Role of metal-oxide interfaces in the multiple resistance switching regimes of Pt/HfO2/TiN devices

Brivio, Stefano; Frascaroli, Jacopo; Spiga, S.

doi:10.1063/1.4926340

Cited by 84 publications

(67 citation statements)

References 43 publications

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“…Ti and TiN layers are deposited by magnetron sputtering and the HfO 2 layer is deposited by atomic layer deposition at 300 °C, as described elsewhere (Brivio et al, 2015; Frascaroli et al, 2015). The switching mechanism of the proposed memristor is filamentary (Brivio et al, 2014), i.e., it is based on the disruption and the restoration of a conductive filament formed inside the oxide.…”

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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Section: Methodsmentioning

confidence: 99%

Analog Memristive Synapse in Spiking Networks Implementing Unsupervised Learning

et al. 2016

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“…Adding to the more frequently observed cf8 ‐BRS mode, a lower‐bias voltage f8 ‐BRS mode (reset process with TiN as anode) with lower resistance ratios (≤10) was also observed in similar devices . This f8 operation was further found to coexist with a complementary resistive switching (CRS) operation . Despite these various observations, the nature of partial coexistence of various switching modes and the conditions for the occurrence of quantization remain to be understood.…”

Section: Introductionmentioning

confidence: 91%

“…In order to achieve this goal, we have investigated as a model system a prototypical HfO 2 based RRAM device using Pt and TiN electrodes, which has been reported to exhibit reliable switching in several publications . In addition, this device type is particularly interesting due to multiple coexisting switching modes and polarities, as well as a large possible resistance window of operation, making it promising as multibit synaptic devices in neuromorphic computing . In the Pt/HfO 2 /TiN device configuration, with applied bias to Pt electrode, a stable cf8 ‐BRS mode has been most often reported.…”

Section: Introductionmentioning

confidence: 99%

Control of Switching Modes and Conductance Quantization in Oxygen Engineered HfO_x based Memristive Devices

Sharath

Vogel

Molina‐Luna

et al. 2017

Adv Funct Materials

113

110

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Hafnium oxide (HfO x )-based memristive devices have tremendous potential as nonvolatile resistive random access memory (RRAM) and in neuromorphic electronics. Despite its seemingly simple two-terminal structure, a myriad of RRAM devices reported in the rapidly growing literature exhibit rather complex resistive switching behaviors. Using Pt/HfO x /TiN-based metal-insulator-metal structures as model systems, it is shown that a well-controlled oxygen stoichiometry governs the filament formation and the occurrence of multiple switching modes. The oxygen vacancy concentration is found to be the key factor in manipulating the balance between electric field and Joule heating during formation, rupture (reset), and reformation (set) of the conductive filaments in the dielectric. In addition, the engineering of oxygen vacancies stabilizes atomic size filament constrictions exhibiting integer and half-integer conductance quantization at room temperature during set and reset. Identifying the materials conditions of different switching modes and conductance quantization contributes to a unified switching model correlating structural and functional properties of RRAM materials. The possibility to engineer the oxygen stoichiometry in HfO x will allow creating quantum point contacts with multiple conductance quanta as a first step toward multilevel memristive quantum devices.

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“…2b) and 10 year retention at 175°C has been previously demonstrated [2]. As expected, the set transition is abrupt with a sudden current jump at about -0.8 V. The transition is limited by the instrumental current compliance at 1 mA that, together with the device design [4], prevents destructive breakdown. Such abrupt resistance transition is usually explained by a threshold switching event that triggers a fast selfaccelerated process of CF formation inside the oxide [8].…”

Section: Resultsmentioning

confidence: 62%

“…Pt/HfO 2 /TiN devices, displaying good memory performances [2]- [4], are usually characterized by abrupt set transitions from high to low resistance states (HRS to LRS) that require current compliance in order to prevent an uncontrolled growth of a conductive filament (CF). Only few examples of gradual set transition in the same device structure exist in literature.…”

