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Gabriele Navarro

Bio: Gabriele Navarro is an academic researcher from University of Grenoble. The author has contributed to research in topics: Phase-change memory & GeSbTe. The author has an hindex of 12, co-authored 54 publications receiving 699 citations.

Papers published on a yearly basis

Papers
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Journal ArticleDOI
TL;DR: This manuscript describes the most recommendable methodologies for the fabrication, characterization, and simulation of RS devices, as well as the proper methods to display the data obtained.
Abstract: Resistive switching (RS) is an interesting property shown by some materials systems that, especially during the last decade, has gained a lot of interest for the fabrication of electronic devices, with electronic nonvolatile memories being those that have received the most attention. The presence and quality of the RS phenomenon in a materials system can be studied using different prototype cells, performing different experiments, displaying different figures of merit, and developing different computational analyses. Therefore, the real usefulness and impact of the findings presented in each study for the RS technology will be also different. This manuscript describes the most recommendable methodologies for the fabrication, characterization, and simulation of RS devices, as well as the proper methods to display the data obtained. The idea is to help the scientific community to evaluate the real usefulness and impact of an RS study for the development of RS technology. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

441 citations

Journal ArticleDOI
TL;DR: The model explaining the switching mechanism occurring in amorphous OTS materials under electric field involves the metastable formation of newly introduced metavalent bonds, which opens the way for design of improved Ots materials and for future types of applications such as brain-inspired computing.
Abstract: Fifty years after its discovery, the ovonic threshold switching (OTS) phenomenon, a unique nonlinear conductivity behavior observed in some chalcogenide glasses, has been recently the source of a real technological breakthrough in the field of data storage memories. This breakthrough was achieved because of the successful 3D integration of so-called OTS selector devices with innovative phase-change memories, both based on chalcogenide materials. This paves the way for storage class memories as well as neuromorphic circuits. We elucidate the mechanism behind OTS switching by new state-of-the-art materials using electrical, optical, and x-ray absorption experiments, as well as ab initio molecular dynamics simulations. The model explaining the switching mechanism occurring in amorphous OTS materials under electric field involves the metastable formation of newly introduced metavalent bonds. This model opens the way for design of improved OTS materials and for future types of applications such as brain-inspired computing.

77 citations

Proceedings ArticleDOI
01 Dec 2013
TL;DR: The need for a specific "programming-current-vs-time-profile" to finally achieve an LRS stable at high-working temperature with programming times compatible with industrial applications is demonstrated and fully enables PCM for embedded applications, in which data integrity after the peak temperature of reflow soldering must be ensured.
Abstract: In this paper, we investigate the impact of Ge-enrichment coupled to N- or C-doping in Ge2Sb2Te5 based materials on low-resistance state (LRS or SET) performance combined with high-resistance state (HRS or RESET) high-temperature data retention (HTDR) in Phase-Change Memories (PCM). These innovative materials have been integrated in state-of-the-art memory cell prototypes. For the first time, a focus on the trade-off between SET stability (which is affected by resistance drift) and RESET HTDR is proposed. This aspect has been extensively characterized. Through physico-chemical analysis and electrical characterization we demonstrate the need for a specific "programming-current-vs-time-profile" to finally achieve an LRS stable at high-working temperature with programming times compatible with industrial applications. Finally, the reliability of the HRS and the LRS obtained with our optimized programming procedure has been demonstrated through Reflow Soldering Temperature Profile (RSTP) tests. The last result fully enables PCM for embedded applications, in which data integrity after the peak temperature of reflow soldering must be ensured.

52 citations

Journal ArticleDOI
TL;DR: Through the use of system‐level simulation, it is shown that this device is especially adapted to a neuroscience‐inspired learning, highlighting how nanodevices can be suitable for bioinspired applications while retaining the qualities of industrial technology.

51 citations

Journal ArticleDOI
TL;DR: A novel procedure, named R-SET technique, is proposed to boost the SET speed of these innovative phase change materials by overcoming the decrease of crystallization speed caused by Ge enrichment.
Abstract: In this paper, we examine the problem of the drift of the low-resistance state (LRS) in phase change memories based on C or N doped and undoped Ge-rich Ge2Sb2Te5. A novel procedure, named R-SET technique, is proposed to boost the SET speed of these innovative phase change materials by overcoming the decrease of crystallization speed caused by Ge enrichment. The R-SET technique allows, at the same time, an optimized SET programming of the memory cell and the reduction of the LRS drift with respect to standard SET procedures. A circuit that generates the desired R-SET pulse based on a time reference scheme is proposed and discussed.

38 citations


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Journal ArticleDOI
TL;DR: This Review surveys the four physical mechanisms that lead to resistive switching materials enable novel, in-memory information processing, which may resolve the von Neumann bottleneck and examines the device requirements for systems based on RSMs.
Abstract: The rapid increase in information in the big-data era calls for changes to information-processing paradigms, which, in turn, demand new circuit-building blocks to overcome the decreasing cost-effectiveness of transistor scaling and the intrinsic inefficiency of using transistors in non-von Neumann computing architectures. Accordingly, resistive switching materials (RSMs) based on different physical principles have emerged for memories that could enable energy-efficient and area-efficient in-memory computing. In this Review, we survey the four physical mechanisms that lead to such resistive switching: redox reactions, phase transitions, spin-polarized tunnelling and ferroelectric polarization. We discuss how these mechanisms equip RSMs with desirable properties for representation capability, switching speed and energy, reliability and device density. These properties are the key enablers of processing-in-memory platforms, with applications ranging from neuromorphic computing and general-purpose memcomputing to cybersecurity. Finally, we examine the device requirements for such systems based on RSMs and provide suggestions to address challenges in materials engineering, device optimization, system integration and algorithm design. Resistive switching materials enable novel, in-memory information processing, which may resolve the von Neumann bottleneck. This Review focuses on how the switching mechanisms and the resultant electrical properties lead to various computing applications.

