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Perovskite structures have attracted extensive attention in microelectromechanical systems and nanoelectromechanical systems devices due to the high piezoelectric response and the low dielectric constant. The piezoelectric and dielectric properties of the tetragonal BiFeO3/BaTiO3 superlattices grown along the c-axis direction are investigated using density functional theory (DFT) and density functional perturbation theory. The calculated results demonstrate that the (BiFeO3)n/(BaTiO3)n (n = 1–5) superlattices exhibit a profoundly increased piezoelectric response compared to their bulk structures. The (BiFeO3)2/(BaTiO3)2 possesses the highest piezoelectric d33 of 697 pC/N among known lead-free perovskite systems. Furthermore, the (BiFeO3)2/(BaTiO3)2 superlattice possesses a low dielectric ɛ33, and its d33 at 2% tensile strain is 16 times larger than that of an unstrained equilibrium structure. This demonstrates that biaxial tensile strain significantly enhances the piezoelectric response. Combining the special quasirandom structure method with DFT, the structures of a 0.5BiFeO3–0.5BaTiO3 solid solution are predicted, and its calculated d33 is 58 pC/N, which is much smaller than that of a (BiFeO3)2/(BaTiO3)2 superlattice. The results suggest that the (BiFeO3)2/(BaTiO3)2 superlattice might be a potential candidate for nonvolatile random access memory, transducers and actuators, and nanoscale electronic devices. [ABSTRACT FROM AUTHOR]

Electrically induced resistive switching (RS) effects have been proposed as the basis for future non-volatile memories. In this work, 9 nm-thick BiFeO3 (BFO) epitaxial thin films were deposited on (001)-oriented SrTiO3 substrates by pulsed laser deposition and their resistive switching (RS) behaviors were investigated. A large resistive switching with ON/OFF ratio of ∼106 is observed, surpassing the performance of most resistive random access memories ever reported. The conducting filament is proposed to dominate the RS behavior in the positive voltage region, while the modulation of ferroelectric polarization is suggested to play a significant role in the negative voltage region. Our study significantly deepens the understanding of the physical origin of RS and could provide a reference for designing high-performance memories and memristors based on ultrathin ferroelectric films. [ABSTRACT FROM AUTHOR]

Resistive Random Access Memory (RRAM) is considered one of the most promising candidates for big data storage. By using atomic layer deposition and magnetron sputtering, HfO2 thin films were prepared on ITO first, which exhibited good resistive switching (RS) characteristics in the structure of Ag/HfO2/ITO. By analyzing the RS mechanism, it is found that both metal conductive filaments and oxygen vacancy conductive filaments coexisted and Sn ion in ITO can influence the retention of RRAM. Furthermore, a device in the structure of Ag/HfO2/Pt was proposed and prepared, which exhibited excellent RS characteristics, including an ultrahigh switching ratio averaging up to 108 and low operating voltage. It is concluded that the difference in the work function between the top and bottom electrodes contributes to improving the switching ratio, reducing the operating voltage. In addition, the Ag/HfO2/Pt device is similar to the Ag/HfO2-based threshold switching selector in the structure and in characteristics of high switching ratio, besides non-volatile memory. Hence, the device is functionally equivalent to the combination of an RRAM and a threshold switching selector. It is the potential way to replace the conventional 1S1R structure memory. [ABSTRACT FROM AUTHOR]

This study presents findings indicating that the ferroelectric tunnel junction (FTJ) or resistive random-access memory (RRAM) in one cell can be intentionally selected depending on the application. The HfAlO film annealed at 700 °C shows stable FTJ characteristics and can be converted into RRAM by forming a conductive filament inside the same cell, that is, the process of intentionally forming a conductive filament is the result of defect generation and redistribution, and applying compliance current prior to a hard breakdown event of the dielectric film enables subsequent RRAM operation. The converted RRAM demonstrated good memory performance. Through current–voltage fitting, it was confirmed that the two resistance states of the FTJ and RRAM had different transport mechanisms. In the RRAM, the 1/f noise power of the high-resistance state (HRS) was about ten times higher than that of the low-resistance state (LRS). This is because the noise components increase due to the additional current paths in the HRS. The 1/f noise power according to resistance states in the FTJ was exactly the opposite result from the case of the RRAM. This is because the noise component due to the Poole–Frenkel emission is added to the noise component due to the tunneling current in the LRS. In addition, we confirmed the potentiation and depression characteristics of the two devices and further evaluated the accuracy of pattern recognition through a simulation by considering a dataset from the Modified National Institute of Standards and Technology. [ABSTRACT FROM AUTHOR]

Non-volatile memory devices have been getting significant attention from researchers worldwide in recent years due to their application in resistive random access memory and neuromorphic computing. Here, we have fabricated polyvinyl alcohol-graphene oxide (PVA-GO) composite as an active material for the resistive switching with different concentrations of GO (0.0, 0.1, 0.2, 0.3, 0.4, and 0.5 wt. % GO solution) dispersed in 5 wt. % PVA matrix in a 2:1 volume ratio. We demonstrate the non-volatile forming free resistive switching properties of Ag/PVA-GO/Ag devices. Structural properties of PVA-GO composites are established from the x-ray diffraction pattern, which indicates the complete dispersion of GO inside the PVA matrix. The Ag/PVA-GO-0.1 wt. %/Ag device shows better bipolar resistive switching at VSET ∼ 0.4 V and VRESET at ∼−0.8 V. This device indicates well-resolved two distinct states at a read voltage of 0.1 V in endurance and retention measurements. The fabricated device switches successfully tested for 2.5 × 103 cycles and retains its state for 3.36 × 103 s without any observable degradation. Furthermore, the non-volatile retention property was modeled using time series analysis. For this, Holt–Winter's exponential smoothing technique was utilized. Additionally, the charge–flux linkage characteristic shows the double-valued function, and time domain–charge and time domain–flux show asymmetric behaviors. The electrical conduction mechanism exhibits ohmic behavior in the entire region of the low resistance state and the lower voltage region of the high resistance state. In the high-voltage region of the high resistance state, the space charge-limited conduction mechanism is observed. The resistive switching behavior is explained with the help of an appropriate model. [ABSTRACT FROM AUTHOR]