Section: Introductionmentioning

confidence: 99%

Gradual set dynamics in HfO₂-based memristor driven by sub-threshold voltage pulses

Brivio

Covi

Serb

et al. 2015

2015 International Conference on Memristive Systems (MEMRISYS)

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The switching dynamics of filamentary Pt/HfO 2 /TiN memristive devices is managed through sub-threshold pulses in order to display gradual resistance decrease useful for analog logic computation based on spiking networks. Such memristive devices are known to display abrupt set transitions (resistance decrease) that require current limitation because of the triggering of a threshold switching event. In this report, we demonstrate the gradual resistance decrease driven by trains of identical sub-threshold pulses. The experimental finding is explained by a compact model considering a gradual closure of the filament interruption and a following lateral filament growth IntroductionMemristive devices are now encountering a renewed interest for logic and for alternative computations, such as in spiking neuromorphic networks where analog behavior and gradual transitions are desirable[1]. Pt/HfO 2 /TiN devices, displaying good memory performances[2]-[4], are usually characterized by abrupt set transitions from high to low resistance states (HRS to LRS) that require current compliance in order to prevent an uncontrolled growth of a conductive filament (CF). Only few examples of gradual set transition in the same device structure exist in literature.[5] This fact is supposed to prevent their use for analog memristive applications. In this work, we demonstrate that a controlled resistance decrease can be induced by sub-threshold pulses, which allows a gradual resistance modulation. The result demonstrates the existence of a switching regime that differs from the usually known in the memory field and it is explained through a filamentary compact model that justify the resistance evolution. On the other hand, the result can pave the way for the exploitation of filamentary HfO 2 -based devices for neuromorphic architectures with memristive devices emulating the plasticity of biological synapses. Results and DiscussionPt/HfO 2 /TiN are fabricated sequentially by 40 nm TiN sputtering deposition, 5 nm HfO 2 atomic layer deposition and 50 nm Pt sputtering deposition. 40 x 40 m 2 devices are patterned by optical lithography and Pt lift-off [2],[4] (fig . 1a). Devices are characterized in DC by Agilent B1500 semiconductor parameter analyzer and in pulsed operation by custom instrumentation [6]. After a current controlled forming process, Pt/HfO 2 /TiN devices display reliable resistance switching between LRS and HRS ( fig.2a), as a result of the formation and partial dissolution of a CF connecting the two electrodes ( fig. 1b)[7]. The resistance levels are stable during cycling ( fig. 2b) and 10 year retention at 175°C has been previously demonstrated [2]. As expected, the set transition is abrupt with a sudden current jump at about -0.8 V. The transition is limited by the instrumental current compliance at 1 mA that, together with the device design [4], prevents destructive breakdown. Such abrupt resistance transition is usually explained by a threshold switching event that triggers a fast selfaccelerated process of CF forma...

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Role of metal-oxide interfaces in the multiple resistance switching regimes of Pt/HfO2/TiN devices

Cited by 84 publications

References 43 publications

Analog Memristive Synapse in Spiking Networks Implementing Unsupervised Learning

Analog Memristive Synapse in Spiking Networks Implementing Unsupervised Learning

Control of Switching Modes and Conductance Quantization in Oxygen Engineered HfO_x based Memristive Devices

Gradual set dynamics in HfO₂-based memristor driven by sub-threshold voltage pulses

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Role of metal-oxide interfaces in the multiple resistance switching regimes of Pt/HfO2/TiN devices

Cited by 84 publications

References 43 publications

Analog Memristive Synapse in Spiking Networks Implementing Unsupervised Learning

Analog Memristive Synapse in Spiking Networks Implementing Unsupervised Learning

Control of Switching Modes and Conductance Quantization in Oxygen Engineered HfOx based Memristive Devices

Gradual set dynamics in HfO2-based memristor driven by sub-threshold voltage pulses

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Control of Switching Modes and Conductance Quantization in Oxygen Engineered HfO_x based Memristive Devices

Gradual set dynamics in HfO₂-based memristor driven by sub-threshold voltage pulses