Pt/Ta 2 O 5 /HfO 2− x /Ti Resistive Switching Memory Competing with Multilevel NAND Flash

499

434

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.

Section: Wwwadvelectronicmatdementioning

confidence: 99%

Recommended Methods to Study Resistive Switching Devices

Lanza

Wong

Pop

et al. 2018

499

434

“…Recently, Hsu et al [ 12 ] and Yoon et al [ 10 ] reported promising rectifying RRAM devices with multilevel resistance states. Recently, Hsu et al [ 12 ] and Yoon et al [ 10 ] reported promising rectifying RRAM devices with multilevel resistance states.…”

Section: Analytical Model For the Writing Margin And Its Confi Rmatiomentioning

confidence: 99%

“…[7][8][9][10][11][12] Although the writing margin issue has been examined in several studies, [13][14][15][16][17] the effect of sneak currents has been overlooked in their analysis, or if any, they mostly relied on the numerical simulation method, which relatively lacked insightful description of the circuits and the mechanisms behind it. [7][8][9][10][11][12] Although the writing margin issue has been examined in several studies, [13][14][15][16][17] the effect of sneak currents has been overlooked in their analysis, or if any, they mostly relied on the numerical simulation method, which relatively lacked insightful description of the circuits and the mechanisms behind it.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Writing Margin Analysis and its Application to Stacked one Diode‐One Memory Device for High‐Density Crossbar Resistance Switching Random Access Memory

Yoon

Bae

Jeong

et al. 2016

Self Cite

The sneak current through the neighboring cells interrupts reading of a selected cell in high-resistance state in crossbar resistance switching random access memory (RRAM). As the sneak current mainly originates from a parallel resistance component to a selected cell, write operation, which requires suffi ciently high voltage delivered to the selected cell, has been regarded to have little relevance to the sneak current issue. In this work, it is revealed that an additional voltage drop on the wire resistances of selected word and bit lines causes an increase in voltage for the write operation (programming/erasing), whose degree is associated with the selectivity of the RRAM selector, array size, resistance of the selected cell, and wire resistances. The presence of sneak paths gives rise to an additional concern about the disturbance of the unselected cells, especially when the writing voltage of the selected cell was increased by the aforementioned factors. A fl oating voltage scheme is considered for proposing a unifi ed writing margin analysis model with diode selectors, and a feasible design strategy based on the analysis for an experimental RRAM device employing a diode with an extremely high rectifi cation ratio (8.4 × 10 8 ) is provided. Furthermore, application of the model to the universal RRAM devices, irrespective of their switching polarity, selector types, and adopted voltage schemes, is included. Figure 1. a) Floating or V dd scheme in the RRAM crossbar array. b) Parallel resistor circuit comprised of the selected cell ( R cell ) and the sneak current components ( R sneak ). c) Model RRAM device used in the simulation.

“…Thus, anion migration-related switching mechanism is typically referred to as valence change mechanism (VCM). Till now, electronic memristive devices have been reported in several systems mainly based on two kinds of mechanisms: charge trapping/detrapping [138][139][140][141][142][143][144][145] and metal-insulator transition (MIT). [101][102][103] However, unlike the fruitful findings of the metal cation filaments in the ECM cells, the details of the oxygen migration and the resultant oxygendeficient conductive filaments are still elusive and controversial owing to the difficulty in directly observing the oxygen migration process.…”

Section: Wwwadvelectronicmatdementioning

confidence: 99%

Memristive Synapses and Neurons for Bioinspired Computing

Yang

Huang

Guo

2019

176

127

and constant data movement are needed while working. In the human brain, huge and complex neural networks composed of gigantic amounts of neurons massively interconnected by an even larger number of synapses are in charge of computing and memory. Unlike a digital computer, information storage and processing happen at the same time in the brain. [3] Artificial intelligence (AI) that could rival biological neural networks is being continuously pursued by human being. There are two approaches to implement AI: one is the software simulation of neural networks with the aid of digital computers, another is developing hardware-integrated circuits of neural networks. In fact, AI based on the software simulation is evolving very fast thanks to the advances in the development of algorithm in recent years, and it even outperforms the human brain in specific complex tasks, such as playing the game of Go. [4] However, software-based AI suffers from critical issues of enormous power consumption and massive data throughput. Hardware-based AI systems are more efficient in terms of speed and power consumption; however, complementary metal oxide semiconductor (CMOS) transistors are inherently different from the basic building blocks of biological neural networks, neurons, and synapses, in terms of operation dynamic and behavior. A circuit consisting of at least ten transistors are required to realize the function of one biological synapse, which is impractical for the implementation of large networks, not to mention the human brain (roughly 10 11 neurons interconnected by ≈10 15 synapses). [5,6] Fortunately, the emergence of memristive devices with innate dynamics resembling biological synapses and neurons provides a feasible and simple way to build hardware-based bio-inspired computing systems. [7,8] For instance, only one compact memristive device with a metalinsulator-metal (MIM) sandwich structure is sufficient to reproduce some functions of a biological synapse. Moreover, the vector-matrix multiplication, which is believed to be the most data-intensive work in neural networks, can be naturally performed in a cross-bar array of memristive devices on the physical level based on Ohm and Kirchhoff laws. [9,10] These features endow the memristive devices ideally suited to realize highly efficient bioinspired computing systems in hardware.To realize highly efficient neuromorphic computing that is comparable to biological counterparts, bioinspired computing systems, consisting of biorealistic artificial synapses and neurons, are developed with memristive devices with native dynamics resembling biological synapses and neurons. Tremendous materials and devices have been successfully used to emulate diverse functions of synapses, as well as neurons, in the last decade. Herein, approaches to realize certain synaptic or neuronal functions are introduced with state-of-art experimental demonstrations. First, the dynamics and working principles of biological synapses and neurons are briefly presented to provide guidance for developing biore...