Resistive Random Access Memory Research Articles

An Account of Natural Material-Based Nonvolatile Memory Device

The development in electronic sector has brought a remarkable change in the lifestyle of mankind. At the same time, this technological advancement results in adverse effect on environment due to the use of toxic and non-degradable materials in various electronic devices. With the emergence of environmental problems, the green, reprogrammable, biodegradable, sustainable and environmental-friendly electronic devices have become one of the best solutions for protecting our environment from hazardous materials without compromising the growth of the electronic industry. Natural material has emerged as the promising candidate for the next generation of electronic devices due to its easy processing, transparency, flexibility, abundant resources, sustainability, recyclability, and simple extraction. This review targets the characteristics, advancements, role, limitations, and prospects of using natural materials as the functional layer of a resistive switching memory device with a primary focus on the switching/memory properties. Among the available memory devices, resistive random access memory, write once read many unipolar memory, etc., devices have a huge potential to become the nonvolatile memory of the next generation owing to their simple structure, high scalability, and low power consumption. The motivation behind this work is to promote the use of natural materials in electronic devices and attract researchers toward a green solution of hazardous problems associated with the electronic devices.

Capacitive and RRAM Forming-Free Memory Behavior of Electron-Beam Deposited Ta2O5 Thin Film for Nonvolatile Memory Application

The present study reports the presence of capacitive memory and forming-free resistive random access memory (RRAM) in a tantalum pentoxide (Ta2O5) thin film (TF) device. The capacitance and voltage analysis of the electron-beam evaporated Ta2O5 TF device exhibits three distinct regimes: inversion, depletion, and accumulation, which decrease as the frequency of operation increases. This behavior is attributed to the interface state density (Dit) and series resistance (Rs). Moreover, the memory window of the Ta2O5 device was found to increase from 1.2 (± 1 V) to 7.9 (± 10 V). Furthermore, the resistive switching mechanism was investigated through the current (I) vs voltage (V) measurement for 1000 cycles. The device exhibits abnormal forming-free bipolar resistive switching. In addition, a desirable and stable switching behavior with resistance ratio of 102 and a retention up to 103 s at +2.5 V was observed. The overall findings of the Au/Ta2O5 TF/Si device could provide a pave way for nonvolatile memory (NVM) application.

Multi-Level Resistive Al/Ga2O3/ITO Switching Devices with Interlayers of Graphene Oxide for Neuromorphic Computing.

Recently, resistive random access memory (RRAM) has been an outstanding candidate among various emerging nonvolatile memories for high-density storage and in-memory computing applications. However, traditional RRAM, which accommodates two states depending on applied voltage, cannot meet the high density requirement in the era of big data. Many research groups have demonstrated that RRAM possesses the potential for multi-level cells, which would overcome demands related to mass storage. Among numerous semiconductor materials, gallium oxide (a fourth-generation semiconductor material) is applied in the fields of optoelectronics, high-power resistive switching devices, and so on, due to its excellent transparent material properties and wide bandgap. In this study, we successfully demonstrate that Al/graphene oxide (GO)/Ga2O3/ITO RRAM has the potential to achieve two-bit storage. Compared to its single-layer counterpart, the bilayer structure has excellent electrical properties and stable reliability. The endurance characteristics could be enhanced above 100 switching cycles with an ON/OFF ratio of over 103. Moreover, the filament models are also described in this thesis to clarify the transport mechanisms.

Instantaneous Gelation of a Self-Healable Wide-Bandgap Semiconducting Supramolecular Mg(II)-Metallohydrogel: An Efficient Nonvolatile Memory Design with Supreme Endurance

