ABSTRACT
The continuous advancement of Artificial Intelligence (AI) technology depends on the efficient processing of unstructured data, encompassing text, speech, and video. Traditional serial computing systems based on the von Neumann architecture, employed in information and communication technology development for decades, are not suitable for the concurrent processing of massive unstructured data tasks with relatively low-level operations. As a result, there arises a pressing need to develop novel parallel computing systems. Recently, there has been a burgeoning interest among developers in emulating the intricate operations of the human brain, which efficiently processes vast datasets with remarkable energy efficiency. This has led to the proposal of neuromorphic computing systems. Of these, Spiking Neural Networks (SNNs), designed to closely resemble the information processing mechanisms of biological neural networks, are subjects of intense research activity. Nevertheless, a comprehensive investigation into the relationship between spike shapes and Spike-Timing-Dependent Plasticity (STDP) to ensure efficient synaptic behavior remains insufficiently explored. In this study, we systematically explore various input spike types to optimize the resistive memory characteristics of Hafnium-based Ferroelectric Tunnel Junction (FTJ) devices. Among the various spike shapes investigated, the square-triangle (RT) spike exhibited good linearity and symmetry, and a wide range of weight values could be realized depending on the offset of the RT spike. These results indicate that the spike shape serves as a crucial indicator in the alteration of synaptic connections, representing the strength of the signals.
ABSTRACT
Recently, considerable attention has been paid to the development of advanced technologies such as artificial intelligence (AI) and big data, and high-density, high-speed storage devices are being extensively studied to realize the technology. Ferroelectrics are promising non-volatile memory materials because of their ability to maintain polarization, even when an external electric field is removed. Recently, it has been reported that HfO2 thin films compatible with complementary metal-oxide-semiconductor (CMOS) processes exhibit ferroelectricity even at a thickness of less than 10 nm. Among the ferroelectric-based memories, ferroelectric tunnel junctions are attracting attention as ideal devices for improving integration and miniaturization due to the advantages of a simple metal-ferroelectric-metal two-terminal structure and low ultra-low power driving through tunneling. The FTJs are driven by adjusting the tunneling electrical resistance through partial polarization switching. Theoretically and experimentally, a large memory window in a broad coercive field and/or read voltage is required to induce sophisticated partial-polarization switching. Notably, antiferroelectrics (like) have different switching properties than ferroelectrics, which are generally applied to ferroelectric tunnel junctions. The memory features of ferroelectric tunnel junctions are expected to be improved through a broad coercive field when the switching characteristics of the ferroelectric and antiferroelectric (like) are utilized concurrently. In this study, the implementation of multiresistance states was improved by driving the ferroelectric and antiferroelectric (like) devices in parallel. Additionally, by modulating the area ratio of ferroelectric and antiferroelectric (like), the memory window size was increased, and controllability was enhanced by increasing the switchable voltage region. In conclusion, we suggest that ferroelectric and antiferroelectric (like) parallel structures may overcome the limitations of the multiresistance state implementation of existing ferroelectrics.
ABSTRACT
Owing to the 4th Industrial Revolution, the amount of unstructured data, such as voice and video data, is rapidly increasing. Brain-inspired neuromorphic computing is a new computing method that can efficiently and parallelly process rapidly increasing data. Among artificial neural networks that mimic the structure of the brain, the spiking neural network (SNN) is a network that imitates the information-processing method of biological neural networks. Recently, memristors have attracted attention as synaptic devices for neuromorphic computing systems. Among them, the ferroelectric doped-HfO2-based ferroelectric tunnel junction (FTJ) is considered as a strong candidate for synaptic devices due to its advantages, such as complementary metal-oxide-semiconductor device/process compatibility, a simple two-terminal structure, and low power consumption. However, research on the spiking operations of FTJ devices for SNN applications is lacking. In this study, the implementation of long-term depression and potentiation as the spike timing-dependent plasticity (STDP) rule in the FTJ device was successful. Based on the measured data, a CrossSim simulator was used to simulate the classification of handwriting images. With a high accuracy of 95.79% for the Mixed National Institute of Standards and Technology (MNIST) dataset, the simulation results demonstrate that our device is capable of differentiating between handwritten images. This suggests that our FTJ device can be used as a synaptic device for implementing an SNN.
