Fabien Alibart
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View article: Chemical control for the morphogenesis of conducting polymer dendrites in water
Chemical control for the morphogenesis of conducting polymer dendrites in water Open
The relationship between the morphology of electrogenerated conducting polymer dendrites with the chemical composition of their environment and the adaptability of evolving polymers to water-based conditions for bio-inspired electronics ar…
View article: Unsupervised sparse coding-based spiking neural network for real-time spike sorting
Unsupervised sparse coding-based spiking neural network for real-time spike sorting Open
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 is achieving real-time, low-power spike sorting…
View article: Hardware implementation of tunable fractional-order capacitors by morphogenesis of conducting polymer dendrites
Hardware implementation of tunable fractional-order capacitors by morphogenesis of conducting polymer dendrites Open
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 observe…
View article: Brain‐Inspired Polymer Dendrite Networks for Morphology‐Dependent Computing Hardware
Brain‐Inspired Polymer Dendrite Networks for Morphology‐Dependent Computing Hardware Open
Process variation is always a challenge to mitigate in electronics. This especially holds true for organic semiconductors, where reproducibility concerns hinder industrialization. Challenging this concept, it shows AC‐electropolymerization…
View article: Chemical Control for the Morphogenesis of Conducting Polymer Dendrites in Water
Chemical Control for the Morphogenesis of Conducting Polymer Dendrites in Water Open
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…
View article: All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfO<sub>x</sub> ReRAM Devices
All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfO<sub>x</sub> ReRAM Devices Open
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 technol…
View article: Enhancing temporal learning in recurrent spiking networks for neuromorphic applications
Enhancing temporal learning in recurrent spiking networks for neuromorphic applications Open
Training Recurrent Spiking Neural Networks (RSNNs) with binary spikes for tasks of extended time scales presents a challenge due to the amplified vanishing gradient problem during back propagation through time. This paper introduces three …
View article: Hardware Implementation of Tunable Fractional-Order Capacitors by Morphogenesis of Conducting Polymer Dendrites
Hardware Implementation of Tunable Fractional-Order Capacitors by Morphogenesis of Conducting Polymer Dendrites Open
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 observe…
View article: All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices
All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices Open
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 technol…
View article: Unsupervised local learning based on voltage-dependent synaptic plasticity for resistive and ferroelectric synapses
Unsupervised local learning based on voltage-dependent synaptic plasticity for resistive and ferroelectric synapses Open
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…
View article: Phase Change Memory Drift Compensation in Spiking Neural Networks Using a Non-Linear Current Scaling Strategy
Phase Change Memory Drift Compensation in Spiking Neural Networks Using a Non-Linear Current Scaling Strategy Open
The non-ideality aspects of phase change memory (PCM) such as drift and resistance variability can pose significant obstacles in neuromorphic hardware implementations. A unique drift and variability compensation strategy is demonstrated an…
View article: 28 nm FDSOI embedded PCM exhibiting near zero drift at 12 K for cryogenic SNNs
28 nm FDSOI embedded PCM exhibiting near zero drift at 12 K for cryogenic SNNs Open
International audience
View article: Versatile CMOS Analog LIF Neuron for Memristor-Integrated Neuromorphic Circuits
Versatile CMOS Analog LIF Neuron for Memristor-Integrated Neuromorphic Circuits Open
International audience
View article: PCM Drift-Compensation in SNNs using a Non-Linear Current Scaling Strategy
PCM Drift-Compensation in SNNs using a Non-Linear Current Scaling Strategy Open
View article: CMOS-compatible Hf<sub>0.5</sub>Zr<sub>0.5</sub>O<sub>2</sub>-based ferroelectric memory crosspoints fabricated with damascene process
CMOS-compatible Hf<sub>0.5</sub>Zr<sub>0.5</sub>O<sub>2</sub>-based ferroelectric memory crosspoints fabricated with damascene process Open
We report the fabrication of Hf 0.5 Zr 0.5 O 2 (HZO) based ferroelectric memory crosspoints using a complementary metal-oxide-semiconductor-compatible damascene process. In this work, we compared 12 and 56 µ m 2 crosspoint devices with the…
View article: Versatile CMOS Analog LIF Neuron for Memristor-Integrated Neuromorphic Circuits
Versatile CMOS Analog LIF Neuron for Memristor-Integrated Neuromorphic Circuits Open