An efficient strategy for room-temperature, atmospheric-pressure synthesis of a supramolecular metallohydrogel of the Mg(II) ion, i.e., Mg@3AP, using the metal-coordinating organic ligand 3-amino-1-propanol as a low-molecular-weight gelator (LMWG) in a water medium has been developed. Through a rheological analysis, we looked into the mechanical properties of the supramolecular Mg(II)-metallohydrogel. The self-healing nature of the metallohydrogel is confirmed along with the thixotropic characteristics. Investigation using field emission scanning electron microscopy revealed the hierarchical network of the supramolecular metallohydrogel. The EDX elemental mapping confirms the primary chemical constituents of the metallohydrogel. The possible metallohydrogel formation strategy has been analyzed through FT-IR spectroscopic studies. In this work, Schottky diode structures in a metal–semiconductor–metal geometry structures based on a magnesium(II) metallohydrogel (Mg@3AP) have been constructed, and charge transport behavior has been observed. Furthermore, here, it is demonstrated that the resistive random access memory (RRAM) device based on Mg@3AP exhibits bipolar resistive switching behavior at room temperature and ambient conditions. We have also looked into the switching mechanism through the formation (rupture) of conductive filaments between the metal electrodes to understand the process of resistive switching behavior. With a high on/off ratio (∼100), this RRAM device exhibits remarkable switching endurance over 10,000 switching cycles. These structures are suitable for use in nonvolatile memory design, neuromorphic computing, flexible electronics, and optoelectronics, among other fields, due to their simple fabrication procedures, reliable resistive switching behavior, and stability of the current system.

Resistive random access memory based on graphene oxide with UV-O3 treatment

Resistive random access memory based on graphene oxide with UV-O3 treatment

A RRAM-Based True Random Number Generator with 2T1R Architecture for Hardware Security Applications.

Resistance random access memory (RRAM) based true random number generator (TRNG) has great potential to be applied to hardware security owing to its intrinsic switching variability. Especially the high resistance state (HRS) variation is usually taken as the entropy source of RRAM-based TRNG. However, the small HRS variation of RRAM may be introduced owing to fabrication process fluctuations, which may lead to error bits and be vulnerable to noise interference. In this work, we propose an RRAM-based TRNG with a 2T1R architecture scheme, which can effectively distinguish the resistance values of HRS with an accuracy of 1.5 kΩ. As a result, the error bits can be corrected to a certain extent while the noise is suppressed. Finally, a 2T1R RRAM-based TRNG macro is simulated and verified using the 28 nm CMOS process, which suggests its potential for hardware security applications.

Al2O3 interfacial layer derived hybrid conductive filament for the reliability enhancement of Ta2O5-based resistive random access memory

This study used an Al2O3 interfacial layer to improve the reliability characteristics of Ag/Ta2O5/Au resistive random access memory (RRAM), such as endurance and retention. The Ag/Ta2O5/Au device without the Al2O3 interfacial layer exhibited a high on/off ratio, low endurance, and poor retention. In Ag/Ta2O5/Au devices, it is difficult to precisely control the formation and rupture of conductive filaments (CFs) owing to the high diffusion coefficient of Ag cations. However, in the RS mechanism of Ag/Ta2O5/Au with an Al2O3 interfacial layer, a hybrid CF composed of Ag cations in Ta2O5 and oxygen vacancies in the Al2O3 layer was formed during the set process. During the reset process, the hybrid CF was stably ruptured by the recombination of oxygen vacancies and oxygen ions at the Al2O3 interfacial layer. This RS mechanism induces stable switching characteristics, improving endurance and retention. Therefore, Ag/Ta2O5/Au with an Al2O3 interfacial layer exhibits relatively high endurance (up to 700 cycles) and excellent retention (up to 1.0 × 104 s) while maintaining a high on/off ratio (>106) compared to those of the Ag/Ta2O5/Au device.

Comparative analysis of low-frequency noise based resistive switching phenomenon for filamentary and interfacial RRAM devices

We investigate the low-frequency noise (LFN) characteristics of resistive switching random access memory (RRAM) devices with metal–insulator–metal structures of TiN/Ti/TiO2/TiN (filamentary) and TiN/Ti/TiO2/HfO2/TiN (interfacial). By comparing filamentary and interfacial RRAM devices, we stipulate guidelines for noise sources according to the resistance states or resistive switching mechanisms of RRAM device. In filamentary RRAM devices, the dominant noise source in the low resistance state is the localized oxygen vacancies, whereas the noise characteristics in the high resistance state represent a bulk effect. In interfacial RRAM devices, the noise characteristics in both resistance states represent the bulk effect. The physical mechanism of the multilevel cell operation is inferred by the I–V characteristics in both Icc and Vreset control modes. Specifically, the changes in the structure of the conductive filament inside the switching layer can be confirmed through LFN measurement.

