arXiv subjects

Fabien Alibart

Publications and source records attributed to Fabien Alibart.

At least 19 recordsLinked to original sources

arXiv2026

On Electropolymerized Fingerprints and their Potential for Identification and Encryption

While human technology is ruled by determinism, biological systems exploit a subtle balance of control and stochasticity. This balance, evident in the morphogenesis of textural patterns imprinted on leaves, fur or skin can help hierarchize organisms both as a representative of their species and as unique individuals. In this study, we identified that, by exploiting electrochemistry, it is possible to generate such versatile but specific textures, to imprint patterns of a conducting polymer on a conducting substrate. It is shown that the 1D morphogenesis of conducting polymer dendrites on wires translates, on 2D surfaces, as highly heterogeneous coatings of dark spots, rosettes or marbled patterns. Despite their inherent stochasticity, these patterns are characteristic of the physical conditions they grew in, and particularly of the chemical content of the electroactive solution used for their electropolymerization. A statistical study demonstrates that these patterns could be used as fingerprints to physically tag the identity of a solution within a specific class. By the identification of a new electrochemical process which allows generating physical fingerprints with optical, electrical and chemical contrast on an electrode, this research paves the way toward a disruptive low-cost technology which could allow any end-user to generate personal tags on a glass slide or on a micro-chip, to engrave physically-encrypted personal information for various applications.

physics.chem-ph↗

arXiv2025

Unsupervised local learning based on voltage-dependent synaptic plasticity for resistive and ferroelectric synapses

The deployment of AI on edge computing devices faces significant challenges related to energy consumption and functionality. These devices could greatly benefit from brain-inspired learning mechanisms, allowing for real-time adaptation while using low-power. In-memory computing with nanoscale resistive memories may play a crucial role in enabling the execution of AI workloads on these edge devices. In this study, we introduce voltage-dependent synaptic plasticity (VDSP) as an efficient approach for unsupervised and local learning in memristive synapses based on Hebbian principles. This method enables online learning without requiring complex pulse-shaping circuits typically necessary for spike-timing-dependent plasticity (STDP). We show how VDSP can be advantageously adapted to three types of memristive devices (TiO$_2$, HfO$_2$-based metal-oxide filamentary synapses, and HfZrO$_4$-based ferroelectric tunnel junctions (FTJ)) with disctinctive switching characteristics. System-level simulations of spiking neural networks incorporating these devices were conducted to validate unsupervised learning on MNIST-based pattern recognition tasks, achieving state-of-the-art performance. The results demonstrated over 83% accuracy across all devices using 200 neurons. Additionally, we assessed the impact of device variability, such as switching thresholds and ratios between high and low resistance state levels, and proposed mitigation strategies to enhance robustness.

cs.NE↗

arXiv2025

Chemical Control for the Morphogenesis of Conducting Polymer Dendrites in Water

Conducting polymer dendrite (CPD) morphogenesis is an electrochemical process that unlocks the potential to implement in materio evolving intelligence in electrical systems: As an electronic device experiences transient voltages in an open-space wet environment, electrically conductive structures physically change over time, programming the filtering properties of an interconnect as a non-linear analog device. Mimicking the self-preservation strategies of some sessile organisms, CPDs adapt their morphology to the environment they grow in. Either studied as an electrochemical experiment or as neuromorphic devices, the dependence of CPDs' electrical properties on the chemical nature of their environment is still unreported, despite the inter-dependence between the electrical properties of the electrogenerated material and the chemical composition of their growth medium. In this study, we report on the existing intrication between the nature and concentration of the electrolytes, electroactive compounds and co-solvents and the electrical and the electrochemical properties of CPDs in water. CPDs exhibit various chemical sensitivities in water: their morphology is highly dependent on the nature of the chemical resources available in their environment. The selection of these resources therefore critically influence morphogenesis. Also, concentrations have different impacts on growth dynamics, conditioning the balance between thermodynamic and kinetic control on polymer electrosynthesis. By correlating the dependencies of these evolving objects with the availability of the chemical resources in an aqueous environment, this study proposes guidelines to tune the degree of evolution of electronic materials in water. Such hardware is envisioned to exploit the chemical complexity of real world environments as part of information processing technologies.

physics.chem-ph↗

arXiv2025

Unsupervised Sparse Coding-based Spiking Neural Network for Real-time Spike Sorting

