Synaptic Characteristic of Hafnia-Based Ferroelectric Tunnel Junction Device for Neuromorphic Computing Application.
Wonwoo KhoGyuil ParkJisoo KimHyunjoo HwangJisu ByunYoomi KangMinjeong KangSeung-Eon AhnPublished in: Nanomaterials (Basel, Switzerland) (2022)
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-HfO 2 -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.
Keyphrases
- neural network
- electronic health record
- deep learning
- big data
- quality improvement
- primary care
- convolutional neural network
- machine learning
- prefrontal cortex
- white matter
- depressive symptoms
- optical coherence tomography
- resting state
- magnetic resonance
- data analysis
- physical activity
- heavy metals
- multiple sclerosis
- social media
- blood brain barrier
- computed tomography
- highly efficient
- functional connectivity
- contrast enhanced
- metal organic framework