564 citations

Journal ArticleDOI
01 Aug 2018
TL;DR: It is shown that multilayer hexagonal boron nitride (h-BN) can be used as a resistive switching medium to fabricate high-performance electronic synapses, enabling the emulation of a range of synaptic-like behaviour, including both short- and long-term plasticity.
Abstract: Neuromorphic computing systems, which use electronic synapses and neurons, could overcome the energy and throughput limitations of today’s computing architectures. However, electronic devices that can accurately emulate the short- and long-term plasticity learning rules of biological synapses remain limited. Here, we show that multilayer hexagonal boron nitride (h-BN) can be used as a resistive switching medium to fabricate high-performance electronic synapses. The devices can operate in a volatile or non-volatile regime, enabling the emulation of a range of synaptic-like behaviour, including both short- and long-term plasticity. The behaviour results from a resistive switching mechanism in the h-BN stack, based on the generation of boron vacancies that can be filled by metallic ions from the adjacent electrodes. The power consumption in standby and per transition can reach as low as 0.1 fW and 600 pW, respectively, and with switching times reaching less than 10 ns, demonstrating their potential for use in energy-efficient brain-like computing. Vertically structured electronic synapses, which exhibit both short- and long-term plasticity, can be created using layered two-dimensional hexagonal boron nitride.

420 citations

Journal ArticleDOI
TL;DR: The opportunities, progress and challenges of integrating two-dimensional materials with in-memory computing and transistor-based computing technologies, from the perspective of matrix and logic computing, are discussed.
Abstract: Rapid digital technology advancement has resulted in a tremendous increase in computing tasks imposing stringent energy efficiency and area efficiency requirements on next-generation computing. To meet the growing data-driven demand, in-memory computing and transistor-based computing have emerged as potent technologies for the implementation of matrix and logic computing. However, to fulfil the future computing requirements new materials are urgently needed to complement the existing Si complementary metal–oxide–semiconductor technology and new technologies must be developed to enable further diversification of electronics and their applications. The abundance and rich variety of electronic properties of two-dimensional materials have endowed them with the potential to enhance computing energy efficiency while enabling continued device downscaling to a feature size below 5 nm. In this Review, from the perspective of matrix and logic computing, we discuss the opportunities, progress and challenges of integrating two-dimensional materials with in-memory computing and transistor-based computing technologies. This Review discusses the recent progress and future prospects of two-dimensional materials for next-generation nanoelectronics.

402 citations

Journal ArticleDOI
TL;DR: A critical survey of emerging neuromorphic devices and architectures enabled by quantum dots, metal nanoparticles, polymers, nanotubes, nanowires, two-dimensional layered materials and van der Waals heterojunctions with a particular emphasis on bio-inspired device responses that are uniquely enabled by low-dimensional topology, quantum confinement and interfaces.
Abstract: Memristive and nanoionic devices have recently emerged as leading candidates for neuromorphic computing architectures. While top-down fabrication based on conventional bulk materials has enabled many early neuromorphic devices and circuits, bottom-up approaches based on low-dimensional nanomaterials have shown novel device functionality that often better mimics a biological neuron. In addition, the chemical, structural and compositional tunability of low-dimensional nanomaterials coupled with the permutational flexibility enabled by van der Waals heterostructures offers significant opportunities for artificial neural networks. In this Review, we present a critical survey of emerging neuromorphic devices and architectures enabled by quantum dots, metal nanoparticles, polymers, nanotubes, nanowires, two-dimensional layered materials and van der Waals heterojunctions with a particular emphasis on bio-inspired device responses that are uniquely enabled by low-dimensional topology, quantum confinement and interfaces. We also provide a forward-looking perspective on the opportunities and challenges of neuromorphic nanoelectronic materials in comparison with more mature technologies based on traditional bulk electronic materials. This Review highlights the progress made towards the development of neuromorphic devices and architectures enabled by low-dimensional nanomaterials

390 citations

Journal ArticleDOI
TL;DR: Recent progress in the area of resistive random access memory (RRAM) technology which is considered one of the most standout emerging memory technologies owing to its high speed, low cost, enhanced storage density, potential applications in various fields, and excellent scalability is comprehensively reviewed.
Abstract: In this manuscript, recent progress in the area of resistive random access memory (RRAM) technology which is considered one of the most standout emerging memory technologies owing to its high speed, low cost, enhanced storage density, potential applications in various fields, and excellent scalability is comprehensively reviewed. First, a brief overview of the field of emerging memory technologies is provided. The material properties, resistance switching mechanism, and electrical characteristics of RRAM are discussed. Also, various issues such as endurance, retention, uniformity, and the effect of operating temperature and random telegraph noise (RTN) are elaborated. A discussion on multilevel cell (MLC) storage capability of RRAM, which is attractive for achieving increased storage density and low cost is presented. Different operation schemes to achieve reliable MLC operation along with their physical mechanisms have been provided. In addition, an elaborate description of switching methodologies and current voltage relationships for various popular RRAM models is covered in this work. The prospective applications of RRAM to various fields such as security, neuromorphic computing, and non-volatile logic systems are addressed briefly. The present review article concludes with the discussion on the challenges and future prospects of the RRAM.

379 citations