Spike sorting is a crucial step in decoding multichannel extracellular neural signals, enabling the identification of individual neuronal activity. A key challenge in brain-machine interfaces (BMIs) is achieving real-time, low-power spike sorting at the edge while keeping high neural decoding performance. This study introduces the Neuromorphic Sparse Sorter (NSS), a compact two-layer spiking neural network optimized for efficient spike sorting. NSS leverages the Locally Competitive Algorithm (LCA) for sparse coding to extract relevant features from noisy events with reduced computational demands. NSS learns to sort detected spike waveforms in an online fashion and operates entirely unsupervised. To exploit multi-bit spike coding capabilities of neuromorphic platforms like Intel's Loihi 2, a custom neuron model was implemented, enabling flexible power-performance trade-offs via adjustable spike bit-widths. Evaluations on simulated and real-world tetrode signals with biological drift showed NSS outperformed established pipelines such as WaveClus3 and PCA+KMeans. With 2-bit graded spikes, NSS on Loihi 2 outperformed NSS implemented with leaky integrate-and-fire neuron and achieved an F1-score of 77% (+10% improvement) while consuming 8.6mW (+1.65mW) when tested on a drifting recording, with a computational processing time of 0.25ms (+60 us) per inference.

cs.NE↗

arXiv2025

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices

Analog in-memory computing is an emerging paradigm designed to efficiently accelerate deep neural network workloads. Recent advancements have focused on either inference or training acceleration. However, a unified analog in-memory technology platform-capable of on-chip training, weight retention, and long-term inference acceleration-has yet to be reported. This work presents an all-in-one analog AI accelerator, combining these capabilities to enable energy-efficient, continuously adaptable AI systems. The platform leverages an array of analog filamentary conductive-metal-oxide (CMO)/HfOx resistive switching memory cells (ReRAM) integrated into the back-end-of-line (BEOL). The array demonstrates reliable resistive switching with voltage amplitudes below 1.5V, compatible with advanced technology nodes. The array multi-bit capability (over 32 stable states) and low programming noise (down to 10nS) enable a nearly ideal weight transfer process, more than an order of magnitude better than other memristive technologies. Inference performance is validated through matrix-vector multiplication simulations on a 64x64 array, achieving a root-mean-square error improvement by a factor of 20 at 1 second and 3 at 10 years after programming, compared to state-of-the-art. Training accuracy closely matching the software equivalent is achieved across different datasets. The CMO/HfOx ReRAM technology lays the foundation for efficient analog systems accelerating both inference and training in deep neural networks.

cs.ET↗

arXiv2025

Hardware Implementation of Tunable Fractional-Order Capacitors by Morphogenesis of Conducting Polymer Dendrites

Conventional electronics is founded on a paradigm where shaping perfect electrical elements is done at the fabrication plant, so as to make devices and systems identical, "eternally immutable". In nature, morphogenic evolutions are observed in most living organisms and exploit topological plasticity as a low-resource mechanism for in operando manufacturing and computation. Often fractal, the resulting topologies feature inherent disorder: a property which is never exploited in conventional electronics manufacturing, while necessary for data generation and security in software. In this study, we present how such properties can be exploited to implement long-term and evolvable synaptic plasticity in an electronic hardware. The rich topology of conducting polymer dendrites (CPDs) is exploited to program the non-ideality of their electrochemical capacitances containing constant-phase-elements. Their evolution through structural changes alters the characteristic time constants for them to charge and discharge with the applied voltage stimuli. Under a train of voltage spikes, the evolvable current relaxation of the electrochemical systems promotes short-term plasticity with timescales ranging from milliseconds to seconds. This large window depends on the temporality of the voltage pulses used for reading, but also on the structure of a pair of CPDs on two electrodes, grown by voltage pulses. This study demonstrates how relevant physically transient and non-ideal electrochemical components can be exploited for unconventional electronics, with the aim to mimic a universal property of living organisms which could barely be replicated in a silicon monocrystal.

