A Novel Read Scheme for Large Size One-Resistor Resistive Random Access Memory Array

Zackriya, Mohammed; Kittur, Harish M.; Chin, Albert

doi:10.1038/srep42375

Cited by 49 publications

(23 citation statements)

References 8 publications

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“…It gives a diode-like current versus voltage characteristic, which could minimize sneak currents (at V/2) in crossbar resistive random access memory devices. [17] Therefore, systematic studies of memristive switching in ferroelectric materials are critical in understanding the underlying operation mechanisms and enabling ferroelectric memristors for neuromorphic computing and nonvolatile memory. [18] The switchable diode effect (SDE) is considered as one of the most intriguing phenomena in metal/ferroelectric/metal (M/ FE/M) memristors, where a relatively thick ferroelectric layer is used in comparison with an ultrathin ferroelectric layer used in FTJs.…”

Section: Introductionmentioning

confidence: 99%

Couplings of Polarization with Interfacial Deep Trap and Schottky Interface Controlled Ferroelectric Memristive Switching

Chen

Zhang

Dedon

et al. 2020

Adv Funct Materials

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Memristors with excellent scalability have the potential to revolutionize not only the field of information storage but also neuromorphic computing. Conventional metal oxides are widely used as resistive switching materials in memristors. Interface-type memristors based on ferroelectric materials are emerging as alternatives in the development of high-performance memory devices. A clear understanding of the switching mechanisms in this type of memristors, however, is still in its early stages. By comparing the bipolar switching in different systems, it is found that the switchable diode effect in ferroelectric memristors is controlled by polarization modulated Schottky barrier height and polarization coupled interfacial deep states trapping/ detrapping. Using semiconductor theories with consideration of polarization effects, a phenomenological theory is developed to explain the currentvoltage behavior at the metal/ferroelectric interface. These findings reveal the critical role of the interaction among polarization charges, interfacial defects, and Schottky interface in controlling ferroelectric resistive switching and offer the guidance to design ferroelectric memristors with enhanced performance.

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Section: Introductionmentioning

confidence: 99%

Couplings of Polarization with Interfacial Deep Trap and Schottky Interface Controlled Ferroelectric Memristive Switching

Chen

Zhang

Dedon

et al. 2020

Adv Funct Materials

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“…The excellent endurance of the PVD-SiN x device is related to the high number of defects that can be set and reset easily, with less destruction to the dielectric material. Figure 8(a,b) display the device-to-device and cycle-to-cycle distributions of V set and V reset respectively, which are critical for the RRAM cross-point array [24][25][26] . The coefficient of variation (CV) was used to study the distribution and is defined as follows:…”

mentioning

confidence: 99%

High Performance All Nonmetal SiNx Resistive Random Access Memory with Strong Process Dependence

Yen

Chin

Gritsenko

2020

Sci Rep

Self Cite

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All-nonmetal resistive random access memory (RRAM) with a n + -Si/Sin x /p + -Si structure was investigated in this study. the device performance of Sin x developed using physical vapor deposition (PVD) was significantly better than that of a device fabricated using plasma-enhanced chemical vapor deposition (pecVD). the Sin x RRAM device developed using pVD has a large resistance window that is larger than 10 4 and exhibits good endurance to 10 5 cycles under switching pulses of 1 μs and a retention time of 10 4 s at 85 °C. Moreover, the SiN x RRAM device developed using pVD had tighter device-todevice distribution of set and reset voltages than those developed using pecVD. Such tight distribution is crucial to realise a large-size cross-point array and integrate with complementary metal-oxidesemiconductor technology to realise electronic neurons. the high performance of the Sin x RRAM device developed using pVD is attributed to the abundant defects in the pVD dielectric that was supported by the analysed conduction mechanisms obtained from the measured current-voltage characteristics.Resistive random access memory (RRAM) 1-26 has attracted considerable attention over the past two decades due to its simple structure, nonvolatility, high scalability, rapid switching speed, and relatively low operating power. These advantages make RRAM devices suitable for use in future artificial intelligence and neuromorphic computing applications 1-4 . A variety of binary composite materials, such as HfO x , TaO x , TiO x , SiO x , and GeO x , that exhibit different device properties have been used as the switching layer. Although various conduction mechanisms for RRAMs have been proposed, the carrier transport behaviour of RRAMs still has not been completely confirmed. To prevent the metal ions from contributing to transport behaviour, we pioneered nonmetal GeO x dielectric RRAM devices 6-10 and all-nonmetal N + -Si/SiO x /P + -Si RRAM devices 11 . In this study, we investigated the all-nonmetal SiN x RRAM devices in which the bond enthalpy of SiN is lower than that of SiO 27 . The SiN x material has been widely used in the semiconductor industry for various applications, such as the passivation layer of an integrated circuit and the charge-trapping layer of a flash memory. This material can be easily integrated into complementary metal-oxide-semiconductor (CMOS) technology. Moreover, the RRAM device performance strongly depends on the SiN x formation process. A high-performance RRAM device with a large memory window, good endurance, long retention time, and tight device-to-device distribution of the set-reset voltages V set -V reset is only achievable when the SiN x layers are formed using low-temperature physical vapor deposition (PVD) rather than the standard plasma-enhanced chemical vapor deposition (PECVD). The high-performance SiN x RRAM device developed using PVD (PVD-SiN x RRAM device) is linked to the high number of defects and high amount of defect-related current conduction in the SiN x dielectric.

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“…While there have been considerable efforts to model crossbar arrays in the past, in most attempts, the selector device parameters were not included in the models. Most of these crossbar array modelling have been done using the SPICE modelling tool [15][16][17]. However, modelling large memory arrays above a megabit requires extensive computational power with SPICE [18].…”

Section: Introductionmentioning

confidence: 99%

Modelling resistive and phase-change memory with passive selector arrays: a MATLAB tool

Noori

Groot

2020

J Comput Electron

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Memristor devices are crucial for developing neuromorphic computers and next-generation memory technologies. In this work, we provide a comprehensive modelling tool for simulating static DC reading operations of memristor crossbar arrays that use passive selectors with matrix algebra in MATLAB. The software tool was parallel coded and optimised to run with personal computers and distributed computer clusters with minimised CPU and memory consumption. We study the effect of changing the line resistance, array size, voltage selection scheme, selector diode's ideality factor, reverse saturation current and sense resistance on the electrical behaviour and expected sense margin of a conventional one-diode-one-resistor crossbar arrays. We then investigate the effect of single-and dual-side array biasing and grounding on the dissipated current throughout the array cells. The tool we offer to the memristor community and the studies we present enable the design of larger and more practical memristor arrays for application in data storage and neuromorphic computing.

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A Novel Read Scheme for Large Size One-Resistor Resistive Random Access Memory Array

Cited by 49 publications

References 8 publications

Couplings of Polarization with Interfacial Deep Trap and Schottky Interface Controlled Ferroelectric Memristive Switching

Couplings of Polarization with Interfacial Deep Trap and Schottky Interface Controlled Ferroelectric Memristive Switching

High Performance All Nonmetal SiNx Resistive Random Access Memory with Strong Process Dependence

Modelling resistive and phase-change memory with passive selector arrays: a MATLAB tool

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