Recent advancements in the fabrication of Resistive Random Access Memory (ReRAM) devices have led to the development of large-scale crossbar structures. In-memory computing architectures relying on ReRAM crossbars aim to mitigate the processor-memory bottleneck that exists with current complementary metal-oxide semiconductor technology. With this motivation, several synthesis and mapping approaches focusing on the realizations of Boolean functions in the ReRAM crossbars have been proposed earlier. Thus far, the verification of the designs realized on ReRAM crossbars is done either through manual inspection or using simulation-based approaches. Since manual inspections and simulation-based approaches are limited to smaller designs, they cannot be applied to the verification of complex designs on large-scale ReRAM crossbars. Motivated by this, we propose, for the first time, an automatic equivalence checking flow that determines the equivalence between the original function specification (e.g., Majority-inverter Graph) and the crossbar micro-operations file formats. We consider two crossbar structures, zero-transistor, one-memristor (0T1R) and one-transistor, one-memristor (1T1R) to implement the micro-operations. While the micro-operations file format exists for 0T1R crossbar structures, no representations for micro-operations to be executed in 1T1R crossbars exist yet. In this work, we introduce the micro-operation file format for 1T1R crossbar structures to efficiently represent the micro-operations as ReRAM crossbar netlists. Afterwards, we introduce two intermediate data structures, ReRAM Sequence Graph for 0T1R crossbars (ReSG-0T1R) and for 1T1R crossbars (ReSG-1T1R), that are derived from the 0T1R and 1T1R crossbar micro-operations file formats, respectively. These ReSGs are then translated into Boolean Satisfiability (SAT) formula, and then the verification is done by checking the generated SAT formulae against the golden functional specification (represented in Verilog) using Z3 Satisfiability solver. Experimental evaluations confirm the effectiveness of the proposed verification methodology on MCNC and ISCAS benchmarks. [ABSTRACT FROM AUTHOR]

With the advent of the internet era, wearable electronic devices and flexible electronic technology are rapidly emerging, and new resistive random access memory (RRAM) based on flexible substrates have thus gained extensive attention. Here, the flexible Bi 0·95 Er 0·05 FeO 3 /ZnO/La 0·66 Sr 0·34 MnO 3 (BEFO/ZnO/LSMO) heterostructure is prepared on Mica substrate using the sol-gel method. The high resistance state (HRS) to low resistance state (LRS) ratio consistently surpasses 102 under the flat and the 15 mm bending radius state. There is no significant change in the resistive switching (RS) behavior after 30 I–V curve cycles and 103 write/erase operations on the BEFO/ZnO/LSMO heterostructure, which reflects excellent stability. In addition, the device still works properly at 175 °C for dynamic conversion between HRS and LRS. The observed RS behavior in the BEFO/ZnO/LSMO heterostructure is attributed to ion migration and alterations in the potential barrier height at the interfaces. These results suggest that the BEFO/ZnO/LSMO heterostructure based on a flexible Mica substrate plays a crucial role in the development of next-generation portable and flexible electronic systems due to its excellent flexibility and thermal stability. [ABSTRACT FROM AUTHOR]

Low‐band gap semiconductor polymer materials play a crucial role in the field of organic optoelectronics. In this context, a series of low‐band gap polymers containing thienopyrazine as the acceptor and cyclofluorene–bithiophene as the donor were synthesized and utilized in resistive random access memory (RRAM) devices. These memory devices consistently exhibit nonvolatile flash memory behavior. Remarkably, all three polymers demonstrate stability even after 1000 cycles without significant fluctuations. Notably, the three polymer‐based devices also exhibit excellent ternary memory performance, with current ratios of 1:102.8:104, 1:101.3:103.9, and 1:101.8:103.6. Furthermore, the photoelectric properties of the three polymers and the conduction mechanisms of the memory devices were thoroughly discussed. [ABSTRACT FROM AUTHOR]

Resistive Random Access Memory (RRAM) has gained considerable momentum due to its non-volatility and energy efficiency. Material and device scientists have been proposing novel material stacks that can mimic the "ideal memristor" which can deliver performance, energy efficiency, reliability and accuracy. However, designing RRAM-based systems is challenging. Engineering a new material stack, designing a device, and experimenting takes significant time for material and device researchers. Furthermore, the acceptability of the device is ultimately decided at the system level. We see a gap here where there is a need for facilitating material and device researchers with a "push button" modeling framework that allows to evaluate the efficacy of the device at system level during early device design stages. Speed, accuracy, and adaptability are the fundamental requirements of this modelling framework. In this paper, we propose a digital twin (DT)-like modeling framework that automatically creates RRAM device models from device measurement data. Furthermore, the model incorporates the peripheral circuit to ensure accurate energy and performance evaluations. We demonstrate the DT generation and DT usage for multiple RRAM technologies and applications and illustrate the achieved performance of our GPU implementation. We conclude with the application of our modeling approach to measurement data from two distinct fabricated devices, validating its effectiveness in a neural network processing an Electrocardiogram (ECG) dataset and incorporating Fault Aware Training (FAT). [ABSTRACT FROM AUTHOR]

Recently, metal–organic frameworks (MOFs) have gained attention in the field of electronics owing to their capability to tune their electrical characteristics. However, conventional methods for synthesizing MOFs pose challenges for their integration into electronic devices because of their long synthesis times and complex transfer steps. In this study, for the first time, lasing‐assisted synthesis (LAS) is used to rapidly and directly synthesize MOFs. These are applied to resistive random access memory (RRAM) devices. Using the LAS method, Cu(BDC) and Cu(BTC) are synthesized in a remarkably short time (≈5 min) and formed directly on metal substrates as thin films. This simplified their integration into RRAMs. The Cu(BDC)‐ and Cu(BTC)‐based RRAMs are evaluated for their potential in memory and neuromorphic applications. Both devices demonstrated nonvolatile memory capabilities with a remarkable data retention time of 104 s and long‐term plasticity (LTP) in response to voltage stimuli. However, the suitability of each device for a specific application varies depending on the type of MOFs used. The Cu(BTC)‐based RRAM is more suitable for memory applications because of its higher on/off ratio, longer endurance, and more data storage capacity. Conversely, Cu(BDC)‐based RRAM is highly effective in neural network simulation, achieving higher classification accuracy. [ABSTRACT FROM AUTHOR]

Summary: Multivalued logic (MVL) offers higher information density as compared with binary logic systems for an equal number of digits. In a ternary microprocessor, memory access is the most time‐ and power‐consuming action. In order to design a ternary computer, the possibilities for designing ternary encoders and decoders must be examined. This paper presents the designs of 9:2 unbalanced ternary encoder (TENC) and 2:9 unbalanced ternary line address decoder (TDEC) based on novel designs of standard ternary inverter (STI), ternary NAND (TNAND), and ternary NOR (TNOR) logic gates using resistive random‐access memory (RRAM) and carbon nanotube field effect transistor (CNTFET) technology. The simulation results indicate an improvement in performance parameters such as power consumption, delay, power–delay product (PDP), and component count of the proposed circuits in comparison with the different existing counterparts. Moreover, a detailed analysis is carried out on the impact of different process, voltage, and temperature (PVT) variations on the performance metrics, including propagation delay, power consumption, and PDP of the 9:2 unbalanced ternary encoder and 2:9 unbalanced ternary line address decoder. The proposed ternary encoder circuit shows an improvement of 62% in delay, 71% in power consumption, 88.6% improvement in PDP, and 53% reduction in CNTFET count, whereas the ternary decoder circuit exhibits an improvement of 6.6% and 94% in delay and 43% and 94% improvement in power consumption, respectively. Moreover, the CNTFET count is reduced by 16.07% and 46%. [ABSTRACT FROM AUTHOR]