physics.app-ph↗

arXiv2024

Versatile CMOS Analog LIF Neuron for Memristor-Integrated Neuromorphic Circuits

Heterogeneous systems with analog CMOS circuits integrated with nanoscale memristive devices enable efficient deployment of neural networks on neuromorphic hardware. CMOS Neuron with low footprint can emulate slow temporal dynamics by operating with extremely low current levels. Nevertheless, the current read from the memristive synapses can be higher by several orders of magnitude, and performing impedance matching between neurons and synapses is mandatory. In this paper, we implement an analog leaky integrate and fire (LIF) neuron with a voltage regulator and current attenuator for interfacing CMOS neurons with memristive synapses. In addition, the neuron design proposes a dual leakage that could enable the implementation of local learning rules such as voltage-dependent synaptic plasticity. We also propose a connection scheme to implement adaptive LIF neurons based on two-neuron interaction. The proposed circuits can be used to interface with a variety of synaptic devices and process signals of diverse temporal dynamics.

cs.ET↗

arXiv2023

Expanding memory in recurrent spiking networks

Recurrent spiking neural networks (RSNNs) are notoriously difficult to train because of the vanishing gradient problem that is enhanced by the binary nature of the spikes. In this paper, we review the ability of the current state-of-the-art RSNNs to solve long-term memory tasks, and show that they have strong constraints both in performance, and for their implementation on hardware analog neuromorphic processors. We present a novel spiking neural network that circumvents these limitations. Our biologically inspired neural network uses synaptic delays, branching factor regularization and a novel surrogate derivative for the spiking function. The proposed network proves to be more successful in using the recurrent connections on memory tasks.

cs.NE↗

arXiv2023

Hardware-aware Training Techniques for Improving Robustness of Ex-Situ Neural Network Transfer onto Passive TiO2 ReRAM Crossbars

Passive resistive random access memory (ReRAM) crossbar arrays, a promising emerging technology used for analog matrix-vector multiplications, are far superior to their active (1T1R) counterparts in terms of the integration density. However, current transfers of neural network weights into the conductance state of the memory devices in the crossbar architecture are accompanied by significant losses in precision due to hardware variabilities such as sneak path currents, biasing scheme effects and conductance tuning imprecision. In this work, training approaches that adapt techniques such as dropout, the reparametrization trick and regularization to TiO2 crossbar variabilities are proposed in order to generate models that are better adapted to their hardware transfers. The viability of this approach is demonstrated by comparing the outputs and precision of the proposed hardware-aware network with those of a regular fully connected network over a few thousand weight transfers using the half moons dataset in a simulation based on experimental data. For the neural network trained using the proposed hardware-aware method, 79.5% of the test set's data points can be classified with an accuracy of 95% or higher, while only 18.5% of the test set's data points can be classified with this accuracy by the regularly trained neural network.

cs.AR↗

arXiv2023

A tunable and versatile 28nm FD-SOI crossbar output circuit for low power analog SNN inference with eNVM synapses

In this work we report a study and a co-design methodology of an analog SNN crossbar output circuit designed in a 28nm FD-SOI technology node that comprises a tunable current attenuator and a leak-integrate and fire neurons that would enable the integration of emerging non-volatile memories (eNVMs) for synaptic arrays based on various technologies including phase change (PCRAM), oxide-based (OxRAM), spin transfer and spin orbit torque magnetic memories (STT, SOT-MRAM). Circuit SPICE simulation results and eNVM experimental data are used to showcase and estimate the neurons fan-in for each type of eNVM considering the technology constraints and design trade-offs that set its limits such as membrane capacitance and supply voltage, etc.

eess.SY↗

arXiv2022

Voltage-Dependent Synaptic Plasticity (VDSP): Unsupervised probabilistic Hebbian plasticity rule based on neurons membrane potential

This study proposes voltage-dependent-synaptic plasticity (VDSP), a novel brain-inspired unsupervised local learning rule for the online implementation of Hebb's plasticity mechanism on neuromorphic hardware. The proposed VDSP learning rule updates the synaptic conductance on the spike of the postsynaptic neuron only, which reduces by a factor of two the number of updates with respect to standard spike-timing-dependent plasticity (STDP). This update is dependent on the membrane potential of the presynaptic neuron, which is readily available as part of neuron implementation and hence does not require additional memory for storage. Moreover, the update is also regularized on synaptic weight and prevents explosion or vanishing of weights on repeated stimulation. Rigorous mathematical analysis is performed to draw an equivalence between VDSP and STDP. To validate the system-level performance of VDSP, we train a single-layer spiking neural network (SNN) for the recognition of handwritten digits. We report 85.01 $ \pm $ 0.76% (Mean $ \pm $ S.D.) accuracy for a network of 100 output neurons on the MNIST dataset. The performance improves when scaling the network size (89.93 $ \pm $ 0.41% for 400 output neurons, 90.56 $ \pm $ 0.27 for 500 neurons), which validates the applicability of the proposed learning rule for spatial pattern recognition tasks. Future work will consider more complicated tasks. Interestingly, the learning rule better adapts than STDP to the frequency of input signal and does not require hand-tuning of hyperparameters

