Bi-Modal Synapse Based on a Short-Channel Ferroelectric van der Waals Heterostructure

The growing demand for energy-efficient and -adaptive computing drives research into neuromorphic architectures. Van der Waals (vdW) ferroelectric field-effect transistors offer nonvolatile polarization control and a highly tunable semiconductor channel, enabling multilevel states and making them promising for brain-inspired electronics. Here, we present a reconfigurable bimodal ferroelectric synapse based on the CuInP<sub>2</sub>S<sub>6</sub>/hBN/WSe<sub>2</sub> vdW heterostructure, extending beyond conventional single-modal synaptic devices by introducing added functionality. Transport measurements and piezoresponse force microscopy reveal precise electrical control over the ferroelectric domain landscape, enabling continuous tuning of WSe<sub>2</sub> channel conductance and its threshold voltage. Crucially, the ambipolar nature of WSe<sub>2</sub> allows for real-time switching between excitatory and inhibitory synaptic behaviors, mimicking multimodal neurotransmission observed in the human brain. Moreover, the bimodal synapse is demonstrated at channel lengths down to 50 nm, venturing into previously uncharted territory for ferroelectric vdW synapses. Neural network simulations incorporating our device show excellent learning performance for both synaptic modes, highlighting its potential for next-generation neuromorphic computing. This work expands the functional and scaling capabilities of vdW ferroelectric technology, highlighting its potential for next-generation artificial intelligence electronics.

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Publication Details

Journal
ACS Applied Materials & Interfaces
Published
2025-10-24
DOI
https://doi.org/10.1021/acsami.5c16593
Primary Topic
Advanced Memory and Neural Computing
Type
article
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article

Bi-Modal Synapse Based on a Short-Channel Ferroelectric van der Waals Heterostructure

Abdelkarim Ouerghi, Jean‐François Dayen, Alexei L. Gruverman, S. Fusil et al.
ACS Applied Materials & Interfaces
Advanced Memory and Neural Computing
article

Bi-Modal Synapse Based on a Short-Channel Ferroelectric van der Waals Heterostructure

Abdelkarim Ouerghi, Jean‐François Dayen, Alexei L. Gruverman, S. Fusil, Gautam Samanta, Takashi Taniguchi, Kenji Watanabe, Neeraj Kumar Rajak, Bernard Doudin, Mohamed A. Soliman, S. Masood, Ankita Ram
article en

Abstract

The growing demand for energy-efficient and -adaptive computing drives research into neuromorphic architectures. Van der Waals (vdW) ferroelectric field-effect transistors offer nonvolatile polarization control and a highly tunable semiconductor channel, enabling multilevel states and making them promising for brain-inspired electronics. Here, we present a reconfigurable bimodal ferroelectric synapse based on the CuInP<sub>2</sub>S<sub>6</sub>/hBN/WSe<sub>2</sub> vdW heterostructure, extending beyond conventional single-modal synaptic devices by introducing added functionality. Transport measurements and piezoresponse force microscopy reveal precise electrical control over the ferroelectric domain landscape, enabling continuous tuning of WSe<sub>2</sub> channel conductance and its threshold voltage. Crucially, the ambipolar nature of WSe<sub>2</sub> allows for real-time switching between excitatory and inhibitory synaptic behaviors, mimicking multimodal neurotransmission observed in the human brain. Moreover, the bimodal synapse is demonstrated at channel lengths down to 50 nm, venturing into previously uncharted territory for ferroelectric vdW synapses. Neural network simulations incorporating our device show excellent learning performance for both synaptic modes, highlighting its potential for next-generation neuromorphic computing. This work expands the functional and scaling capabilities of vdW ferroelectric technology, highlighting its potential for next-generation artificial intelligence electronics.

ACS Applied Materials & InterfacesVol. 17(44)
University of Nebraska–Lincoln (US), Centre National de la Recherche Scientifique (FR), National Institute for Materials Science (JP), Université Paris-Saclay (FR), Centre de Nanosciences et de Nanotechnologies (FR), Université de Strasbourg (FR)
Openalex Percentile: Top 70%
Advanced Memory and Neural Computing
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