Resistive random access memory has attracted a great deal of attention due to its simple structure and high storage density. With the development of doping technology for RRAMs, it faces some problems in the resistive switching parament, such as high switching voltage, poor stability and uniformity. In this work, two different nanoparticle doping modes of RRAM were prepared, thin layer doping with appropriate density achieved better RS stability and uniformity for low power consumption devices. A circuit model is presented to analyze the conductive path in the resistive layer to fully account for why the thin layer doping with better performance. The uniform nanoparticle thin layer doping helps to connect more bypass channels as nodes, which increases the conductivity. Additionally, we investigated the effect of Ag nanoparticle density on the resistive characteristics, which experimentally verified the proposed circuit model. The combination of the proposed circuit model with the doping technology will significantly facilitate the development of RRAM. [ABSTRACT FROM AUTHOR]

In the era of big data, traditional computing architectures face limitations in handling vast amounts of data owing to the separate processing and memory units, thus causing bottlenecks and high‐energy consumption. Inspired by the human brain's information exchange mechanism, neuromorphic computing offers a promising solution. Resistive random access memory devices, particularly those with bilayer structures like Pt/TaOx/TiOx/TiN, show potential for neuromorphic computing owing to their simple design, low‐power consumption, and compatibility with existing technology. This study investigates the synaptic applications of Pt/TaOx/TiOx/TiN devices for neuromorphic computing. The unique coexistence of nonfilamentary and filamentary switching in the Pt/TaOx/TiOx/TiN device enables the realization of reservoir computing and the functions of artificial nociceptors and synapses. Additionally, the linkage between artificial nociceptors and synapses is examined based on injury‐enhanced spike‐time‐dependent plasticity paradigms. This study underscores the Pt/TaOx/TiOx/TiN device's potential in neuromorphic computing, providing a framework for simulating nociceptors, synapses, and learning principles. [ABSTRACT FROM AUTHOR]

Reservoir computing (RC) possesses a simple architecture and high energy efficiency for time‐series data analysis through machine learning algorithms. To date, RC has evolved into several innovative variants. The next generation reservoir computing (NGRC) variant, founded on nonlinear vector autoregression (NVAR) distinguishes itself due to its fewer hyperparameters and independence from physical random connection matrices, while yielding comparable results. However, NGRC networks struggle with massive Kronecker product calculations and matrix‐vector multiplications within the read out layer, leading to substantial efficiency challenges for traditional von Neumann architectures. In this work, a hybrid digital‐analog hardware system tailored for NGRC is developed. The digital part is a Kronecker product calculation unit with data filtering, which realizes transformation of nonlinear vector of the input linear vector. For matrix‐vector multiplication, a computing‐in‐memory architecture based on resistive random access memory array offers an energy‐efficient hardware solution, which markedly reduces data transfer and greatly improve computational parallelism and energy efficiency. The predictive capabilities of this hybrid NGRC system are validated through the Lorenz63 model, achieving a normalized root mean square error (NRMSE) of 0.00098 and an energy efficiency of 19.42TOPS W−1. [ABSTRACT FROM AUTHOR]

Currently, 2D nanomaterials-based resistive random access memory (RRAMs) are explored on account of their tunable material properties enabling fabrication of low power and flexible RRAM devices. In this work, hybrid MoS2–GO based active layer RRAM devices are investigated. A facile hydrothermal co-synthesis approach is used to obtain the hybrid materials and a cost-effective spin coating method adopted for the fabrication of Ag/MoS2–GO/ITO RRAM devices. The performance of the fabricated hybrid active layer RRAM device is analysed with respect to change in material properties of the synthesized hybrid material. The progressive addition of 0.5, 1.5, 2.5 and 4.5 weight % of GO to MoS2, results in a hybrid active layer with higher intermolecular interaction, in the case of Ag/MoS2–GO4.5/ITO RRAM device, resulting in a unipolar resistive switching RRAM behavior with low SET voltage of 1.37 V and high I on/ I off of 200 with multilevel resistance states. A space charge limited conduction mechanism is obtained during switching, which may be attributed to the trap states present due to functional groups of GO. The increased number of conduction pathways on account of both Ag+ ions and oxygen vacancies (Vo2+), participating in the formation of conducting filament, results in higher I on/ I off. This is the first report of unipolar Ag/MoS2–GO/ITO RRAM devices, which are particularly important in realizing high density crossbar memories for neuromorphic and in-memory computing as well as enabling flexible 2D nanomaterials-based memristor applications. [ABSTRACT FROM AUTHOR]

The Al/Sm 2 Ce 2 O 7 /ITO (Al/SCO/ITO) structure resistive random access memory (RRAM) was prepared using RF magnetron sputtering, and its resistive switching (RS) behaviors were systematically investigated. The memory exhibited a high switching cycle of 1031, along with set/reset voltages of −1.55/0.53 V and a R on /R off ratio exceeding 102. This high performance can be attributed to the presence of intrinsic oxygen vacancies in the defective fluorite structure, accounting for 34.44 % of the vacancies. This vacancy percentage can be increased to 41.97 % by simultaneously transforming Ce4+ to Ce3+ ions through annealing treatment, thereby releasing additional oxygen vacancies. Despite this increase, there remains a sufficient window (R on /R off > 10) to distinguish between the high-resistance state (HRS) and low-resistance state (LRS) of the SCO device. Furthermore, the device, when subjected to post-metal annealing (PMA), exhibits a high endurance of up to 1815 cycles, a set/reset voltages of −2.19/1.31 V, a R on /R off ratio of >10, and a retention time of over 104 s at 25 °C and 85 °C. However, it is important to note that PMA-treated device requires a higher forming voltage due to the formation of a thicker AlO x layer. This study highlights the potential of Al/SCO/ITO memory devices for RRAM applications. [ABSTRACT FROM AUTHOR]

A nonlinear system, exhibiting a unique asymptotic behaviour, while being continuously subject to a stimulus from a certain class, is said to suffer from fading memory. This interesting phenomenon was first uncovered in a non-volatile tantalum oxide-based memristor from Hewlett Packard Labs back in 2016 out of a deep numerical investigation of a predictive mathematical description, known as the Strachan model, later corroborated by experimental validation. It was then found out that fading memory is ubiquitous in non-volatile resistance switching memories. A nonlinear system may however also exhibit a local form of fading memory, in case, under an excitation from a given family, it may approach one of a number of distinct attractors, depending upon the initial condition. A recent bifurcation study of the Strachan model revealed how, under specific train stimuli, composed of two square pulses of opposite polarity per cycle, the simplest form of local fading memory affects the transient dynamics of the aforementioned Resistive Random Access Memory cell, which, would asymptotically act as a bistable oscillator. In this manuscript we propose an analytical methodology, based on the application of analysis tools from Nonlinear System Theory to the Strachan model, to craft the properties of a generalised pulse train stimulus in such a way to induce the emergence of complex local fading memory effects in the nano-device, which would consequently display an interesting tuneable multistable oscillatory response, around desired resistance states. The last part of the manuscript discusses a case study, shedding light on a potential application of the local history erase effects, induced in the device via pulse train stimulation, for compensating the unwanted yet unavoidable drifts in its resistance state under power off conditions. [ABSTRACT FROM AUTHOR]

