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.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.