A High-Parallelism RRAM-Based Compute-In-Memory Macro With Intrinsic Impedance Boosting and In-ADC Computing

Resistive random access memory (RRAM) is considered to be a promising compute-in-memory (CIM) platform; however, they tend to lose energy efficiency quickly in high-throughput and high-resolution cases. This paper proposes an analog-oriented RRAM array/read circuits co-design technique to solve the trade-offs between distortion, power consumption, throughput, and chip area. In previous works, the RRAM array is built in a digital mindset that access transistors only serve as switches. Instead, this work investigates a new perspective that utilizes access transistors as common-gate current buffers which reduce the operating current and amplify the output impedance. To further decrease the complexity of peripheral circuits, the idea of In-ADC Computing (IAC) is proposed, which performs shift-add operation together with analog-to-digital conversion. Benefiting from proposed ideas, a pre-trained VGG-8 network based on the CIFAR-10 dataset can be implemented and an accuracy of 87.2% is achieved with 8.9 TOPS/W energy efficiency (for 8-bit MAC operation), demonstrating that the proposed techniques enable low-distortion partial sum results while still be able to operate in a power-efficient way.

Endurance Prediction Based on Hidden Markov Model and Programming Optimization for 28nm 1Mbit Resistive Random Access Memory Chip

We proposed a state transition probability model based on Hidden Markov Model (HMM), which can predict the lifetime for different endurance failure modes. The prediction span of this model is 500 cycles, and the accuracy rate can reach 83.6 %. Based on the prediction results, we proposed an optimized programming algorithm to rescue the failing cells during endurance. The failure rate of memory chip is reduced by 38 % after 5000 cycles. The forecasting model is effective and of practical value since it is based on the data from 28nm 1Mbit Resistive Random Access Memory (RRAM) chips, including the peripheral circuit noise, machine noise and device randomness. This prediction model is significant for promoting RRAM application and improving memory chip utilization.

An ADC-Less RRAM-Based Computing-in-Memory Macro With Binary CNN for Efficient Edge AI

Resistive random-access memory (RRAM) based non-volatile computing-in-memory (nvCIM) has been regarded as a promising solution to enable efficient data-intensive artificial intelligence (AI) applications on resource-limited edge systems. However, existing weighted-current summation-based nvCIM suffers from device non-idealities and significant time, storage, and energy overheads when processing high-precision analog signals. To address these issues, we propose a 3T2R digital nvCIM macro for a fully hardware-implemented binary convolutional neural network (HBCNN), focusing on accelerating edge AI applications at low weight precision. By quantizing the voltage-division results of RRAMs through inverters, the 3T2R macro provides a stable rail-to-rail output without analog-to-digital converters or sensing amplifiers. Moreover, both batch normalization and sign activation are integrated on-chip. The hybrid simulation results show that the proposed 3T2R digital macro achieves an 86.2% (95.6%) accuracy on the CIFAR-10 (MNIST) dataset, corresponding to a 4.7% (1.9%) accuracy loss compared to the software baselines, which also feature a peak energy efficiency of 51.3 TOPS/W and a minimum latency of 8 ns, realizing an energyefficient, low-latency, and robust AI processor.

Ti/HfO2-Based RRAM with Superior Thermal Stability Based on Self-Limited TiOx

HfO2-based resistive random-access memory (RRAM) with a Ti buffer layer has been extensively studied as an emerging nonvolatile memory (eNVM) candidate because of its excellent resistive switching (RS) properties and CMOS process compatibility. However, a detailed understanding of the nature of Ti thickness-dependent RS and systematic thermal degradation research about the effect of post-metallization annealing (PMA) time on oxygen vacancy distribution and RS performance still needs to be included. Herein, the impact of Ti buffer layer thickness on the RS performance of the Al/Ti/HfO2/TiN devices is first addressed. Consequently, we have proposed a simple strategy to regulate the leakage current, forming voltage, memory window, and uniformity by varying the thickness of the Ti layer. Moreover, it is found that the device with 15 nm Ti shows the minimum cycle-to-cycle variability (CCV) and device-to-device variability (DDV), good retention (105 s at 85 °C), and superior endurance (104). In addition, thermal degradation of the Al/Ti(15 nm)/HfO2/TiN devices under different PMA times at 400 °C is carried out. It is found that the leakage current increases and the forming voltage and memory window decrease with the increase in PMA time due to the thermally activated oxidation of the Ti. However, when the PMA time increases to 30 min, the Ti can no longer capture oxygen from HfO2 due to the formation of self-limited TiOx. Therefore, the device shows superior thermal stability with a PMA time of 90 min at 400 °C and no degradation of the memory window, uniformity, endurance, or retention. This work demonstrates that the Ti/HfO2-based RRAM shows superior back-end-of-line compatibility with high thermal stability up to 400 °C for over an hour.