Owing to the increasing demands of high-density data storage double-halide perovskite-based resistive random access memory (RRAM) have recently emerged as a promising candidate in the forefront of next-generation optoelectronic memory applications. The ionic motion-based quick switching is the key feature of this kind of material, which plays a significant role in resistive switching (RS) applications. Recently, lead-free tin-based double-halide perovskites have been considered as favourable material due to their superior stability, functionality and eco-friendly nature. Here, we report the synthesis of cesium tin (IV) iodide (Cs2SnI6) perovskites. X-ray diffraction (XRD) pattern of the as-synthesized perovskite confirms the formation of Cs2SnI6 material. The crystallographic data corroborate the formation of a pure cubic phase, free of any other phase at room temperature. We also studied optical properties of the sample by using the ultraviolet–visible (UV) spectra and photoluminescence (PL) spectra. A broadband at around 580 nm is observed in the UV−Vis absorption spectra. The optical band gap of the sample is found to be 1.68 eV. Cs2SnI6 perovskite exhibited intense PL emission at ~540 nm. In this work, to fabricate a flexible Al/Cs2SnI6/ITO-PET memory device, we used Cs2SnI6 film as a switching layer and the device exhibits bipolar RS characteristics. [ABSTRACT FROM AUTHOR]

Memristive devices with both electrically and optically induced synaptic dynamic behaviors will be crucial to the accomplishment of brain-inspired neuromorphic computing systems, in which the resistive materials and device architectures are two of the most important cornerstones, but still under challenge. Herein, kuramite Cu3SnS4 is newly introduced into poly-methacrylate as the switching medium to construct memristive devices, and the expected high-performance bio-mimicry of diverse optoelectronic synaptic plasticity is demonstrated. In addition to the excellent basic performances, such as stable bipolar resistive switching with On/Off ratio of ∼486, Set/Reset voltage of ∼−0.88/+0.96 V, and good retention feature of up to 104 s, the new designs of memristors possess not only the multi-level controllable resistive-switching memory property but also the capability of mimicking optoelectronic synaptic plasticity, including electrically and visible/near-infrared light-induced excitatory postsynaptic currents, short-/long-term memory, spike-timing-dependent plasticity, long-term plasticity/depression, short-term plasticity, paired-pulse facilitation, and "learning-forgetting-learning" behavior as well. Predictably, as a new class of switching medium material, such proposed kuramite-based artificial optoelectronic synaptic device has great potential to be applied to construct neuromorphic architectures in simulating human brain functions. [ABSTRACT FROM AUTHOR]

In this study, a two-dimensional electron gas (2DEG), which is a conductive layer formed at the interface of Al2O3 and TiO2, was used as an electrode for resistive random access memory (RRAM) and implemented in a cell size down to 30 nm. For an RRAM device comprising W/2DEG/TiO2/W, we confirmed that the dominant switching mechanism changed from interfacial to filamentary as the cell size decreased from 500 nm to 30 nm. Through analyses of changes in forming characteristics and conduction mechanisms in the low resistive state depending on the cell size, it was identified that the 2DEG acted as an oxygen-scavenging layer of TiO2 during the resistive switching process. By comparing the switching characteristics of RRAM devices with and without 2DEG for a 30 nm cell size, we confirmed that a high-performance 2DEG RRAM was realized, with highly uniform current–voltage characteristics, a low operating voltage (∼1 V), and a high on/off ratio (>102). Finally, the applicability of the proposed device to a crossbar array was validated by evaluating 1S1R operation with an NbO2-based selector. Considering the improved switching uniformity, the 2DEG RRAM shows promise for high-density memory applications. [ABSTRACT FROM AUTHOR]

Recent developments in flexible memories, neuromorphic computing, and other fields have generated a lot of interest in memristive devices based on two-dimensional materials. Due to its capacity for data storage and quick working speed, two-terminal non-volatile memory devices are more efficient and incredible. The need for memory that is flexible, low-powered, and ultra-thin is rising as a result of the rapidly developing fields of healthcare, wearable electronics, and the Internet of Things. The current study observes the effects of pure hexagonal boron nitride (hBN) inclusion on the resistive switching properties of tin oxide (SnO2)-based flexible resistive memory device, which was synthesized by a straightforward hydrothermal method. The synthesized nanocomposite powders were characterized by X-ray diffraction and Raman spectroscopic methods. A pure SnO2 and SnO2-hBN (5, 7, and 10 wt.%) nanocomposite film was spin-coated on an ITO-PET flexible substrate to create the memory device. To finish the memory device, thermal evaporation was used to create Al top electrodes with a thickness of less than 100 nm. After comparing the resistive switching performance of the created devices, it was found that the memory device using 7% hBN in SnO2 had a higher ION/IOFF ratio of 1000, whereas the samples including 5 and 10 wt.% hBN had ratios of 96 and 10, respectively. In addition, the retention and endurance behaviour of the fabricated 7%SnO2-hBN composite film-based device was studied for up to 1.5 × 104 and 100 cycles, respectively, with no degradation in the memory window. It has been noted that the current metal oxide–2D hybrid nanocomposite is a promising option for low-power, flexible, and enduring non-volatile memory applications. [ABSTRACT FROM AUTHOR]

Resistive switching memory devices based on organic materials are intriguing. These devices are biodegradable and nontoxic to living organisms. In this work, using euphorbia cotinifolia plant extract was investigated for its applicability as an active layer of a resistive switching memory device consisting of silver top electrode and indium-doped tin oxide bottom electrode. This study selected Euphorbia cotinifolia because it is a common, prolific and simple-to-grow plant in many South African homes. When the euphorbia cotinifolia plant is broken, an extract resembling milk is emitted. This extract was collected directly onto the bottom electrode, which was then dried at ambient temperature. For top electrode, silver paste was applied. The entire fabrication process was devoid of heat and electricity. The fabricated device showed impressive properties such as memory hysteresis with ON/OFF = 5 , endurance of over 3 2 write/erase cycles, and an impressive retention of ≥ 1 0 3   s. Therefore, this system may be a candidate for a nonvolatile and disposable memory device. [ABSTRACT FROM AUTHOR]

