A &lt;inline-formula&gt; &lt;tex-math notation="LaTeX"&gt;$\mu $ &lt;/tex-math&gt;&lt;/inline-formula&gt;-Controller-Based System for Interfacing Selectorless RRAM Crossbar Arrays

Berdan, Radu; Serb, Alexantrou; Khiat, Ali; Regoutz, Anna; Papavassiliou, Christos; Prodromakis, Themis

doi:10.1109/ted.2015.2433676

Cited by 80 publications

(11 citation statements)

References 22 publications

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“…All characterisation of the devices has been done with our characterisation platform ArC ONE 28 , although the methodology itself is instrumentation independent. Read pulses are set to up to 50 ms in duration (depending on actual resistive state) and 0.2 V in amplitude.…”

Section: Methodsmentioning

confidence: 99%

An Electrical Characterisation Methodology for Benchmarking Memristive Device Technologies

Stathopoulos

Michalas

Khiat

et al. 2019

Sci Rep

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The emergence of memristor technologies brings new prospects for modern electronics via enabling novel in-memory computing solutions and energy-efficient and scalable reconfigurable hardware implementations. Several competing memristor technologies have been presented with each bearing distinct performance metrics across multi-bit memory capacity, low-power operation, endurance, retention and stability. Application needs however are constantly driving the push towards higher performance, which necessitates the introduction of a standard benchmarking procedure for fair evaluation across distinct key metrics. Here we present an electrical characterisation methodology that amalgamates several testing protocols in an appropriate sequence adapted for memristors benchmarking needs, in a technology-agnostic manner. Our approach is designed to extract information on all aspects of device behaviour, ranging from deciphering underlying physical mechanisms to assessing different aspects of electrical performance and even generating data-driven device-specific models. Importantly, it relies solely on standard electrical characterisation instrumentation that is accessible in most electronics laboratories and can thus serve as an independent tool for understanding and designing new memristive device technologies.

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Section: Methodsmentioning

confidence: 99%

An Electrical Characterisation Methodology for Benchmarking Memristive Device Technologies

Stathopoulos

Michalas

Khiat

et al. 2019

Sci Rep

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“…In the former case, during depression spike amplitude ranges from 0.1 to 1.2 V, during potentiation, from −0.1 to −0.65 V. The same spike is repeated 5 times before the amplitude is incremented by 50 mV (decremented by −50 mV for negative voltages) and the pulse duration is fixed at 100 μs. Measurements are performed using the custom instrument described in Berdan et al (2015). In the second experiment, the trains of identical pulses are constituted by 300 repetitions of −550 mV—high and 25 μs—long pulses for potentiation and 300 repetitions of 700 mV—high and 20 μs—long pulses for depression.…”

Section: Methodsmentioning

confidence: 99%

Analog Memristive Synapse in Spiking Networks Implementing Unsupervised Learning

et al. 2016

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Emerging brain-inspired architectures call for devices that can emulate the functionality of biological synapses in order to implement new efficient computational schemes able to solve ill-posed problems. Various devices and solutions are still under investigation and, in this respect, a challenge is opened to the researchers in the field. Indeed, the optimal candidate is a device able to reproduce the complete functionality of a synapse, i.e., the typical synaptic process underlying learning in biological systems (activity-dependent synaptic plasticity). This implies a device able to change its resistance (synaptic strength, or weight) upon proper electrical stimuli (synaptic activity) and showing several stable resistive states throughout its dynamic range (analog behavior). Moreover, it should be able to perform spike timing dependent plasticity (STDP), an associative homosynaptic plasticity learning rule based on the delay time between the two firing neurons the synapse is connected to. This rule is a fundamental learning protocol in state-of-art networks, because it allows unsupervised learning. Notwithstanding this fact, STDP-based unsupervised learning has been proposed several times mainly for binary synapses rather than multilevel synapses composed of many binary memristors. This paper proposes an HfO2-based analog memristor as a synaptic element which performs STDP within a small spiking neuromorphic network operating unsupervised learning for character recognition. The trained network is able to recognize five characters even in case incomplete or noisy images are displayed and it is robust to a device-to-device variability of up to ±30%.

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“…The current versus voltage (I-V) characteristics were obtained on 30 × 30 µm 2 devices using our inhouse mem ristor characterization platform ArC ONE ™ [43]. The voltage sweeping was carried out in staircase mode towards posi tive biases, while both positive and negative polarities were always applied to the TE with respect to the BE that was con tinuously kept grounded.…”

Section: Methodsmentioning

confidence: 99%

Electrical characteristics of interfacial barriers at metal—TiO₂ contacts

Michalas

Khiat

Stathopoulos

et al. 2018

J. Phys. D: Appl. Phys.

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materials (paper, flexible); adequate device level mobility [2] that can be further optimised by engineering appropriate gate dielectrics; high transparency due to their wide band gap; the capacity for postfabrication tunable resistance (memristive effects) [3,4], and good chemical stability. These features have led to the use of MOs in a variety of applications ranging from resistive random access memories (RRAMs) [5,6] and thin film transistors (TFTs) [7,8] to oxidebased photovol taics [9] and sensors [10], introducing a new era for large area transparent/stretchable electronics [11,12] and neuromor phic systems [13,14]. It is also worth mentioning two very recently published perspective articles [15,16] AbstractThe electrical properties of thin TiO 2 films have recently been extensively exploited with the aim of enabling a variety of metaloxide electron devices: unipolar and bipolar semiconductor devices and/or memristors. In these efforts, investigations into the role of TiO 2 as active material were the main focus; however, electrode materials are equally important. In this work we address this point by presenting a systematic quantitative electrical characterization study on the interface characteristics of metalTiO 2 metal structures. Our study employs typical contact materials that are used both as top and bottom electrodes in a metalTiO 2 metal setting. This allows an investigation of the characteristics of the interfaces as well as holistically studying an electrode's influence on the opposite interface, referred to in this work as the top/bottom electrodes interrelationship. Our methodology comprises the recording of current-voltage (I-V) characteristics from a variety of solidstate prototypes in the temperature range of 300 K -350 K, and their analysis through appropriate modelling. Clear field and temperaturedependent signature plots were also obtained, so as to shine more light on the role of each material as top/bottom electrodes in metalTiO 2 metal configurations. Our results highlight that these are not conventional metal-semiconductor contacts, and that several parameters are involved in the formation of the interfacial barriers, such as the electrode's position (atop or below the film), the electronegativity, the interface states, and even the opposite interface electrode material. Overall, our study provides a useful database for selecting appropriate electrode materials in TiO 2 based devices, offering new insights into the role of electrodes in metaloxide electronics applications.

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A <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>-Controller-Based System for Interfacing Selectorless RRAM Crossbar Arrays

Cited by 80 publications

References 22 publications

An Electrical Characterisation Methodology for Benchmarking Memristive Device Technologies

An Electrical Characterisation Methodology for Benchmarking Memristive Device Technologies

Analog Memristive Synapse in Spiking Networks Implementing Unsupervised Learning

Electrical characteristics of interfacial barriers at metal—TiO₂ contacts

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A <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>-Controller-Based System for Interfacing Selectorless RRAM Crossbar Arrays

Cited by 80 publications

References 22 publications

An Electrical Characterisation Methodology for Benchmarking Memristive Device Technologies

An Electrical Characterisation Methodology for Benchmarking Memristive Device Technologies

Analog Memristive Synapse in Spiking Networks Implementing Unsupervised Learning

Electrical characteristics of interfacial barriers at metal—TiO2 contacts

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Electrical characteristics of interfacial barriers at metal—TiO₂ contacts