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 oper…
View article: 28 nm FD-SOI embedded phase change memory exhibiting near-zero drift at 12 K for cryogenic spiking neural networks (SNNs)
28 nm FD-SOI embedded phase change memory exhibiting near-zero drift at 12 K for cryogenic spiking neural networks (SNNs) Open
Seeking to circumvent the bottleneck of conventional computing systems, alternative methods of hardware implementation, whether based on brain-inspired architectures or cryogenic quantum computing systems, invariably suggest the integratio…
View article: Damascene versus subtractive line CMP process for resistive memory crossbars BEOL integration
Damascene versus subtractive line CMP process for resistive memory crossbars BEOL integration Open
In recent years, resistive memories have emerged as a pivotal advancement in the realm of electronics, offering numerous advantages in terms of energy efficiency, scalability, and non-volatility [1]. Characterized by their unique resistive…
View article: Neuromorphic Signal Classification Using Organic Electrochemical Transistor Array and Spiking Neural Simulations
Neuromorphic Signal Classification Using Organic Electrochemical Transistor Array and Spiking Neural Simulations Open
International audience
View article: Structural plasticity for neuromorphic networks with electropolymerized dendritic PEDOT connections
Structural plasticity for neuromorphic networks with electropolymerized dendritic PEDOT connections Open
View article: Editorial: Focus on organic materials, bio-interfacing and processing in neuromorphic computing and artificial sensory applications
Editorial: Focus on organic materials, bio-interfacing and processing in neuromorphic computing and artificial sensory applications Open
International audience
View article: Expanding memory in recurrent spiking networks
Expanding memory in recurrent spiking networks Open
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-ar…
View article: A tunable and versatile 28 nm FD-SOI crossbar output circuit for low power analog SNN inference with eNVM synapses
A tunable and versatile 28 nm FD-SOI crossbar output circuit for low power analog SNN inference with eNVM synapses Open
View article: Hardware-aware Training Techniques for Improving Robustness of Ex-Situ Neural Network Transfer onto Passive TiO2 ReRAM Crossbars
Hardware-aware Training Techniques for Improving Robustness of Ex-Situ Neural Network Transfer onto Passive TiO2 ReRAM Crossbars Open
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. Howeve…
View article: A tunable and versatile 28nm FD-SOI crossbar output circuit for low power analog SNN inference with eNVM synapses
A tunable and versatile 28nm FD-SOI crossbar output circuit for low power analog SNN inference with eNVM synapses Open
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 enab…
View article: Développement de dendrites polymères organiques en 3D comme dispositif neuromorphique
Développement de dendrites polymères organiques en 3D comme dispositif neuromorphique Open
Les technologies neuromorphiques constituent une voie prometteuse pour le développement d'une informatique plus avancée et plus économe en énergie. Elles visent à reproduire les caractéristiques attrayantes du cerveau, telles qu'une grande…
View article: Memristor-Based Cryogenic Programmable DC Sources for Scalable In Situ Quantum-Dot Control
Memristor-Based Cryogenic Programmable DC Sources for Scalable In Situ Quantum-Dot Control Open
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 scalab…
View article: Precision of neuronal localization in 2D cell cultures by using high-performance electropolymerized microelectrode arrays correlated with optical imaging
Precision of neuronal localization in 2D cell cultures by using high-performance electropolymerized microelectrode arrays correlated with optical imaging Open
Recently, the development of electronic devices to extracellularly record the simultaneous electrical activities of numerous neurons has been blooming, opening new possibilities to interface and decode neuronal activity. In this work, we t…
View article: A Tunable and Versatile 28nm Fd-Soi Crossbar Output Circuit for Low Power Analog Snn Inference with Envm Synapses
A Tunable and Versatile 28nm Fd-Soi Crossbar Output Circuit for Low Power Analog Snn Inference with Envm Synapses Open
View article: Unsupervised and efficient learning in sparsely activated convolutional spiking neural networks enabled by voltage-dependent synaptic plasticity
Unsupervised and efficient learning in sparsely activated convolutional spiking neural networks enabled by voltage-dependent synaptic plasticity Open
Spiking neural networks (SNNs) are gaining attention due to their energy-efficient computing ability, making them relevant for implementation on low-power neuromorphic hardware. Their biological plausibility has permitted them to benefit f…