Resistive Random Access Memory (ReRAM) devices are being regarded as very promising choices for the future of non-volatile memory technology. The subject comprises crucial components like as material engineering, device architectural optimization, switching mechanisms, and improvements in reliability. This study examines the resistive switching capabilities of a device made from a ZrO2-CNT nanocomposite. The device was constructed utilizing a trilayer structure consisting of FTO/ZrO2-CNT/Ag, with the ZrO2-CNT film being fabricated by the spray coating technique. Incorporating 1wt% CNT into the ZrO2 matrix reduces the bias voltage needed for resistive switching and approximately doubles the resistance ratio between HRS and LRS. The use of higher weight percentages of carbon nanotubes (CNT) negatively impacts the switching properties. The temperature dependence of resistance of ZrO2 and ZrO2-1wt% CNT devices reveals that in ZrO2, O2 vacancies align to create conducting filaments. On the other hand, in the ZrO2-CNT device, both vacancies of O2 atoms and CNTs contribute to the production of conducting filaments. Inclusion of higher weight percentages of carbon nanotubes (CNT) leads to the formation of permanent conduction paths, which are electrical shorts and results in the loss of the switching capability. [ABSTRACT FROM AUTHOR]

Currently, halide perovskites with exceptional properties are the most suitable candidates for RRAM devices. In the present study, a comprehensive investigation of structural, mechanical, optoelectronic and thermoelectric properties of Cs2BSnX6 (B = Mg, Cu; X = Cl, Br, I) has been conducted for the potential application in resistive switching random access memory (RRAM) devices. Mechanical properties display that studied perovskites exhibit mechanical stability and ductile behavior with an anisotropic character. A comparison of band structure using PBE-GGA, and TB-mBJ was also made. The band structure with TB-mBJ investigation disclosed that Cs2MgSnCl6, Cs2MgSnBr6, Cs2MgSnI6, Cs2CuSnCl6, Cs2CuSnBr6, and Cs2CuSnI6 have indirect bandgaps with value of 3.64 eV, 2.87 eV, 2.05 eV, 2.30 eV, 1.50 eV and 0.90 eV respectively. These values are found to be slightly more than PBE-GGA approximations. The optical investigation illustrates that Cs2CuSnI6 can absorb a wide range of electromagnetic radiation wavelengths from ultraviolet to infrared, making it the optimal candidate for RRAM devices. Thermoelectric properties show electrical conductivity values of the 1.65 × 1019, 1.61 × 1019, 1.58 × 1019, 1.95 × 1019, 2.35 × 1019, 2.40 × 1019 (Ω.m.s)−1 for Cs2MgSnCl6, Cs2MgSnBr6, Cs2MgSnI6, Cs2CuSnCl6, Cs2CuSnBr6, and Cs2CuSnI6 respectively at 1500 K. Furthermore, the calculated optical and thermoelectric properties of Cs2BSnX6 (B = Mg, Cu; X = Cl, Br, I) compared with previously studied Cs2CaSnX6 (X = Cl, Br, I), disclosing that Mg and Cu-based perovskites display better optical and thermoelectric properties. The vital findings of this study express that Cs2CuSnI6 is the most appropriate candidate for less-energy consuming RRAM devices among all studied perovskites. [ABSTRACT FROM AUTHOR]

Resistive Random Access Memory (RRAM) is a prominent contender in the realm of emerging non-volatile memory technologies owing to its numerous advantages like simple structure, low power usage, high speed, and exceptional scalability. In the current study, RRAM device was fabricated using tantalum oxide (TaOx) as a resistive switching insulating oxide layer, which was sandwiched between two conducting electrodes, namely Ag and ITO. RF magnetron sputtering was used to deposit the insulating layer of TaOx at room temperature on the ITO substrate. Further, X-ray reflectivity and hard X-ray photoelectron spectroscopy analyses were conducted to find the thickness, roughness, and elemental composition of the deposited TaOx films. Using a semiconductor analyzer, the RRAM cell's first DC switching characteristics were determined. The data acquired exhibited a resistance ratio (RHRS/RLRS) of 120 showing a good distinction between the two-resistance states. Additionally, a thorough investigation was conducted to examine the electrical conduction mechanism occurring inside the fabricated RRAM device. [ABSTRACT FROM AUTHOR]

To reduce the leakage and power consumption of metal–oxide resistive random access memory (RRAM), we propose and fabricate a cold-electrode (CE) RRAM (CE-RRAM) by extending the mechanism of cold-source FETs. First-principles calculations show that the n-Si/TiN composite CE can filter electrons with energy within the Si bandgap, which contribute to leakage current. A n-Si/TiN/HfOx/Pt CE-RRAM with low leakage current and large on/off current ratio was designed and fabricated. Comparative analysis with conventional RRAM demonstrates over a 100-fold reduction in leakage current in a high resistance state and a tenfold improvement in the Ion/Ioff ratio. Additionally, the CE-RRAM effectively suppresses the overshoot effect in terminal I–V characteristics and exhibits good endurance, maintaining a 100 Ion/Ioff ratio after 104 cycles. Furthermore, even after 104 s at 100 °C, the state remains unchanged. Moreover, the CE-RRAM demonstrates its multi-level storage capability. [ABSTRACT FROM AUTHOR]

This study addresses the demand for more efficient logic circuits by focusing on reducing area, power consumption, and delay. As conventional CMOS technology faces scaling and efficiency limitations, integrating emerging memory technologies like Resistive Random Access Memory (RRAM), also known as memristor, offers a promising solution. By replacing conventional PMOS transistors with memristors in CMOS logic, the study leverages the high-off resistance and low-voltage operation of RRAM devices to develop more compact and energy-efficient circuits. The proposed RRAM device is a Metal-Insulator-Metal structure fabricated with Platinum electrodes, Aluminum Oxide and Tantalum Pentoxide insulator layers. It operates with set and reset voltages of 1 V, and its current-voltage characteristics were theoretically modeled using the VTEAM model. A Memristor-based Imply and N-Imply(MINI) logic approach is introduced for XNOR, XOR and 2X1 Multiplexer designs and compared with three other hybrid NMOS-Memristor logics. Implementing 36 different 1-bit adder circuits in the Cadence Virtuoso 45 nm technology, the study evaluates area efficiency, power consumption, and delay. Results show that the memristor-based MINI logic designs are more area and powerefficient than traditional CMOS-based full adders and various optimized CMOS and memristor-based logics. This research underscores the potential of the proposed RRAM device integration in advanced hybrid logic design. [ABSTRACT FROM AUTHOR]

