Al Nanoparticle‐Decorated Metal Oxide Synaptic Transistors for Ultralow‐Energy Neuromorphic Computing with Wide Dynamic Range

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. 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. Advanced Science, EarlyView.