The Role of Ti Buffer Layer Thickness on the Resistive Switching Properties of Hafnium Oxide-Based Resistive Switching Memories

Rahaman, S. Z.; Lin, Yu-Wei; Lee, Heng-Yuan; Chen, Yu‐Sheng; Chen, Pang-Shiu; Chen, Wei-Su; Hsu, Cheng Hsiung; Tsai, Kuang‐Lei; Tsai, Ming-Jinn; Wang, Peihua

doi:10.1021/acs.langmuir.7b00479

Cited by 52 publications

(24 citation statements)

References 56 publications

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“…There is a high probability of an oxygen rich Ti layer forming at the Cu/HfO 2 interface. The interfacial layer can effectively change the switching behavior in RRAM devices . Notably, the switching stability in those devices with a Ti interfacial layer is much better than in those without, as reported in our previous study .…”

Section: Resultssupporting

confidence: 71%

Quantized Conduction Device with 6‐Bit Storage Based on Electrically Controllable Break Junctions

Banerjee

Hwang

2019

Adv Elect Materials

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electron charge and h is the Planck constant. [6] Electronic switches with atomicsized functional building blocks are the main driving force of modern nanotechnology. The design of atomic dimensional memory devices is limited by the lithographic bottleneck. Nevertheless, the origin of atomic contact in nanoscale architectures is being investigated. [7] The conductive bridge RAM device with quantized conductance (QC) effect proposed by Terebe et al., [8] was based on the fabrication of a gap-type Pt/ nano-gap/Ag 2 S/Ag structure. A small operating voltage of ±600 mV can switch the device between the "OFF" and "ON" states. When a device changes its resistance from high to low, it is usually known as "set"; the reverse change is known as "reset". The QC effect is suitable to realize multiple levels in devices and has been studied in both electrochemical metallization type [9][10][11][12][13][14] and valance change memory type [15,16] gap-less RRAM. In most cases, switching is the after-effect of a stress dependent virginity breakdown, which can consolidate the CF for switching operations. Because the behavior of the CF of conductive bridge RAM is considered to be an example of atomic-switching, further discussion is mainly focused on conductive bridge RAM type devices. Higher initial stress injects more cations into the switching oxide layer that results in poor control over the reset process, resulting in abrupt state transition, and atomic conductance instability. [9][10][11][12][13][14] In another approach, single-atom memory has been proposed for mechanically controllable break junctions (MCBJs), [17] which are a kind of electronic device formed by two knife-edged metal wires placed with a nanometer sized gap, as shown in Figure 1a. MCBJs are well known as atomic or single molecular junction, and have even been investigated to in the context of control of quantum interferences in graphene. [18,19] In an MCBJ, when the two wires are curved, broadening the gap, the contact is broken, and the reverse order experiences an atomic-contact at the break junction. Break junctions are a possible approach to the fabrication of atomic switches. However, in reality, for an electronic device, such design is burdensome due to exorbitant lithographic processes. It is possible to prepare a break junction by electrically controlling the ion migration process in conductive bridge RAM devices, as the switching mechanism is driven by the formation of metallic wire like CF. Although the concept of QC in conductive bridge RAM has been investigated for over a decade, precise and reliable atomic-level control Atomic-level control of conductance in a Cu/Ti/HfO 2 /TiN-based electrically controllable break junction (ECBJ) is demonstrated. The ECBJ is designed through sophisticated stack engineering and refined electrical operation. Control over bias-induced ion migration is the key to forming the ECBJ. Precise atomic-level control is accomplished with an optimized high temperature forming (OHTF) scheme. OHTF-controlled single-atomic switch...

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Section: Resultssupporting

confidence: 71%

Quantized Conduction Device with 6‐Bit Storage Based on Electrically Controllable Break Junctions

Banerjee

Hwang

2019

Adv Elect Materials

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“…The elements that have been reported to have RS characteristics in binary oxides are shown in Table 2 . Among them, silicon oxide (SiO 2 ), [ 84 ] titanium oxide (TiO 2 ), [ 85 ] vanadium oxide (VO 2 ), [ 78 ] zirconium oxide (ZrO 2 ), [ 86 ] nickel oxide (NiO), [ 87 ] zinc oxide (ZnO), [ 88 ] hafnium oxide (HfO 2 ), [ 89 ] tantalum oxide (Ta 2 O 5 ), [ 90 ] and alumina (Al 2 O 3 ) [ 91 ] are relatively widely studied. Among them, Al 2 O 3 is a very common dielectric material that can be used as a functional layer to limit current, or as an RS layer.…”

