Design Rules for Memories Based on Graphene Ferroelectric Field-Effect Transistors

Amiri, Mojtaba; Heidler, Jonas; Müllen, Kläus; Asadi, Kamal

doi:10.1021/acsaelm.9b00532

Cited by 29 publications

(16 citation statements)

References 44 publications

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“…An efficient gating of graphene can be strategically coupled with a large polarization field at a ferroelectric interface to build high-performance ferroelectric graphene FET (Fe-GFET) memory devices. − The majority of reported Fe-GFETs incorporated a separate ferroelectric layer, while other studies suggested a graphene–ferroelectric composite channel . For flexible memory applications, the prototypical ferroelectric copolymer poly(vinyl fluoride)–trifluoroethylene (PVDF-TrFE) has been widely utilized.…”

Section: Progress Report: a Focus On Structural Classificationmentioning

confidence: 99%

“…There are several important considerations for flexible Fe-GFET memories. For instance, Hassanpour Amiri et al recently proposed modeling-based design rules for Fe-GFETs . This analytical model effectively combined charge displacement of the ferroelectric layer and charge transport in the graphene channel, in the theoretical prediction of an overall hysteretic behavior.…”

Section: Progress Report: a Focus On Structural Classificationmentioning

confidence: 99%

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Flexible Graphene-Channel Memory Devices: A Review

Sattari-Esfahlan

Kim

2021

ACS Appl. Nano Mater.

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There is an increasing importance of memory technologies in our ever-digitalizing society, which is characterized by the generation and use of a tremendous amount of real-time data. Beyond traditional performance requirements, mechanical flexibility of memory systems becomes therefore critical to enabling emerging applications such as the Internet of things. Graphene, now an established nanomaterial platform, is a promising element for building such high-performance memories with an unconventional form factor because of its exceptional electrical conductivities, outstanding mechanical properties, and processing versatility desirable for hybrid integration. Here, we provide an overview of recently developed flexible memory devices based on graphene and its functionalized counterparts. A defining feature of this review is that it exclusively compares and analyzes the devices that meet the following two criteria: (i) an explicit demonstration of working devices on a flexible substrate is reported; (ii) graphene is employed as an active channel rather than as an electrode. Our primary focus is to systematically classify various types of memories, in view of their materials, structural, and functional characteristics. For this, the shapes and compositions of the conductive channel, the key operational mechanisms underlying the electrical functionalities, and the major characteristics of representative device structures and materials systems are carefully evaluated. Furthermore, the applicability of flexible graphene memories to the neuromorphic computing area is discussed. Finally, we address several remaining issues that need to be solved for future technological advancements.

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Section: Progress Report: a Focus On Structural Classificationmentioning

confidence: 99%

Section: Progress Report: a Focus On Structural Classificationmentioning

confidence: 99%

Flexible Graphene-Channel Memory Devices: A Review

Sattari-Esfahlan

Kim

2021

ACS Appl. Nano Mater.

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“…An analytical model was previously developed that coupled voltage-dependent charge displacement of the ferroelectric gate layer with the charge transport across the graphene channel. [57] To extend the formalism to multi-bit graphene Fe-FET, the geometrical topography was incorporated in the charge displacement description of the ferroelectric gate layer. The displacement, D, of a ferroelectric gate layer with a constant thickness, t, is the sum of the linear dielectric response and the ferroelectric polarization and is given by…”

Section: Multi-bit Graphene Memory Through Designmentioning

confidence: 99%

Designing Multi‐Level Resistance States in Graphene Ferroelectric Transistors

Amiri

Heidler

Müllen

et al. 2020

Adv Funct Materials

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Conventional memory elements code information in the Boolean "0" and "1" form. Devices that exceed bistability in their resistance are useful as memory for future data storage due to their enhanced memory capacity, and are also a necessity for contemporary applications such as neuromorphic computing.Here, with the aid of an experimentally validated device model, design rules are outlined and more than two stable resistance states in a graphene ferroelectric field-effect transistor are experimentally demonstrated. The design methodology can be extrapolated for on-demand introduction of multiple resistance states in ferroelectric transistors for applications both in data storage and neuromorphic computing.

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“…Pyroelectric energy harvesting (PyEH) is preferred to be operated at lower possible temperatures. Thus, by increasing the temperature of a pyroelectric material, the domains get aligned and give rise to additional electricity by accumulating charges on the top and bottom electrodes. − …”

Section: Introductionmentioning

confidence: 99%

Structural Tailing and Pyroelectric Energy Harvesting of P(VDF-TrFE) and P(VDF-TrFE-CTFE) Ferroelectric Polymer Blends

Shehzad

Wang

2020

ACS Omega

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The copolymer P(VDF-TrFE) is a normal ferroelectric because the bulky TrFE monomer improves its crystalline chain structure, while the terpolymer P(VDF-TrFE-CTFE) is a relaxor ferroelectric because the third monomer CTFE makes it amorphous. Herein, in order to induce a crystalline beta phase in the terpolymer, we blended a small amount of crystalline P(VDF-TrFE) into P(VDF-TrFE-CTFE) and investigated the effect of blending on the pyroelectric energy harvesting (PyEH) properties. The polarization-electric field hysteresis loops at different temperatures and energy densities were investigated. The PyEH energy density (N D ) is compared with the electrical energy density (U E ). The U E and N D at the ferroelectric−paraelectric transition temperature for the χ = 0.1 blend are reported as 3.18 and 5.04 J/cm 3 , respectively, which are higher than the other polymer blends. Interestingly, the N D of the χ = 0.9 blend is found to be 3.44 J/cm 3 when operated at lower and higher temperatures, that is, at T L = 25 °C and T H = 40 °C, respectively, which is the highest possible energy density at the lowest possible transition temperature for the polymer blends.

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Design Rules for Memories Based on Graphene Ferroelectric Field-Effect Transistors

Cited by 29 publications

References 44 publications

Flexible Graphene-Channel Memory Devices: A Review

Flexible Graphene-Channel Memory Devices: A Review

Designing Multi‐Level Resistance States in Graphene Ferroelectric Transistors

Structural Tailing and Pyroelectric Energy Harvesting of P(VDF-TrFE) and P(VDF-TrFE-CTFE) Ferroelectric Polymer Blends

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