Si-Doped HfO2-Based Ferroelectric Tunnel Junctions with a Composite Energy Barrier for Non-Volatile Memory Applications

Lee, Yoseop; Song, Sungmun; Ham, Woori; Ahn, Seung‐Eon

doi:10.3390/ma15062251

Cited by 18 publications

(11 citation statements)

References 41 publications

(42 reference statements)

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“…5, the reliability characteristics of the FTJ device are verified, and the HfO x -based FTJ continuously raises the need for memory window maintenance and high endurance. 53 Fig. 5(a) shows the endurance characteristic in terms of switching cycles from ±6 to ±7 V, increasing the amplitude by 0.2 V. Previous reports have indicated that the lower the voltage, the higher the endurance, but the lower the peak value of 2P r , the lower is the memory window.…”

Section: Resultsmentioning

confidence: 97%

Ferroelectric synaptic devices based on CMOS-compatible HfAlO_xfor neuromorphic and reservoir computing applications

Kim

Yun

et al. 2023

Nanoscale

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Section: Resultsmentioning

confidence: 97%

Ferroelectric synaptic devices based on CMOS-compatible HfAlO_xfor neuromorphic and reservoir computing applications

Kim

Yun

et al. 2023

Nanoscale

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“…The general techniques for implementing the various resistance states of the FTJ are amplitude or pulse width adjustments of the programming pulse [ 19 , 29 ]. Figure 3 a depicts the measurement process, which gradually increases the amplitude of the programming pulse to change the resistance state.…”

Section: Resultsmentioning

confidence: 99%

Improvement of Resistance Change Memory Characteristics in Ferroelectric and Antiferroelectric (like) Parallel Structures

Kho¹,

Hwang²,

Kim³

et al. 2023

Nanomaterials

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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.

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“…Owing to the asymmetric potential barrier, a state in which a significant amount of current flows due to low energy barriers to tunneling is called a low-resistance state (LRS); in contrast, a state in which less current flows due to high energy barriers to tunneling is called a high-resistance state (HRS). The tunnelling electro-resistance effect is a phrase for describing these two electrical resistance states [ 24 ]. In this study, the HRS and LRS of the manufactured FTJ device were verified using a semiconductor parameter analyzer ( Figure 2 a).…”

Section: Resultsmentioning

confidence: 99%

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

Kho¹,

Park²,

Kim³

et al. 2022

Nanomaterials

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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.

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Si-Doped HfO2-Based Ferroelectric Tunnel Junctions with a Composite Energy Barrier for Non-Volatile Memory Applications

Cited by 18 publications

References 41 publications

Ferroelectric synaptic devices based on CMOS-compatible HfAlO_xfor neuromorphic and reservoir computing applications

Ferroelectric synaptic devices based on CMOS-compatible HfAlO_xfor neuromorphic and reservoir computing applications

Improvement of Resistance Change Memory Characteristics in Ferroelectric and Antiferroelectric (like) Parallel Structures

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

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Si-Doped HfO2-Based Ferroelectric Tunnel Junctions with a Composite Energy Barrier for Non-Volatile Memory Applications

Cited by 18 publications

References 41 publications

Ferroelectric synaptic devices based on CMOS-compatible HfAlOxfor neuromorphic and reservoir computing applications

Ferroelectric synaptic devices based on CMOS-compatible HfAlOxfor neuromorphic and reservoir computing applications

Improvement of Resistance Change Memory Characteristics in Ferroelectric and Antiferroelectric (like) Parallel Structures

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

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Product

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Ferroelectric synaptic devices based on CMOS-compatible HfAlO_xfor neuromorphic and reservoir computing applications

Ferroelectric synaptic devices based on CMOS-compatible HfAlO_xfor neuromorphic and reservoir computing applications