed circuit board process is proposed in this paper. It is of particular interest for millimeter-wave applications that generally require low cost and low-loss performance and excellent power-handling capability. This three-layered air-filled SIW allows for substantial loss reduction and power-handling capability enhancement. The top and bottom layers may make use of a low-cost standard substrate such as FR-4 on which baseband or digital circuits can be implemented so to obtain a very compact, high-performance, low-cost, and self-packaged millimeter-wave integrated system. Over Ka-band (U-band), it is shown that the air-filled SIW compared to its dielectric-filled counterparts based on Rogers substrates RT/Duroid 5880 and also 6002 reduces losses by a mean value of 0.068 dB/cm (0.098 dB/cm) and 0.104 dB/cm (0.152 dB/cm), increases average power-handling capability by 8 dB (6 dB) and 7.5 dB (5.7 dB), and quality factor by 2.7 (2.8) and 3.6 (3.8) times, respectively. The peak power-handling capability of the proposed structure is also studied. A wideband transition is presented to facilitate interconnects of the proposed air-filled SIW with dielectric-filled SIW. Design steps of this transition are detailed and its bandwidth limitation due to fabrication tolerances is theoretically examined and established. For validation purposes, a back-to-back transition operating over the Ka-band is fabricated. It achieves a return loss of better than 15 dB and an insertion loss of 0.6 ±0.2 dB ( 0.3 ±0.1 dB for the transition) from 27 to 40 GHz. Finally, two elementary circuits, namely, the T-junction and 90 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> hybrid coupler based on the air-filled SIW, are also demonstrated.

Grapevine wood fungal diseases such as esca are among the biggest threats in vineyards nowadays. The lack of very efficient preventive (best results using commercial products report 20% efficiency) and curative means induces huge economic losses. The study presented in this paper is centered around the in-field detection of foliar esca symptoms during summer, exhibiting a typical “striped” pattern. Indeed, in-field disease detection has shown great potential for commercial applications and has been successfully used for other agricultural needs such as yield estimation. Differentiation with foliar symptoms caused by other diseases or abiotic stresses was also considered. Two vineyards from the Bordeaux region (France, Aquitaine) were chosen as the basis for the experiment. Pictures of diseased and healthy vine plants were acquired during summer 2017 and labeled at the leaf scale, resulting in a patch database of around 6000 images (224 × 224 pixels) divided into red cultivar and white cultivar samples. Then, we tackled the classification part of the problem comparing state-of-the-art SIFT encoding and pre-trained deep learning feature extractors for the classification of database patches. In the best case, 91% overall accuracy was obtained using deep features extracted from MobileNet network trained on ImageNet database, demonstrating the efficiency of simple transfer learning approaches without the need to design an ad-hoc specific feature extractor. The third part aimed at disease detection (using bounding boxes) within full plant images. For this purpose, we integrated the deep learning base network within a “one-step” detection network (RetinaNet), allowing us to perform detection queries in real time (approximately six frames per second on GPU). Recall/Precision (RP) and Average Precision (AP) metrics then allowed us to evaluate the performance of the network on a 91-image (plants) validation database. Overall, 90% precision for a 40% recall was obtained while best esca AP was about 70%. Good correlation between annotated and detected symptomatic surface per plant was also obtained, meaning slightly symptomatic plants can be efficiently separated from severely attacked plants.

The recently-discovered polar codes are widely seen as a major breakthrough in coding theory. These codes achieve the capacity of many important channels under successive cancellation decoding. Motivated by the rapid progress in the theory of polar codes, we pro pose a family of architectures for efficient hardware implementation of successive cancellation decoders. We show that such decoders can be implemented with O(n) processing elements and O(n) memory elements, while providing constant throughput. We also pro pose a technique for overlapping the decoding of several consecutive codewords, thereby achieving a significant speed-up factor. We furthermore show that successive cancellation decoding can be implemented in the logarithmic domain, thereby eliminating the multiplication and division operations and greatly reducing the complexity of each processing element.

International audience

The inefficient use of phosphorus (P) in the food chain is a threat to the global aquatic environment and the health and well-being of citizens, and it is depleting an essential finite natural resource critical for future food security and ecosystem function. We outline a strategic framework of 5R stewardship (Re-align P inputs, Reduce P losses, Recycle P in bioresources, Recover P in wastes, and Redefine P in food systems) to help identify and deliver a range of integrated, cost-effective, and feasible technological innovations to improve P use efficiency in society and reduce Europe's dependence on P imports. Their combined adoption facilitated by interactive policies, co-operation between upstream and downstream stakeholders (researchers, investors, producers, distributors, and consumers), and more harmonized approaches to P accounting would maximize the resource and environmental benefits and help deliver a more competitive, circular, and sustainable European economy. The case of Europe provides a blueprint for global P stewardship.

