Non-Volatile Electrolyte-Gated Transistors Based on Graphdiyne/MoS2 with Robust Stability for Low-Power Neuromorphic Computing and Logic-In-Memory

Artificial synapses are the key building blocks for low-power neuromorphic computing that can go beyond the constraints of von Neumann architecture. In comparison with two-terminal memristors and three-terminal transistors with filament-formation and charge-trapping mechanisms, emerging electrolyte-gated transistors (EGTs) have been demonstrated as a promising candidate for neuromorphic applications due to their prominent analog switching performance. Here, a novel graphdiyne (GDY)/MoS2-based EGT is proposed, where an ion-storage layer (GDY) is adopted to EGTs for the first time. Benefitting from this Li-ion-storage layer, the GDY/MoS2-based EGT features a robust stability (variation < 1% for over 2000 cycles), an ultralow energy consumption (50 aJ mu m(-2)), and long retention characteristics (>10(4) s). In addition, a quasi-linear conductance update with low noise (1.3%), an ultrahigh G(max)/G(min) ratio (10(3)), and an ultralow readout conductance (<10 nS) have been demonstrated by this device, enabling the implementation of the neuromorphic computing with near-ideal accuracies. Moreover, the non-volatile characteristics of the GDY/MoS2-based EGT enable it to demonstrate logic-in-memory functions, which can execute logic processing and store logic results in a single device. These results highlight the potential of the GDY/MoS2-based EGT for next-generation low-power electronics beyond von Neumann architecture.

Automated summary

Emerging electrolyte-gated transistors (EGTs) with ion-storage layer, such as graphdiyne (GDY)/MoS2-based EGT, demonstrate robust stability, low energy consumption, and non-volatile characteristics, making them potential candidates for low-power neuromorphic computing beyond von Neumann architecture. These devices show promising analog switching performance, quasi-linear conductance update, and logic-in-memory functions, highlighting their potential for next-generation electronics with near-ideal accuracies.