Al Nanoparticle-Decorated Metal Oxide Synaptic Transistors for Ultralow-Energy Neuromorphic Computing with Wide Dynamic Range #MMPMID41319294
Choi JG; Song Y; Baek S; Jang J; Park S
Adv Sci (Weinh) 2025[Nov]; ? (?): e21321 PMID41319294show ga
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 x 60 x 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.
Adv+Sci+(Weinh) 2025 ; ? (?): e21321