In recent advancements, the traditional von Neumann architecture has been challenged by the computational needs of AI. This is due to its high power and data transfer costs. As a solution, the computing-in-memory (CIM) architecture, which combines storage and computation, has gained attention for its superior computational power and energy efficiency. Within CIM, using resistive random access memory (RRAM) arrays, the readout circuit, which converts analog outputs from multiply–accumulate operations into digital signals, faces limitations due to its area and power consumption. There are mainly two types of CIM readout circuits for analog types: the traditional ADC type and the non-traditional type. This paper presents two types of readout circuit designs. The first is a low-power, compact successive approximation register (SAR) analog-to-digital converter (ADC) readout circuit. The core circuit is an 8-bit SAR ADC operating at 70 MS/s. It incorporates a linearity-improved bootstrapped switch to minimize leakage and enhance linearity, whose spurious-free dynamic range (SFDR) has been improved by 10.1 dB from 76.78 dB to 86.88 dB, and whose signal-to-noise and distortion ratio (SNDR) has increased by 4.56 dB from 75.13 dB to 79.69 dB. The delay of a transconductance-enhanced dynamic comparator is reduced from 184 ps to 149 ps, presenting a performance improvement of approximately 20%. Concurrently, the energy consumption decreased from 178 μm to 132 μm, attaining an improvement of roughly 26%. A "sandwich" capacitor structure is used that reduces the overall area of the layout. After layout and post-simulation, this circuit occupies only 49.6 μm × 51.5 μm, consumes 553 μW power, has a SINAD of 46.22 dB, and has an SFDR of 57.21 dB. The second is a current controlled oscillator (CCO)-type readout circuit, which comprises a CCO oscillator with low process-sensitivity. The readout circuit also utilizes an op-amp and current mirrors for a negative feedback loop, ensuring a constant voltage across the RRAM arrays. The frequency generated through the CCO is controlled by the current, and quantified by a counter, supporting different weights quantification per ReRAM column without additional digital weighting. This circuit achieves 95-level resolution, 5.2 μs delay, and an average consumption of 183.1 μW. A comparative analysis highlights that traditional ADC readout circuits offer high resolution and speed but are limited by their high power and area costs, often overshadowing CIM arrays' benefits. Thus, for applications with more lenient resolution and speed requirements, non-traditional readout circuits present considerable advantages. [ABSTRACT FROM AUTHOR]

Resistive random-access memories have attracted significant attention in memory applications, while the physical mechanism behind their resistive switching behavior is still unclear. As a key issue, the migration of oxygen vacancies (VO) directly influences the performance of devices in the formation and rupture of conductive filaments (CFs). In this work, the distance of VO migration is performed as electric field dependent and affects the performance of the device. Sufficient distance of VO migration is essential for formation and rupture of CF completely, leading to, ideally, SET/RESET transition of the device. Based on this mechanism, an enhanced electric field is designed that optimizes the stability of the device significantly, which is demonstrated to be a promising approach to optimize the properties of the device. The investigation is helpful for guiding the design of device structures. [ABSTRACT FROM AUTHOR]

A rapid and effective strategy has been devised for the swift development of a Zn(II)-ion-based supramolecular metallohydrogel, termed Zn@PEH, using pentaethylenehexamine as a low molecular weight gelator. This process occurs in an aqueous medium at room temperature and atmospheric pressure. The mechanical strength of the synthesized Zn@PEH metallohydrogel has been assessed through rheological analysis, considering angular frequency and oscillator stress dependencies. Notably, the Zn@PEH metallohydrogel exhibits exceptional self-healing abilities and can bear substantial loads, which have been characterized through thixotropic analysis. Additionally, this metallohydrogel displays injectable properties. The structural arrangement resembling pebbles within the hierarchical network of the supramolecular Zn@PEH metallohydrogel has been explored using FESEM and TEM measurements. EDX elemental mapping has confirmed the primary chemical constituents of the metallohydrogel. The formation mechanism of the metallohydrogel has been analyzed via FT-IR spectroscopy. Furthermore, zinc(II) metallohydrogel (Zn@PEH)-based Schottky diode structure has been fabricated in a lateral metal–semiconductor-metal configuration and it's charge transport behavior has also been studied. Notably, the zinc(II) metallohydrogel-based resistive random access memory (RRAM) device (Zn@PEH) demonstrates bipolar resistive switching behavior at room temperature. This RRAM device showcases remarkable switching endurance over 1000 consecutive cycles and a high ON/OFF ratio of approximately 270. Further, 2 × 2 crossbar array of the RRAM devices were designed to demonstrate OR and NOT logic circuit operations, which can be extended for performing higher order computing operations. These structures hold promise for applications in non-volatile memory design, neuromorphic and in-memory computing, flexible electronics, and optoelectronic devices due to their straightforward fabrication process, robust resistive switching behavior, and overall system stability. [ABSTRACT FROM AUTHOR]

The spinel Ce-doping MnCo 2 O 4 (MCO) thin films were fabricated on Pt/Ti/SiO 2 /Si substrates for resistive switching (RS) layer using sol-gel spin-coating deposition method. The Pt/Mn 0.925 Ce 0.075 Co 2 O 4 /Pt (Pt/7.5MCC/Pt) device exhibits stabler and superior bipolar RS performance, the concentrated distribution of Reset/Set voltage as low as – 0.62/1.175 V, good cycle endurance and excellent time retention capability. The improvement in the RS performance of the Pt/7.5MCC/Pt devices can be attributable to the effective limitation of the random formation and rupture of conductive filaments by appropriate Ce doping. The carrier transport mechanism of the device at high resistance state can be dominated by the Schottky emission, whereas the carrier transport mechanism at low resistance state consistent with the Ohmic conduction. This work provides a potential performance improvement approach to design the resistive memory devices for data storage applications. [ABSTRACT FROM AUTHOR]

In the realm of next-generation nonvolatile memories, there is a strong demand for resistive random access memory (ReRAM) devices that are affordable, stable, and simple to produce. Metal–organic frameworks (MOFs) have garnered extensive research attention across diverse fields due to their remarkable chemical adaptability, stability, and exceptional structural variability. In this work, a ReRAM device has been fabricated that incorporates a solution-processed thin film of zeolitic imidazolate framework (ZIF-67), a material of MOFs class, as the resistive switching (RS) layer. This Al/ZIF-67/ITO structured device possesses a consistent RS behavior with a high on/off resistance ratio of ∼ 10 4 . The retention of low resistance state and high resistance state have been shown up to 10 4 s. Furthermore, the device also shows a consistent switching for 500 cycles of pulse switching signals of +6 V/−1.8 V (set/reset). Based on the impedance spectroscopy analysis, a filamentary switching mechanism has been established for the RS behavior of the device. The robust and enduring performance, coupled with the substantial on/off resistance ratio and high retention of the states, makes this device a promising candidate for nonvolatile ReRAM applications. [ABSTRACT FROM AUTHOR]

This paper presents in-memory computing using fast write/erase quantum dot (QD) nonvolatile random access memory (NVRAM). In comparison to NVMs, multi-state NVRAMs offer enhanced Compute-In-Memory capability for applications in deep neural network architecture. Dot product is the methodology that enables an array structure for multiply and accumulate (MAC) operation. We show an approach to dot product computation using multi-state quantum dot channel (QDC) FETs and QD-NVRAM. [ABSTRACT FROM AUTHOR]