Memristive devices present a new device technology allowing for the realization of compact non-volatile memories. Some of them are already in the process of industrialization. Additionally, they exhibit complex multilevel and plastic behaviors, which make them good candidates for the implementation of artificial synapses in neuromorphic engineering. However, memristive effects rely on diverse physical mechanisms, and their plastic behaviors differ strongly from one technology to another. Here, we present measurements performed on different memristive devices and the opportunities that they provide. We show that they can be used to implement different learning rules whose properties emerge directly from device physics: real time or accelerated operation, deterministic or stochastic behavior, long term or short term plasticity. We then discuss how such devices might be integrated into a complete architecture. These results highlight that there is no unique way to exploit memristive devices in neuromorphic systems. Understanding and embracing device physics is the key for their optimal use.

Printed organic photodetectors can transform plastic, paper or glass into smart surfaces. This innovative technology is now growing exponentially due to the strong demand in human-machine interfaces. To date, only niche markets are targeted since organic sensors still present reduced performances in comparison with their inorganic counterparts. Here we demonstrate that it is possible to engineer a state-of-the-art organic photodetector approaching the performances of Si-based photodiodes in terms of dark current, responsivity and detectivity. Only three solution-processed layers and two low-temperature annealing steps are needed to achieve the performance that is significantly better than most of the organic photodetectors reported so far. We also perform a long-term ageing study. Lifetimes of over 14,000 hours under continuous operation are more than promising and demonstrate that organic photodetectors can reach a competitive level of stability for successful commercialization of this new and promising technology.

Three-dimensional optical recording by laser-induced fluorescent silver clusters is demonstrated in glass. The fluorescence properties of these stable clusters can be altered, depending on the glass recording exposure conditions. A “Blu-ray”-like drive enables readout of the information inside the glass without cross-talk and photobleaching (see figure). This original recording medium can provide an answer to the societal problem of long-term high-density data storage.

Photovoltaic generation has stepped up within the last decade from outsider status to one of the important contributors of the ongoing energy transition, with about 1.7% of world electricity provided by solar cells. Progress in materials and production processes has played an important part in this development. Yet, there are many challenges before photovoltaics could provide clean, abundant, and cheap energy. Here, we review this research direction, with a focus on the results obtained within a Japan-French cooperation program, NextPV, working on promising solar cell technologies. The cooperation was focused on efficient photovoltaic devices, such as multijunction, ultrathin, intermediate band, and hot-carrier solar cells, and on printable solar cell materials such as colloidal quantum dots.

This techical note deals with interval state observer design for time-varying discrete-time systems. The problem of a similarity transformation computation which connects a (time-varying) matrix and its nonnegative representation is studied. Three solutions are proposed: for a generic time-varying system, a system with positive state, and for a particular class of periodical systems. Numerical simulations are provided to demonstrate advantages of the developed techniques.

International audience

The application of pulsed lasers to the study of Single-Event Effects (SEEs) in integrated circuits and devices is described. The role of a pulsed laser is to provide spatial and temporal information about SEEs, information that is not available when broad-beam ion sources are used. A detailed description is given of the mechanisms involved, including light propagation and absorption by both linear and non-linear processes. Numerous examples highlight the versatility and usefulness of the technique in the study of SEEs.

A novel stable bisazide molecule that can freeze the bulk heterojunction morphology at its optimized layout by specifically bonding to fullerenes is reported. The concept is demonstrated with various polymers: fullerene derivatives systems enable highly thermally stable polymer solar cells.

This paper adresses the statistical performance of subspace DoA estimation using a sensor array, in the asymptotic regime where the number of samples and sensors both converge to infinity at the same rate. Improved subspace DoA estimators were derived (termed as G-MUSIC) in previous works, and were shown to be consistent and asymptotically Gaussian distributed in the case where the number of sources and their DoA remain fixed. In this case, which models widely spaced DoA scenarios, it is proved in the present paper that the traditional MUSIC method also provides DoA consistent estimates having the same asymptotic variances as the G-MUSIC estimates. The case of DoA that are spaced of the order of a beamwidth, which models closely spaced sources, is also considered. It is shown that G-MUSIC estimates are still able to consistently separate the sources, while it is no longer the case for the MUSIC ones. The asymptotic variances of G-MUSIC estimates are also evaluated.