Atomic-scale fatigue mechanism of ferroelectric tunnel junctions

Yang, Yihao; Wu, Ming; Zheng, Xingwen; Chen, Kewei; Xu, Jibo; Xu, Zhiyu; Li, Xiaofei; Lou, Xiaojie; Wu, Di; Liu, Xiaohui; Pennycook, Stephen J.; Wen, Zheng

doi:10.1126/sciadv.abh2716

Cited by 32 publications

(28 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The fitted d is consistent with the measured thickness (∼3 nm) of PZT film by STEM in Figure a, suggesting that the Schottky barrier at the NSTO surface is almost eliminated at the ON state (see SI Section 2 for detailed discussions) . The Δϕ is related to the difference between the work functions of Ti (4.33 eV) and NSTO (4.2–4.3 eV). , The direct tunneling at the ON state and the obvious rectification effect at the OFF state are consistent with previous reports on MFS-type FTJs. ,,− …”

Section: Resultssupporting

confidence: 89%

High-Speed Nanoscale Ferroelectric Tunnel Junction for Multilevel Memory and Neural Network Computing

Wang

Guan

Sun

et al. 2022

ACS Appl. Mater. Interfaces

View full text Add to dashboard Cite

Ferroelectric tunnel junction (FTJ) is one promising candidate for next-generation nonvolatile data storage and neural network computing systems. In this work, the high-performance 50 nm-diameter Au/Ti/PbZr0.52Ti0.48O3 (∼3 nm, (111)-oriented)/Nb:SrTiO3 (Nb: 0.7 wt %) FTJs are achieved to demonstrate the scaling down capability of FTJ. As a nonvolatile memory, the FTJ shows eight distinct resistance states (3 bits) with a large ON/OFF ratio (>103), and these states can be switched at a fast speed of 10 ns. Intriguingly, the long-term potentiation/depression and spike timing-dependent plasticity, that is, fundamental functions of biological synapses, can be emulated in the nanoscale FTJ-based artificial synapse. A convolutional neural network (CNN) simulation is then carried out based on the experimental results, and a high recognition accuracy of ∼93.8% on fashion product images is obtained, which is very close to the result of ∼94.4% by a floating-point-based CNN software. In particular, the FTJ-based CNN simulation also exhibits robustness to input image noises. These results indicate the great potential of FTJ for high-density information storage and neural network computing.

show abstract

Section: Resultssupporting

confidence: 89%

High-Speed Nanoscale Ferroelectric Tunnel Junction for Multilevel Memory and Neural Network Computing

Wang

Guan

Sun

et al. 2022

ACS Appl. Mater. Interfaces

View full text Add to dashboard Cite

show abstract

“…Dynamically changing the read bias with cycling would somewhat counteract the observed TER fatigue in the samples. A faster fatigue rate with increased applied electric field has been reported in other works as well and was attributed to increased oxygen vacancy generation and aggregation with switching . The growth of a dead-layer was also observed in that case, which in our case does not seem likely, as we do not observe an increase of E c with cycling.…”

Section: Resultssupporting

confidence: 85%

“…A faster fatigue rate with increased applied electric field has been reported in other works as well and was attributed to increased oxygen vacancy generation and aggregation with switching. 42 The growth of a dead-layer was also observed in that case, which in our case does not seem likely, as we do not observe an increase of E c with cycling. Contrary to our improved fatigue performance at lower switching fields, Li et al present an increased rate of fatigue for nonsaturated switching of the ferroelectric, 43 a characteristic not observed in this study.…”

Section: ■ Resultssupporting

confidence: 57%

Top Electrode Engineering for Freedom in Design and Implementation of Ferroelectric Tunnel Junctions Based on Hf_1–xZr_xO₂

Athle

Persson

Troian

et al. 2022

ACS Appl. Electron. Mater.

