The highly parallel artificial neural systems based on transistor-like devices have recently attracted widespread attention due to their high-efficiency computing potential and the ability to mimic biological neurobehavior. For the past decades, plenty of breakthroughs related to synaptic transistors have been investigated and reported. In this work, a kind of photoelectronic transistor that successfully mimics the behaviors of biological synapses has been proposed and systematically analyzed. For the individual device, MXenes and the self-assembled titanium dioxide on the nanosheet surface serve as floating gate and tunneling layers, respectively. As the unit electronics of the neural network, the typical synaptic behaviors and the reliable memory stability of the synaptic transistors have been demonstrated through the voltage test. Furthermore, for the first time, the UV-responsive synaptic properties of the MXenes floating gated transistor and its applications, including conditional reflex and supervised learning, have been measured and realized. These photoelectric synapse characteristics illustrate the great potential of the device in bio-imitation vision applications. Finally, through the simulation based on an artificial neural network algorithm, the device successfully realizes the recognition application of handwritten digital images. Thus, this article provides a highly feasible solution for applying artificial synaptic devices to hardware neuromorphic networks.

No abstract

As one of the promising next-generation electronics, brain-inspired synaptic resistive random access memory (RRAM) devices with stacked solution-processed (SP) spin-coated resistive switching (RS) layers were fabricated in this work. Compared with the RRAM device with a single SP-RS layer (Ag/SP-AlO x /ITO), the device with stacked SP-RS layers (Ag/SP-GaO x /SP-AlO x /ITO) is induced by the metal conductive filament performed with lower power consumption (∼±0.6 V operation voltage), larger read and write capability (∼2 × 10 4 ON/OFF ratio), and enhanced stability (>2 × 10 4 s retention time and >1000 endurance cycles). Multiple conductance states with long-term potentiation and depression (200 pulses) were obtained on Ag/SP-GaO x /SP-AlO x /ITO RRAM devices, which resulted in the human brain-like behavior (learning−forgetting− relearning) of a matrix comprising of RRAM devices with SP-GaO x /SP-AlO x layers. Based on the synaptic performance of Ag/SP-GaO x /SP-AlO x /ITO RRAM devices, an image recognition process based on a neuron network was conducted and the average recognition accuracy was close to 90%.

The ecofriendly combustion synthesis (ECS) and self-combustion synthesis (ESCS) have been successfully utilized to deposit high-k aluminum oxide (AlO x ) dielectrics at low temperatures and applied for aqueous In2O3 thin-film transistors (TFTs) accordingly. The ECS and ESCS processes facilitate the formation of high-quality dielectrics at lower temperatures compared to conventional methods based on an ethanol precursor, as confirmed by thermal analysis and chemical composition characterization. The aqueous In2O3 TFTs based on ECS and ESCS-AlO x show enhanced electrical characteristics and counterclockwise transfer-curve hysteresis. The memory-like counterclockwise behavior in the transfer curve modulated by the gate bias voltage is comparable to the signal modulation by the neurotransmitters. ECS and ESCS transistors are employed to perform synaptic emulation; various short-term and long-term memory functions are emulated with low operating voltages and high excitatory postsynaptic current levels. High stability and reproducibility are achieved within 240 pulses of long-term synaptic potentiation and depression. The synaptic emulation functions achieved in this work match the demand for artificial neural networks (ANN), and a multilayer perceptron (MLP) is developed using an ECS-AlO x synaptic transistor for image recognition. A superior recognition rate of over 90% is achieved based on ECS-AlO x synaptic transistors, which facilitates the implementation of the metal-oxide synaptic transistor for future neuromorphic computing via an ecofriendly route.