Modeling in an emerging technology like RRAM devices is one of the pivotal concerns for its development. In the current bibliography, most of the models face difficulties in implementing or simulating unconventional scenarios, particularly when dealing with complex input signals. In addition, circuit simulators like Spice require long running times for high-resolution results because of their internal mathematical implementation. In this work, a fast, simple, robust, and versatile model for RRAM devices built in MATLAB is presented. The proposed model is a recursive and discretized version of the dynamic memdiode model (DMM) for bipolar-type resistive switching devices originally implemented in LTspice. The DMM model basically consists of two coupled equations: one for the current (non-linear current generator) and a second one for the memory state of the device (time-dependent differential equation). This work presents an easy-to-use tool for researchers to reproduce the experimental behavior of their devices and predict the outcome from non-trivial experiments. Three study cases are reported, aimed at capturing different phenomenologies: a frequency effect study, a cycle-to-cycle variability fit, and a stochastic resonance impact analysis. [ABSTRACT FROM AUTHOR]

Herein, a digital–analog hybrid resistive random‐access memory (RRAM) is prepared by integrating the structurally similar SiNx‐based digital‐type RRAM with analog‐type RRAM by heterogeneous integration method. The Pt/SiNx/Ta/Ru is a digital RRAM due to the formation and breakage of the internal silicon dangling bonds conductive filaments, and the TiN/SiNx/Ta/Ru structure is an analog RRAM due to the traps‐filled limit region of the space‐charge limited current. The heterogeneously integrated digital RRAM has good cycling stability and endurance characteristics, and the heterogeneously integrated analog RRAM has high linearity and multistate counting characteristics. This heterogeneous integration approach is useful for the implementation of hybrid digital–analog RRAM. [ABSTRACT FROM AUTHOR]

The diversity of the carrier transport mechanism in the resistive switching process remains a significant obstacle for the design and application of resistive random access memory (RRAM) devices. In this study, the influence of annealing on the dielectric properties of the Ti/HfOx/Pt RRAM device and its associated carrier transport mechanism is investigated. The results reveal that the current conduction in both annealed and unannealed devices is primarily attributed to grain boundary (GB) relaxation, with only the GB acting as a depletion layer that induces dielectric relaxation. Furthermore, annealing decreases the interfacial polarization and changes the relaxation time at the GB. In addition, the charge carriers exhibit nearest‐neighbor hopping conduction at a higher temperature in Ti/HfOx/Pt RRAM devices, whereas variable‐range‐hopping conduction occurs at lower temperatures. Long‐range charge carrier transport also plays a significant role in the conduction process. To offer a deeper understanding of the conduction processes in the Ti/HfOx/Pt RRAM device, a carrier transport model is proposed, which provides valuable insights into the intricate mechanisms governing conduction in these devices. [ABSTRACT FROM AUTHOR]

Memristors are essential components of neuromorphic systems that mimic the synaptic plasticity observed in biological neurons. In this study, a novel approach employing one‐dimensional covalent organic framework (1D COF) films was explored to enhance the performance of memristors. The unique structural and electronic properties of two 1D COF films (COF‐4,4′‐methylenedianiline (MDA) and COF‐4,4′‐oxydianiline (ODA)) offer advantages for multilevel resistive switching, which is a key feature in neuromorphic computing applications. By further introducing a TiO2 layer on the COF‐ODA film, a built‐in electric field between the COF‐TiO2 interfaces could be generated, demonstrating the feasibility of utilizing COFs as a platform for constructing memristors with tunable resistive states. The 1D nanochannels of these COF structures contributed to the efficient modulation of electrical conductance, enabling precise control over synaptic weights in neuromorphic circuits. This study also investigated the potential of these COF‐based memristors to achieve energy‐efficient and high‐density memory devices. [ABSTRACT FROM AUTHOR]

This work explores an architecture for nonvolatile resistive random-access memory (RRAM) systems. The study proposes a self-rectifying RRAM device utilizing a two-terminal 1R selector that harmonizes the gate control efficacy of transistors with the inherent simplicity of diode structures. A rivet-like HfO2-based RRAM array is meticulously constructed through atomic layer deposition (ALD), aiming to enhance device performance and retention stability. The conformal fabrication technique of ALD method is critical in achieving uniform coverage of isolation layers and precise electrode placement, which is instrumental in the fabrication of high-performance memory cells. Empirical analyses indicate significant improvements in rectification and ON/OFF ratios compared to existing RRAM models, bolstered by compatibility with established CMOS processes. It reveals that these advances are conducive to scalable, high-density memory integration, positioning the RRAM as a viable contender for future computational applications that require high efficiency and neuromorphic computing capabilities. [ABSTRACT FROM AUTHOR]

Artificial neural network‐based computing is anticipated to surpass the von Neumann bottleneck of traditional computers, thus dramatically boosting computational efficiency and showing a wide range of promising applications. In this paper, sol−gel deposition was used to prepare thin films of samarium‐doped hafnium dioxide (Sm:HfO2). When Sm is doped at a concentration of 4%, it mimics biological synapses; meantime, by voltage scanning, an obvious mimicry of resistive switching can be detected, demonstrating that the technology may be applied to simulate biological synapse characteristics, including long‐term potentiation (depression), short‐term potentiation (depression), paired‐pulse facilitation, and learning rules of spike‐time‐dependent plasticity. Additionally, a pulsed neural network is built on the MNIST dataset to test the memristor's capacity to handle visual input. The findings show the possibility of synthetic synapses in artificial intelligence systems that integrate neuromorphic computing with synaptic brain activity. [ABSTRACT FROM AUTHOR]

Resistive random access memory (RRAM) holds great promise for in-memory computing, which is considered the most promising strategy for solving the von Neumann bottleneck. However, there are still significant problems in its application due to the non-uniform performance of RRAM devices. In this work, a bilayer dielectric layer memristor was designed based on the difference in the Gibbs free energy of the oxide. We fabricated Au/Ta2O5/HfO2/Ta/Pt (S3) devices with excellent uniformity. Compared with Au/HfO2/Pt (S1) and Au/Ta2O5/Pt (S2) devices, the S3 device has a low reset voltage fluctuation of 2.44%, and the resistive coefficients of variation are 13.12% and 3.84% in HRS and LRS, respectively, over 200 cycles. Otherwise, the bilayer device has better linearity and more conductance states in multi-state regulation. At the same time, we analyze the physical mechanism of the bilayer device and provide a physical model of ion migration. This work provides a new idea for designing and fabricating resistive devices with stable performance. [ABSTRACT FROM AUTHOR]

