Artificial synapses based on 2D MoS2 memtransistors have recently attracted considerable attention as a promising device architecture for complex neuromorphic systems. However, previous memtransistor devices occasionally cause uncontrollable analog switching and unreliable synaptic plasticity due to random variations in the field‐induced defect migration. Herein, a highly reliable 2D MoS2/Nb2O5 heterostructure memtransistor device is demonstrated, in which the Nb2O5 interlayer thickness is a critical material parameter to induce and tune analog switching characteristics of the 2D MoS2. Ultraviolet photoelectron spectroscopy and photoluminescence analyses reveal that the Schottky barrier height at the 2D channel–electrode junction of the MoS2/Nb2O5 heterostructure films is increased, leading to more effective contact barrier modulation and allowing more reliable resistive switching. The 2D/oxide memtransistors attain dual‐terminal (drain and gate) stimulated heterosynaptic plasticity and highly precise multi‐states. In addition, the memtransistor devices show an extremely low power consumption of ≈6 pJ and reliable potentiation/depression endurance characteristics over 2000 pulses. A high pattern recognition accuracy of ≈94.2% is finally achieved from the synaptic plasticity modulated by the drain pulse configuration using an image pattern recognition simulation. Thus, the novel 2D/oxide memtransistor makes a potential neuromorphic circuitry more flexible and energy‐efficient, promoting the development of more advanced neuromorphic systems.

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The reliable conductance modulation of synaptic devices is key when implementing high-performance neuromorphic systems. Herein, we propose a floating gate IGZO synaptic device with an aluminum trapping layer to investigate the correlation between its diverse electrical parameters and pattern recognition accuracy. Basic synaptic properties such as excitatory postsynaptic current, paired pulse facilitation, long/short term memory, and long-term potentiation/depression are demonstrated in the IGZO synaptic transistor. The effects of pulse tuning conditions associated with the pulse voltage magnitude, interval, duration, and cycling number of the applied pulses on the conductance update are systematically investigated. It is discovered that both the nonlinearity of the conductance update and cycle-to-cycle variation should be critically considered using an artificial neural network simulator to ensure the high pattern recognition accuracy of Modified National Institute of Standards and Technology (MNIST) handwritten digit images. The highest recognition rate of the MNIST handwritten dataset is 94.06% for the most optimized pulse condition. Finally, a systematic study regarding the synaptic parameters must be performed to optimize the developed synapse device.

Recently, memtransistors have attracted considerable attention as promising building blocks for highly efficient neuromorphic synaptic devices, facilitating heterosynaptic plasticity and a large number of multilevel states. However, the resistive switching characteristics of memtransistors have been limited to a two-dimensional transition-metal dichalcogenide. Here, reliable gate-tunable resistive switching characteristics of an InGaZnO (IGZO) memtransistor with an Al2O3/TiO2 double-oxide structure are first demonstrated. The field-induced oxygen vacancy migration model in the TiO2 layer is proposed to describe the dynamic Schottky barrier modulation between the Al electrode and IGZO channel, causing the resistive switching property. Reliable heterosynaptic plasticity and a highly precise multilevel state can be achieved using dual-terminal presynaptic stimuli. The IGZO memtransistor also showed remarkable endurance in long-term potentiation and depression cycling under 5000 drain and gate pulses. The high pattern recognition accuracies of 90.4 and 86.5% obtained from the drain- and gate-stimulated conductance changes were validated using an artificial neural network (ANN) simulation. Thus, our double-oxide structure designed to realize memtransistor characteristics paves the way for high-performance synaptic circuitry with precisely controlled heterosynaptic plasticity, leading to an advanced neuromorphic system.