Organic Neuromorphic Circuits for Real‐Time Biosignal Applications

Abstract Organic neuromorphic circuits offer new opportunities for low‐power, flexible electronics capable of real‐time inference at the edge. In this work, we present a neuromorphic system based on organic thin‐film transistors (OTFTs) that performs biosignal classification in a Bayesian framework. The spiking behavior of artificial OTFT neuron circuits are first characterized, showing how their dynamics can be tuned through circuit parameters. We then demonstrate how the circuits can infer information from real‐world electroencephalography (EEG) data. When applied to epilepsy‐related signal patterns, the system achieves excellent classification performance, while maintaining ultra‐low power operation. These results highlight the potential of OTFT‐based neuromorphic architectures for embedded medical diagnostics.

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Publication Details

Journal
Advanced Electronic Materials
Published
2025-11-29
DOI
https://doi.org/10.1002/aelm.202500519
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
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article

Organic Neuromorphic Circuits for Real‐Time Biosignal Applications

Yvan Bonnassieux, Benjamı́n Iñı́guez, Yerin Kim, M. Benwadih et al.
Advanced Electronic Materials
Advanced Memory and Neural Computing
article

Organic Neuromorphic Circuits for Real‐Time Biosignal Applications

Yvan Bonnassieux, Benjamı́n Iñı́guez, Yerin Kim, M. Benwadih, Laurie E. Calvet, Lina Kadura, Zonglong Li, A Hemmati, Patryk Golec, G.Y. Kim, Sami El‐Nakouzi, Amer Zaibi
article en

Abstract

Abstract Organic neuromorphic circuits offer new opportunities for low‐power, flexible electronics capable of real‐time inference at the edge. In this work, we present a neuromorphic system based on organic thin‐film transistors (OTFTs) that performs biosignal classification in a Bayesian framework. The spiking behavior of artificial OTFT neuron circuits are first characterized, showing how their dynamics can be tuned through circuit parameters. We then demonstrate how the circuits can infer information from real‐world electroencephalography (EEG) data. When applied to epilepsy‐related signal patterns, the system achieves excellent classification performance, while maintaining ultra‐low power operation. These results highlight the potential of OTFT‐based neuromorphic architectures for embedded medical diagnostics.

Advanced Electronic MaterialsVol. 12(1)
Centre National de la Recherche Scientifique (FR), École Polytechnique (FR), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR), Laboratoire d’Innovation pour les Technologies des Énergies Nouvelles et les nanomatériaux (FR), Institut des Sciences du Vivant Frédéric Joliot (FR), CentraleSupélec (FR), Institut Polytechnique de Paris (FR), Universitat Rovira i Virgili (ES), Université Grenoble Alpes (FR)
Openalex Percentile: Top 63%
Advanced Memory and Neural Computing
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