Investigation of Single Event Effects in a Resistive RAM Memory Array by Coupling TCAD and SPICE Simulations

Investigation of Single Event Effects in a Resistive RAM Memory Array by Coupling TCAD and SPICE Simulations

An Accelerated Variable Stage Size Carry Skip Adder Realization Using 1S1R Resistive Memory

In-memory computing is the method of running computer computations completely in memory. Memristive device (e.g., 1 selector-1 resistor (1S1R)) is preferably utilized for in-memory computing with the appearance of hybrid CMOS nano-crossbar arrays. The parallelism of resistive arrays is considered in parallel adder designs to reduce the critical path delay. However, they minimize latency at the cost of hardware resources. In addition, the resistive random-access memory (ReRAM) is suffered by sneak–path problem because of the incapability for accessing a specific cell without impeding its neighbors. To tackle this issue, an analytical model of Pt/TaO[Formula: see text]/TiO2/TaO[Formula: see text]/Pt selector device is developed and it is integrated with ReRAM model for reducing the sneak path current. In this paper, a novel mapping scheme is proposed for in-memory variable stage size carry skip adder (VSS-CSKA) to harness the parallelism of 1S1R resistive memory. The VSS-CSKA considers variable block lengths and uses basic Boolean functions as skip logic to improve the speed without requiring huge area. Also, the selector device is modeled by considering the Pt/TaO[Formula: see text]/TiO2/TaO[Formula: see text]/Pt crested barrier for suppressing the sneak current in the resistive memory. The macro-model of the 1S1R resistive memory model is modeled in Verilog-A netlist and the simulations are executed on Spectre circuit simulator from Cadence Virtuoso. The proposed in-memory adder reduced the latency to [Formula: see text] for [Formula: see text]-bit addition and it consumes less energy because of the minimization of sneak currents in 1S1R.

Performance degradation and I–V model of TiO2-film-based resistive switching memory under proton irradiation

The performance degradation of a TiO2-film-based RRAM (resistive random access memory) is investigated in a proton irradiation experiment with an energy of 25 MeV. The results reveal that the fabricated Au/TiO2 film/Ti devices exhibit typical I–V of bipolar resistive switching behavior under an irradiation of 1 × 1011 protons/cm2. The low-resistance state (LRS) resistance remains nearly constant, but the high-resistance state (HRS) resistance decreases with an increasing proton fluence. The value of the HRS resistance decreases by approximately one order of magnitude when the value of proton fluence reaches 1 × 1011 protons/cm2. Moreover, the SET voltage decreases with a decreasing proton fluence, while the RESET voltage remains almost constant. Material characterization via x-ray photoelectron spectroscopy demonstrates that the decrease in SET voltage and HRS resistance is mainly caused by radiation-induced oxygen vacancies and non-lattice oxygen. Based on the Voltage Threshold Adaptive Memristor model, a mathematical model of the I–V curve which demonstrates the variation in the RRAM resistance and voltage as a function of proton irradiation is constructed. The simulation results conformed to the experimental results under different proton fluences. Our results form a fundamental guide for the study of radiation performance degradation and radiation hardening of the RRAM with oxygen vacancy conducting filament.

Effect of Hydrogen Annealing on Performances of BN-Based RRAM.

BN-based resistive random-access memory (RRAM) has emerged as a potential candidate for non-volatile memory (NVM) in aerospace applications, offering high thermal conductivity, excellent mechanical, and chemical stability, low power consumption, high density, and reliability. However, the presence of defects and trap states in BN-based RRAM can limit its performance and reliability in aerospace applications. As a result, higher set voltages of 1.4 and 1.23 V were obtained for non-annealed and nitrogen-annealed BN-based RRAM, respectively, but lower set voltages of 1.06 V were obtained for hydrogen-annealed BN-based RRAM. In addition, the hydrogen-annealed BN-based RRAM showed an on/off ratio of 100, which is 10 times higher than the non-annealed BN-based RRAM. We observed that the LRS changed to the HRS state before 10,000 s for both the non-annealed and nitrogen-annealed BN-based RRAMs. In contrast, the hydrogen-annealed BN-based RRAM showed excellent retention characteristics, with data retained up to 10,000 s.

