Ferroelectricity Based Memory Devices: New-Generation of Materials and Applications

Hu, Yifan; Rabelo, Matheus; Kim, Tae-Yong; Cho, Jaewoong; Choi, Ji‐Won; Fan, Xinyi; Yi, Junsin

doi:10.1007/s42341-023-00445-9

Cited by 9 publications

(3 citation statements)

References 45 publications

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“…(7) To address these issues, various nonvolatile memory alternatives have been explored as potential replacements for flash memory. Notable candidates in this pursuit include ferroelectric random access memory (FeRAM), (8) phase-change random access memory (PCRAM), (9) magnetic random access memory (MRAM), (10) and resistive random access memory (ReRAM). (11)(12)(13) Among these alternatives, ReRAM devices have garnered considerable attention due to their advantageous features.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Influence of Copper Chemical Displacement Duration on Nonvolatile Oxide-based Resistive Memory Device Characteristics

Lin,

Yu,

Cheng

et al. 2024

Sensors and Materials

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Nonvolatile memory devices play a pivotal role within sensor systems, serving as integral components that significantly enhance the efficiency, reliability, and overall performance of these advanced technologies. In this paper, we present a nonvolatile oxide-based resistive memory device employing a chemical displacement technique (CDT) for copper thin film as the metal electrode. The utilization of CDT-Cu offers several distinct advantages, including costeffectiveness, high selectivity, and the ability to operate at lower temperatures. Notably, the CDT-Cu film exhibits a rough surface, which proves advantageous for facilitating the formation of filament pathways in the electrochemical metallization (ECM)-type resistive random-access memory (ReRAM) device. We systematically investigated the surface roughness of CDT-Cu samples, varying chemical displacement time, to shed light on the influence of the Cu electrode on the electrical performance of the resistive memory devices. The findings indicate that devices subjected to shorter CDT times, which yields rougher surfaces, demonstrate lower operation electric fields and enhanced reliability. This is attributed to the reduced voltage requirements, effectively mitigating the impact of Joule heating during device operation.

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Section: Introductionmentioning

confidence: 99%

Exploring the Influence of Copper Chemical Displacement Duration on Nonvolatile Oxide-based Resistive Memory Device Characteristics

Lin,

Yu,

Cheng

et al. 2024

Sensors and Materials

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“…Dielectric capacitors are key components of pulsed power applications, and are extensively used in microwave communications, electromagnetic devices, hybrid electric vehicles, and high-frequency inverters [1][2][3][4][5]. Notably, dielectric capacitors display ultrahigh power density, ultrafast charge-discharge rates, excellent fatigue resistance, and thermal stability as compared to batteries [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Improved Energy Storage Density and Efficiency of Nd and Mn Co-Doped Ba0.7Sr0.3TiO3 Ceramic Capacitors Via Defect Dipole Engineering

Choi,

Pattipaka,

Son

et al. 2023

Materials

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In this paper, we investigate the structural, microstructural, dielectric, and energy storage properties of Nd and Mn co-doped Ba0.7Sr0.3TiO3 [(Ba0.7Sr0.3)1−xNdxTi1−yMnyO3 (BSNTM) ceramics (x = 0, 0.005, and y = 0, 0.0025, 0.005, and 0.01)] via a defect dipole engineering method. The complex defect dipoles (MnTi”−VO∙∙)∙ and (MnTi”−VO∙∙) between acceptor ions and oxygen vacancies capture electrons, enhancing the breakdown electric field and energy storage performances. XRD, Raman, spectroscopy, XPS, and microscopic investigations of BSNTM ceramics revealed the formation of a tetragonal phase, oxygen vacancies, and a reduction in grain size with Mn dopant. The BSNTM ceramics with x = 0.005 and y = 0 exhibit a relative dielectric constant of 2058 and a loss tangent of 0.026 at 1 kHz. These values gradually decreased to 1876 and 0.019 for x = 0.005 and y = 0.01 due to the Mn2+ ions at the Ti4+- site, which facilitates the formation of oxygen vacancies, and prevents a decrease in Ti4+. In addition, the defect dipoles act as a driving force for depolarization to tailor the domain formation energy and domain wall energy, which provides a high difference between the maximum polarization of Pmax and remnant polarization of Pr (ΔP = 10.39 µC/cm2). Moreover, the complex defect dipoles with optimum oxygen vacancies in BSNTM ceramics can provide not only a high ΔP but also reduce grain size, which together improve the breakdown strength from 60.4 to 110.6 kV/cm, giving rise to a high energy storage density of 0.41 J/cm3 and high efficiency of 84.6% for x = 0.005 and y = 0.01. These findings demonstrate that defect dipole engineering is an effective method to enhance the energy storage performance of dielectrics for capacitor applications.

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“…The volume of trade in the worldwide electronic memory market is also increasing year by year, as illustrated in Figure 1. Among various technologies like RRAM [2], FeRAM [3], PCM [4], and flash memory [5], charge trapping memory has garnered significant interest due to its simple structure and exceptional performance. However, a major challenge in its development lies in its sensitivity to preparation and post-processing conditions.…”

Section: Introductionmentioning

confidence: 99%

Support Vector Regression Model for Determining Optimal Parameters of HfAlO-Based Charge Trapping Memory Devices

Wang

Chen

et al. 2023

Electronics

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The production and optimization of HfAlO-based charge trapping memory devices is central to our research. Current optimization methods, based largely on experimental experience, are tedious and time-consuming. We examine various fabrication parameters and use the resulting memory window data to train machine learning algorithms. An optimized Support Vector Regression model, processed using the Swarm algorithm, is applied for data prediction and process optimization. Our model achieves a MSE of 0.47, an R2 of 0.98856, and a recognition accuracy of 90.3% under cross-validation. The findings underscore the effectiveness of machine learning algorithms in non-volatile memory fabrication process optimization, enabling efficient parameter selection or outcome prediction.

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Ferroelectricity Based Memory Devices: New-Generation of Materials and Applications

Cited by 9 publications

References 45 publications

Exploring the Influence of Copper Chemical Displacement Duration on Nonvolatile Oxide-based Resistive Memory Device Characteristics

Exploring the Influence of Copper Chemical Displacement Duration on Nonvolatile Oxide-based Resistive Memory Device Characteristics

Improved Energy Storage Density and Efficiency of Nd and Mn Co-Doped Ba0.7Sr0.3TiO3 Ceramic Capacitors Via Defect Dipole Engineering

Support Vector Regression Model for Determining Optimal Parameters of HfAlO-Based Charge Trapping Memory Devices

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