In this work, a machine learning‐assisted prediction model is proposed to analyze the reliability issues in the 28 nm resistive random access memory (RRAM) chip with raw data measured from RRAM test chip. The neural network of long‐short time memory (LSTM) is trained by the voltages and resistance during the endurance test (input vectors) and generates the output of the dichotomy states with a satisfied testing result >83.08%. According to the prediction results, the "real fail" state or "fake fail" state of the devices can get in the future. By collaborating with a well‐designed resurrection algorithm (RA), the percentage of real and fake failed cells dropped by 35% and 29%, respectively. Besides, the tail bits in retention significantly reduced from 33% to 14.6% due to the reduction of oxygen vacancies in the gaps of conducting filaments by applying ten consecutive cycles of RA. This intelligent prediction and repair module can prolong the lifetime of RRAM chips effectively in practical applications. [ABSTRACT FROM AUTHOR]

This study presents a method to enhance data processing by integrating a unidirectional analogue artificial neuromorphic memristor device with a piezoelectric nanogenerator, taking inspiration from biological information processing. A self‐powered unidirectional neuromorphic resistive memory device is proposed, comprising an ITO/ZnO/Yb2O3/Au structure combined with a high‐sensitivity piezoelectric nanogenerator (PENG) ITO/ZnO/Al. The memristor device is operated at a voltage sweep of ±4 V with a low operating current in a range of 1.4 µA. The filament formation is studied using a conductive mode atomic force microscope. The integration enables the creation of a self‐powered artificial sensing system that converts mechanical stimuli from the PENG into electrical signals, which are subsequently processed by analogue unidirectional neuromorphic device to mimic the functionality of a neuron without requiring additional circuitry. This emulation encompasses crucial functions such as potentiation, depression, and synaptic plasticity. Furthermore, this study highlights the potential for hardware implementations of neural networks with a weight change of memristor device with nonlinearity (NL) of potentiation and depression of 1.94 and 0.89, respectively, with an accuracy of 93%. The outcomes of this research contribute to the progress of next‐generation low‐power, self‐powered unidirectional neuromorphic perception networks with correlated learning and trainable memory capabilities. [ABSTRACT FROM AUTHOR]

Resistive random access memory (RRAM) crossbar arrays require the highly nonlinear selector with high current density to address a specific memory cell and suppress leakage current through the unselected cell. 3D monolithic integration of RRAM array requires selector devices with a small footprint and low‐temperature processing for ultrahigh‐density data storage. Here, an ultrathin two‐terminal n‐p‐n selector with 2D transition metal dichalcogenides (TMDs) is designed by a low‐temperature transfer method. The van der Waals contact between transferred Au electrodes and TMDs reduces the Fermi level pinning and retains the intrinsic transport behavior of TMDs. The selector with a single type of TMD exhibits a trade‐off between current density and nonlinearity depending on the barrier height. By tuning the Schottky barrier height and controlling the thickness of p‐type WSe2 in MoS2/WSe2/MoS2 n‐p‐n selector for a punch‐through transport, the selector shows high nonlinearity (≈ 230) and high current density (2 × 103 A cm−2) simultaneously. The n‐p‐n selectors are further integrated with a bipolar hexagonal boron nitride memory and calculate the maximum crossbar size of the 2D material‐based one‐selector one‐resistor according to a 10% read margin, which offers the possible realization of future 3D monolithic integration. [ABSTRACT FROM AUTHOR]

In the realm of neuromorphic computing, integrating Binary Neural Networks (BNN) with non‐volatile memory based on emerging materials can be a promising avenue for introducing novel functionalities. This study underscores the viability of lead‐free, air‐stable Cs2SnI6 (CSI) based resistive random access memory (RRAM) devices as synaptic weights in neuromorphic architectures, specifically for BNNs applications. Herein, hydrothermally synthesized CSI perovskites are explored as a resistive layer in RRAM devices either on the rigid or flexible substrate, highlighting reproducible multibit switching with self‐compliance, low‐ resistance‐state (LRS) variations, a decent On/Off ratio(or retention) of ≈103(or 104 s), and endurance exceeding 300 cycles. Moreover, a comprehensive evaluation with the 32 × 32 × 3 RGB CIFAR‐10 dataset reveals that binary convolutional neural networks (BCNN) trained solely on binary weight values can achieve competitive rates of accuracy comparable to those of their analog weight counterparts. These findings highlight the dominance of the LRS for CSI RRAM with self‐compliance in a weighted configuration and minimal influence of the high resistance state despite substantial fluctuations for flexible CSI RRAM under varying bending radii. With its unique electrical switching capabilities, the CSI RRAM is highly anticipated to emerge as a promising candidate for embedded AI systems, especially in IoT devices and wearables. [ABSTRACT FROM AUTHOR]

In this paper, the electrothermal coupling model of metal oxide resistive random access memory (RRAM) is analyzed by using a 2D axisymmetrical structure in COMSOL Multiphysics simulation software. The RRAM structure is a Ti/HfO2/ZrO2/Pt bilayer structure, and the SET and RESET processes of Ti/HfO2/ZrO2/Pt are verified and analyzed. It is found that the width and thickness of CF1 (the conductive filament of the HfO2 layer), CF2 (the conductive filament of the ZrO2 layer), and resistive dielectric layers affect the electrical performance of the device. Under the condition of the width ratio of conductive filament to transition layer (6:14) and the thickness ratio of HfO2 to ZrO2 (7.5:7.5), Ti/HfO2/ZrO2/Pt has stable high and low resistance states. On this basis, the comparison of three commonly used RRAM metal top electrode materials (Ti, Pt, and Al) shows that the resistance switching ratio of the Ti electrode is the highest at about 11.67. Finally, combining the optimal conductive filament size and the optimal top electrode material, the I-V hysteresis loop was obtained, and the switching ratio Roff/Ron = 10.46 was calculated. Therefore, in this paper, a perfect RRAM model is established, the resistance mechanism is explained and analyzed, and the optimal geometrical size and electrode material for the hysteresis characteristics of the Ti/HfO2/ZrO2/Pt structure are found. [ABSTRACT FROM AUTHOR]

Memristive devices have become promising candidates to complement the CMOS technology, due to their CMOS manufacturing process compatibility, zero standby power consumption, high scalability, as well as their capability to implement high-density memories and new computing paradigms. Despite these advantages, memristive devices are susceptible to manufacturing defects that may cause faulty behaviors not observed in CMOS technology, significantly increasing the challenge of testing these novel devices after manufacturing. This work proposes an optimized Design-for-Testability (DfT) strategy based on the introduction of a DfT circuitry that measures the current consumption of Resistive Random Access Memory (ReRAM) cells to detect not only traditional but also unique faults. The new DfT circuitry was validated using a case study composed of a 3x3 word-based ReRAM with peripheral circuitry implemented based on a 130 nm Predictive Technology Model (PTM) library. The obtained results demonstrate the fault detection capability of the proposed strategy with respect to traditional and unique faults. In addition, this paper evaluates the impact related to the DfT circuitry's introduced overheads as well as the impact of process variation on the resolution of the proposed DfT circuitry. [ABSTRACT FROM AUTHOR]