Section: Investigation Of Storage Materials Of Memristormentioning

confidence: 99%

“…[ 344,345 ] HfO 2 ‐based memristor has excellent retention and multilevel operation capabilities. [ 89 ]…”

Section: Investigation Of Storage Materials Of Memristormentioning

confidence: 99%

“…vanadium oxide (VO 2 ), [78] zirconium oxide (ZrO 2 ), [86] nickel oxide (NiO), [87] zinc oxide (ZnO), [88] hafnium oxide (HfO 2 ), [89] tantalum oxide (Ta 2 O 5 ), [90] and alumina (Al 2 O 3 ) [91] are relatively widely studied. Among them, Al 2 O 3 is a very common dielectric material that can be used as a functional layer to limit current, or as an RS layer.…”

Section: Conventional Oxidesmentioning

confidence: 99%

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The Future of Memristors: Materials Engineering and Neural Networks

Sun

Chen

Yan

2020

Adv Funct Materials

213

173

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From Deep Blue to AlphaGo, artificial intelligence and machine learning are booming, and neural networks have become the hot research direction. However, due to the size limit of complementary metal–oxide–semiconductor (CMOS) transistors, von Neumann‐based computing systems are facing multiple challenges (such as memory walls). As the number of transistors required by the neural network increases, the development of neural networks based on the von Neumann computer is limited by volume and energy consumption. As the fourth basic circuit element, memristor shines in the field of neuromorphic computing. The new computer architecture based on memristor is widely considered as a substitute for the von Neumann architecture and has great potential to deal with the neural network and big data era challenge. This article reviews existing materials and structures of memristors, neurophysiological simulations based on memristors, and applications of memristor‐based neural networks. The feasibility and advancement of implementing neural networks using memristors are discussed, the difficulties that need to be overcome at this stage are put forward, and their development prospects and challenges faced are also discussed.

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“…Regarding the material engineering, active layer doping and electrode–oxide interface modification are widely employed techniques. In particular, the insertions of oxygen barrier layers (e.g., Al 2 O 3 ), and oxygen scavenging electrodes (e.g., Ti, Ta, Hf) have been demonstrated to strongly influence switching dynamics in both forming and switching behavior . Thickness and structure of the interlayer, which can be tuned depending on the deposition technique and its parameters, play a crucial role on the interface properties, having a profound effect on the device electrical response.…”

Section: Introductionmentioning

confidence: 99%

Switching Kinetics Control of W‐Based ReRAM Cells in Transient Operation by Interface Engineering

Shahrabi

Giovinazzo

Hadad

et al. 2019

Adv Elect Materials

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Tungsten (W) is one of the most promising materials to be used in resistive random‐access memory electrodes due to its low work function and compatibility with semiconductors, which raises the possibility of device integration, scalability, and low power consumption. However, W has multiple oxidation states that affect device reliability, due to the formation of semistable oxides at the switching interface. W chemical interaction is modulated through the insertion of Al2O3 or Ti interfacial layers. The time‐dependent switching kinetics are investigated in transient Set/Reset operations. It is observed that a compact and stoichiometric atomic‐layer‐deposited Al2O3 barrier layer completely prevents W oxidation, resulting in a sharp current transient. The use of a sputtered Ti buffer layer allows a partial W oxidation, defining a tunable high‐resistance state by pulse rise time control. Notable improvements in endurance, power consumption, resistance state stabilization, and cycle‐to‐cycle and device‐to‐device variability are reported. Switching kinetics and conductive nanofilament evolution are studied in detail to understand the microscopic effect of the interface modifications. The tunability of multi‐HRS states by pulse timing control in Pt/HfO2/Ti/W is in the interest of network and brain‐inspired computing applications, adding a degree of freedom in the modulation of its resistance.

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The Role of Ti Buffer Layer Thickness on the Resistive Switching Properties of Hafnium Oxide-Based Resistive Switching Memories

Cited by 52 publications

References 56 publications

Quantized Conduction Device with 6‐Bit Storage Based on Electrically Controllable Break Junctions

Quantized Conduction Device with 6‐Bit Storage Based on Electrically Controllable Break Junctions

The Future of Memristors: Materials Engineering and Neural Networks

Switching Kinetics Control of W‐Based ReRAM Cells in Transient Operation by Interface Engineering

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