cs.NE↗

arXiv2022

Memristor-based cryogenic programmable DC sources for scalable in-situ quantum-dot control

Current quantum systems based on spin qubits are controlled by classical electronics located outside the cryostat at room temperature. This approach creates a major wiring bottleneck, which is one of the main roadblocks toward truly scalable quantum computers. Thus, we propose a scalable memristor-based programmable DC source that could be used to perform biasing of quantum dots inside of the cryostat (i.e. in-situ). This novel cryogenic approach would enable to control the applied voltage on the electrostatic gates by programming the resistance of the memristors, thus storing in the latter the appropriate conditions to form the quantum dots. In this study, we first demonstrate multilevel resistance programming of a TiO2-based memristors at 4.2 K, an essential feature to achieve voltage tunability of the memristor-based DC source. We then report hardwarebased simulations of the electrical performance of the proposed DC source. A cryogenic TiO2-based memristor model fitted on our experimental data at 4.2 K was used to show a 1 V voltage range and 100 uV in-situ memristor-based DC source. Finally, we simulate the biasing of double quantum dots enabling sub-2 minutes in-situ charge stability diagrams. This demonstration is a first step towards more advanced cryogenic applications for resistive memories such as cryogenic control electronics for quantum computers.

cs.AR↗

arXiv2021

Fully CMOS-compatible passive TiO2-based memristor crossbars for in-memory computing

Brain-inspired computing and neuromorphic hardware are promising approaches that offer great potential to overcome limitations faced by current computing paradigms based on traditional von-Neumann architecture. In this regard, interest in developing memristor crossbar arrays has increased due to their ability to natively perform in-memory computing and fundamental synaptic operations required for neural network implementation. For optimal efficiency, crossbar-based circuits need to be compatible with fabrication processes and materials of industrial CMOS technologies. Herein, we report a complete CMOS-compatible fabrication process of TiO2-based passive memristor crossbars with 700 nm wide electrodes. We show successful bottom electrode fabrication by a damascene process, resulting in an optimised topography and a surface roughness as low as 1.1 nm. DC sweeps and voltage pulse programming yield statistical results related to synaptic-like multilevel switching. Both cycle-to-cycle and device-to-device variability are investigated. Analogue programming of the conductance using sequences of 200 ns voltage pulses suggest that the fabricated memories have a multilevel capacity of at least 3 bits due to the cycle-to-cycle reproducibility.

cs.ET↗

arXiv2021

Multi-terminal memristive devices enabling tunable synaptic plasticity in neuromorphic hardware: a mini-review

Neuromorphic computing based on spiking neural networks has the potential to significantly improve on-line learning capabilities and energy efficiency of artificial intelligence, specially for edge computing. Recent progress in computational neuroscience have demonstrated the importance of heterosynaptic plasticity for network activity regulation and memorization. Implementing heterosynaptic plasticity in hardware is thus highly desirable, but important materials and engineering challenges remain, calling for breakthroughs in neuromorphic devices. In this mini-review, we propose an overview of the latest advances in multi-terminal memristive devices on silicon with tunable synaptic plasticity, enabling heterosynaptic plasticity in hardware. The scalability and compatibility of the devices with industrial complementary metal oxide semiconductor (CMOS) technologies are discussed.

physics.app-ph↗

arXiv2021

Bio-inspired adaptive sensing through electropolymerization of organic electrochemical transistors

