Synaptic Characteristic of Hafnia-Based Ferroelectric Tunnel Junction Device for Neuromorphic Computing Application

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.

Spike Optimization to Improve Properties of Ferroelectric Tunnel Junction Synaptic Devices for Neuromorphic Computing System Applications

“…When the spike is applied to the sample, the post-spike applied to the bottom electrode takes on an inverted version of the pre-spike shape. Since the synaptic weight changes according to the temporal correlation of the signals applied to the synapse device, i.e., pre-and post-spike, the condition of the spike applied to the synapse device has a major impact on the STDP learning result [19]. In this case, the pre-and post-spike are signals of the same condition, and the spike used for STDP learning usually has the form of two voltage signals of different polarities in succession, as shown in Figure 1b [20][21][22].…”

Exploring Multi‐Bit Logic In‐Memory with Memristive HfO2‐Based Ferroelectric Tunnel Junctions

“…[12][13][14][15][16] Among them, HZO-based ferroelectric tunnel junctions (FTJs) are appropriate devices for LiM and other memristive applications owing to their low power consumption, high scalability, and complementary metal-oxide-semiconductor (CMOS) compatibility. [4,17] The basic concept of FTJ-base LiM is as follows. [18] The polarization state of FTJ is modulated by an externally applied voltage pulse, which directly affects the tunneling current, the output of the FTJ.…”