Unsupervised local learning based on voltage-dependent synaptic plasticity for resistive and ferroelectric synapses
Year: 2026
Type: article
Abstract: 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 (TiO2, HfO2-based metal-oxide filamentary synapses, and HfZrO4-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 HRS/LRS levels, and proposed mitigation strategies to enhance robustness.
Source: Communications Materials
Institutions: École Centrale de Lyon, Centre National de la Recherche Scientifique, Université de Sherbrooke, Université de Lille, Institut d'Electronique, de Microélectronique et de Nanotechnologie, Laboratoire Nanotechnologies et Nanosystèmes, Institut interdisciplinaire d'innovation technologique, Institut National des Sciences Appliquées de Lyon, Université Grenoble Alpes, Institut polytechnique de Grenoble, Commissariat à l'Énergie Atomique et aux Énergies Alternatives, CEA Grenoble, STMicroelectronics (France), Spintronique et Technologie des Composants, IBM Research - Zurich, Université de Toulon, Aix-Marseille Université, Institut des Matériaux, de Microélectronique et des Nanosciences de Provence, Université Paris-Saclay, Centre de Nanosciences et de Nanotechnologies
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,690
OpenAlex ID: W7122686943