View full text Add to dashboard Cite

Ferroelectric tunnel junctions (FTJs) based on ultrathin HfO 2 have great potential as a fast and energy-efficient memory technology compatible with complementary metal oxide semiconductors. FTJs consist of a ferroelectric film sandwiched between two distinct electrodes, the properties of which are intricately linked to the electrical properties of the FTJs. Here we utilize a W crystallization electrode (CE) to achieve a high and reproducible remanent polarization, combined with a metal replacement process in which the W is carefully removed and replaced by another top electrode (TE). In this way we separate the ferroelectric film properties from the device design and can thereby evaluate the effect of the TE work function (WF) and conduction band electron density (n e ) on the tunneling electroresistance (TER) and device reliability. We compare FTJs designed with a TiN bottom electrode and W, Cr, or Ni TE and find that the use of high electron density metals such as Ni or Cr as TE allows for an improved TER, albeit at the cost of reliability due to a large built-in electric field. To bypass this effect, a bilayer Cr/Ni TE is implemented, which allows for a high TER and minimal built-in field, leading to excellent retention and endurance beyond 10 8 cycles. The results presented here thus highlight a process flow for reliable design and implementation of FTJs.

show abstract

“…The ferroelectric domain switching dynamics can be obtained directly from the resistance switching dynamics by considering the FTJ as a parallel circuit of upward and downward polarization domains 35 . The relationship between the area fraction s for upward polarization domains and the junction resistance can be expressed as 1 /R = (1 − s ) /R L + s/R H 35 , 45 . Fig 3c shows that the pulse duration dependence of s at different V p has a plateau, suggesting the multistep ferroelectric switching process.…”

Section: Resultsmentioning

confidence: 99%

High-precision and linear weight updates by subnanosecond pulses in ferroelectric tunnel junction for neuro-inspired computing

et al. 2022

View full text Add to dashboard Cite

The rapid development of neuro-inspired computing demands synaptic devices with ultrafast speed, low power consumption, and multiple non-volatile states, among other features. Here, a high-performance synaptic device is designed and established based on a Ag/PbZr0.52Ti0.48O3 (PZT, (111)-oriented)/Nb:SrTiO3 ferroelectric tunnel junction (FTJ). The advantages of (111)-oriented PZT (~1.2 nm) include its multiple ferroelectric switching dynamics, ultrafine ferroelectric domains, and small coercive voltage. The FTJ shows high-precision (256 states, 8 bits), reproducible (cycle-to-cycle variation, ~2.06%), linear (nonlinearity <1) and symmetric weight updates, with a good endurance of >109 cycles and an ultralow write energy consumption. In particular, manipulations among 150 states are realized under subnanosecond (~630 ps) pulse voltages ≤5 V, and the fastest resistance switching at 300 ps for the FTJs is achieved by voltages <13 V. Based on the experimental performance, the convolutional neural network simulation achieves a high online learning accuracy of ~94.7% for recognizing fashion product images, close to the calculated result of ~95.6% by floating-point-based convolutional neural network software. Interestingly, the FTJ-based neural network is very robust to input image noise, showing potential for practical applications. This work represents an important improvement in FTJs towards building neuro-inspired computing systems.

show abstract

Atomic-scale fatigue mechanism of ferroelectric tunnel junctions

Abstract: Role of charged defects in resistance fatigue of ferroelectric tunnel junctions has been revealed at atomic scale.

Cited by 32 publications

References 68 publications

High-Speed Nanoscale Ferroelectric Tunnel Junction for Multilevel Memory and Neural Network Computing

High-Speed Nanoscale Ferroelectric Tunnel Junction for Multilevel Memory and Neural Network Computing

Top Electrode Engineering for Freedom in Design and Implementation of Ferroelectric Tunnel Junctions Based on Hf_1–xZr_xO₂

High-precision and linear weight updates by subnanosecond pulses in ferroelectric tunnel junction for neuro-inspired computing

Contact Info

Product

Resources

About

Atomic-scale fatigue mechanism of ferroelectric tunnel junctions

Abstract: Role of charged defects in resistance fatigue of ferroelectric tunnel junctions has been revealed at atomic scale.

Cited by 32 publications

References 68 publications

High-Speed Nanoscale Ferroelectric Tunnel Junction for Multilevel Memory and Neural Network Computing

High-Speed Nanoscale Ferroelectric Tunnel Junction for Multilevel Memory and Neural Network Computing

Top Electrode Engineering for Freedom in Design and Implementation of Ferroelectric Tunnel Junctions Based on Hf1–xZrxO2

High-precision and linear weight updates by subnanosecond pulses in ferroelectric tunnel junction for neuro-inspired computing

Contact Info

Product

Resources

About

Top Electrode Engineering for Freedom in Design and Implementation of Ferroelectric Tunnel Junctions Based on Hf_1–xZr_xO₂