Organic Electrochemical Transistors are considered today as a key technology to interact with biological medium through their intrinsic ionic-electronic coupling. In this paper, we show how this coupling can be finely tuned (in operando) post-microfabrication via electropolymerization technique. This strategy exploits the concept of adaptive sensing where both transconductance and impedance are tunable and can be modified on-demand to match different sensing requirements. Material investigation through Raman spectroscopy, atomic force microscopy and scanning electron microscopy reveals that electropolymerization can lead to a fine control of PEDOT microdomains organization, which directly affect the iono-electronic properties of OECTs. We further highlight how volumetric capacitance and effective mobility of PEDOT:PSS influence distinctively the transconductance and impedance of OECTs. This approach shows to improve the transconductance by 150% while reducing their variability by 60% in comparison with standard spin-coated OECTs. Finally, we show how to the technique can influence voltage spike rate hardware classificationwith direct interest in bio-signals sorting applications.

eess.SP↗

arXiv2021

Signals to Spikes for Neuromorphic Regulated Reservoir Computing and EMG Hand Gesture Recognition

Surface electromyogram (sEMG) signals result from muscle movement and hence they are an ideal candidate for benchmarking event-driven sensing and computing. We propose a simple yet novel approach for optimizing the spike encoding algorithm's hyper-parameters inspired by the readout layer concept in reservoir computing. Using a simple machine learning algorithm after spike encoding, we report performance higher than the state-of-the-art spiking neural networks on two open-source datasets for hand gesture recognition. The spike encoded data is processed through a spiking reservoir with a biologically inspired topology and neuron model. When trained with the unsupervised activity regulation CRITICAL algorithm to operate at the edge of chaos, the reservoir yields better performance than state-of-the-art convolutional neural networks. The reservoir performance with regulated activity was found to be 89.72% for the Roshambo EMG dataset and 70.6% for the EMG subset of sensor fusion dataset. Therefore, the biologically-inspired computing paradigm, which is known for being power efficient, also proves to have a great potential when compared with conventional AI algorithms.

eess.SP↗

arXiv2021

Dendritic organic electrochemical transistors grown by electropolymerization for 3D neuromorphic engineering

One of the major limitation of standard top-down technologies used in today's neuromorphic engineering is their inability to map the 3D nature of biological brains. Here, we show how bipolar electropolymerization can be used to engineer 3D networks of PEDOT:PSS dendritic fibers. By controlling the growth conditions of the electropolymerized material, we investigate how dendritic fibers can reproduce structural plasticity by creating structures of controllable shape. We demonstrate gradual topologies evolution in a multi-electrode configuration. We conduct a detail electrical characterization of the PEDOT:PSS dendrites through DC and impedance spectroscopy measurements and we show how organic electrochemical transistors (OECT) can be realized with these structures. These measurements reveal that quasi-static and transient response of OECTs can be adjust by controlling dendrites' morphologies. The unique properties of organic dendrites are used to demonstrate short-term, long-term and structural plasticity, which are essential features required for future neuromorphic hardware development.

cond-mat.dis-nn↗

arXiv2021

Theoretical modeling of dendrite growth from conductive wire electropolymerization

Electropolymerization is a bottom-up materials engineering process of micro and nano-scale that utilizes electrical signals to deposit conducting dendrites' morphologies by a redox reaction in the liquid phase. It resembles synaptogenesis in the brain, in which electrical stimulation in the brain causes the formation of synapses from the cellular neural composites. The strategy has been recently explored for neuromorphic engineering by establishing link between the electrical signals and the dendrites' shapes. Since the geometry of these structures determines their electrochemical properties, understanding the mechanisms that regulate the polymer assembly under electrically programmed conditions is an important aspect. In this manuscript, we simulate this phenomenon using mesoscale simulations, taking into account the important features of spatial-temporal potential mapping based on the time-varying signal, the motion of charged particles in the liquid due to the electric field, and the attachment of particles on the electrode. The study helps in visualizing the motion of particles in different electrical conditions, which is not possible to probe experimentally. Consistent with the experiments, the higher AC frequency of electrical activities favors linear wire-like growth, while lower frequency leads to more dense and fractal dendrites growth, and voltage offset leads to asymmetrical growth. We find that dendrites' shape and growth process systematically depend on particle concentration and random scattering. We discover that the different dendrites' architectures are associated with different Laplace and diffusion fields, which govern the monomers trajectory and subsequent dendrites' growth. Such unconventional engineering routes could have a variety of applications from neuromorphic engineering to bottom-up computing strategies.

cond-mat.dis-nn↗