Mapping and statistical analysis of filaments locations in amorphous HfO2 ReRAM cells

Two optical inspection techniques, Photon Emission Microscopy (PEM) and Optical Beam Induced Resistance Change (OBIRCH) laser stimulation, are used to study the forming and to map the locations of filaments in amorphous HfO2 Resistive Random-Access Memory (ReRAM) cells. The low voltage sensitivity of OBIRCH is used to investigate filament pre-forming while the high frame rate of a Silicon Intensified Charge-Coupled Device (SI-CCD) is used to dynamically study the filament after the initial forming. A Spatial Clustering Model (SCM) is developed to explain the non-Poisson spatial distribution of individual filaments measured from multiple cells of the same size.

Large-area multilayer molybdenum disulfide for 2D memristors

Resistive random access memories (RRAMs) using two-dimensional (2D) materials have delivered comparable switching performance with CMOS devices. However, devices risk short problems because of their ultra-thin body, thus yielding poorly. In this study, we realize high-yield RRAMs thanks to the synthesis of uniform large-area multilayer molybdenum disulfide by thermally decomposing ammonium tetrathiomolybdate. This top-down method has advantages over mainstream chemical vapor deposition, in which layer-by-layer epitaxy is forbidden when surface energy elevates. The resulting film surface roughness is down to 93.8 pm, and its lateral size can be scaled up to wafer scale. A yield value higher than 90% was estimated by testing 8 × 8 RRAM arrays, reaching nearly 100% in isolated devices. These devices show low operation voltages (∼1V) with low cycle-to-cycle and device-to-device variations (∼12%). We also observed stable resistive switching of multilayer films prepared at 400 °C. The large-area synthesis of uniform multilayer films makes it more feasible to use 2D semiconductors in practical RRAM technology for wafer-scale integration.

Assessment of the variability of the I-V characteristic of HfO2-based resistive switching devices and its simulation using the quasi-static memdiode model

Variability of the conduction characteristics of filamentary-type resistive switching devices or resistive RAMs (RRAMs) is a hot research topic both in academia and industry because it is currently considered one of the major showstoppers for the successful development and application of this technology. In this work, we thoroughly investigate the statistics of the cycle-to-cycle (C2C) variability observed in the experimental current–voltage (I-V) curves of HfO2-based memristive structures using the fitdistrplus package for the R language. This exploratory analysis allows us to identify which parametric probability distributions are the most suitable candidates for describing our data. This study involves graphical tools such as the density, skewness-kurtosis (S-K), and quantile–quantile (Q-Q) plots. The analysis is completed with the aid of goodness-of-fit statistics (Kolmogorov-Smirnov, Cramer-von Mises, Anderson-Darling) and criteria (Akaike’s and Bayesian). The selected distributions are incorporated into the SPICE script of the quasi-static memdiode model for resistive switching devices and used for simulating uncorrelated C2C variability. Finally, a one-way sensitivity analysis is carried out in order to test the impact of the model parameters variation in the output characteristics of the device.

Self-Powered Phase Transition Driven by Triboelectric Nanogenerator

The phase transition of SrCoO2.5 (BM-SCO) is very popular in recent reports based on its special structure. The oxygen vacancy channel of BM-SCO provides a convenient condition for phase transformation to SrCoO3-δ (PV-SCO), with prospects in smart windows, memristive devices, and magnetic recording. The traditional method for phase transition is thermal annealing, while the electric-field-controlled ionic liquid (IL) gating is also a convenient method. Herein, we use a triboelectric nanogenerator as a self-powered system, which can provide a constant voltage for the IL and achieve ion injection to induce phase transition, and this procedure proved to be reversible. X-ray diffraction scanning and high-resolution transmission electron microscope observation showed that the crystal structures of the two phases are significantly different. Besides, BM-SCO is antiferromagnetic, PV-SCO is ferromagnetic, and the optical transmittance also changes during the phase transition. As for the applications of this film, we used IL gating to modulate the resistive switching behavior, which shows a change between high-resistance state and low-resistance state during the phase transition and can be used in resistive random access memory devices.