ScholarWorks@동국대학교

초록

Wearable and flexible neuromorphic devices capable of accurately emulating synaptic behaviors while autonomously responding to mechanical stimuli hold great promise for intelligent sensing and bio-inspired computing. Here, we present a fully flexible synaptic device in which a graphene-channel ion-gel-gated transistor (g-IGT) fabricated on a plastic substrate is directly driven by a poly(vinylidene fluoride-co-trifluoroethylene)-based triboelectric nanogenerator (TENG), enabling self-powered tactile sensing and analog weight storage. The device emulates the multistore memory hierarchy through sensory–short-term–long-term memory transitions, characterized by synaptic decay times of ∼70 ms (sensory), 0.2–0.45 s (short-term), and >2.0 s (long-term). Furthermore, rate-coded learning is demonstrated through spike-rate-dependent plasticity, enabling frequency-selective potentiation and depression. When the experimentally measured weight-update profiles are implemented in a single-layer perceptron for human activity recognition, the neural network system achieves >88% classification accuracy, even under 1.1 MPa bending stress. Furthermore, the network system maintains relatively stable performance (>75%) even under the extreme environment with a high noise level. These results establish the TENG-driven g-IGT as a viable route toward mechanically compliant, battery-free neuromorphic platforms capable of sensing, learning, and adapting in situ. © 2026 The Author(s). Advanced Materials published by Wiley-VCH GmbH.

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