A nanoparticle-engineered electrolyte-gated memtransistor is introduced as a materials-level strategy to overcome the intrinsic trade-off between energy consumption and synaptic precision. By embedding aluminum nanoparticles at the oxide–electrolyte interface to modulate ion trapping dynamics, the device achieves stable multistate plasticity under millivolt operation, enabling a practical route toward scalable ultralow-energy neuromorphic hardware.
Abstract
Achieving ultralow energy consumption alongside high synaptic fidelity remains a key challenge in the development of practical and scalable neuromorphic hardware systems. Electrolyte-gated memtransistors (EGMTs), which enable low-voltage analog switching via electric double layer modulation, suffer from a fundamental trade-off between dynamic range and energy consumption. Here, a nanoparticle-engineered EGMT is reported that mitigates this limitation by incorporating aluminum nanoparticles at the interface between a solution-processed indium gallium zinc oxide channel and a solid polymer electrolyte composed of polyethylene oxide doped with lithium hexafluoroarsenate. This design yields 50 discrete conductance states at a drain voltage of 1 mV, achieving a dynamic range exceeding 78 and a synaptic switching energy of 0.62 pJ spike−1, which ranks among the lowest reported for EGMTs. Neural network simulations (784 × 60 × 10), based on experimentally extracted conductance updates, predict energy savings of 99.7% during training and 91.4% during inference compared to digital complementary metal–oxide–semiconductor implementations.
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