Study of in Situ Silver Migration in Amorphous Boron Nitride CBRAM Device

Volatile threshold switching and synaptic properties controlled by Ag diffusion using Schottky defects

We investigated diffusion memristors in the structure of Ag/Ta2O5/HfO2/Pt, in which active Ag ions control active metal ion diffusion and mimic biological brain functions. The CMOS compatible high-k metal oxide could control an Ag electrode that was ionized by applying an appropriate voltage to form a conductive filament, and the movement of Ag ions was chemically and electrically controlled due to oxygen density. This diffusion memristor exhibited diffused characteristics with a selectivity of 109, and achieved a low power consumption of 2 mW at a SET voltage of 0.2 V. The threshold transitions were reliably repeatable over 20 cycles for compliance currents of 10-6 A, 10-4 A, and no compliance current, with the largest standard deviation value of SET variation being 0.028. Upon filament formation, Ag ions readily diffused into the interface of the Ta2O5 and HfO2 layer, which was verified by investigating the Ag atomic percentage using XPS and vertical EDX and by measuring the relaxation time of 0.8 ms. Verified volatile switching device demonstrated the biological synaptic properties of quantum conductance, short-term memory, and long-term memory due to controlling the Ag. Diffusion memristors using designed control and switching layers as following film density and oxygen vacancy have improved results as low-power devices and neuromorphic devices compared to other diffusion-based devices, and these properties can be used for various applications such as selectors, synapses, and neuromorphic devices.

A Highly Reliable Molybdenum Disulfide-Based Synaptic Memristor Using a Copper Migration-Controlled Structure

Memristors are drawing attention as neuromorphic hardware components because of their non-volatility and analog programmability. In particular, electrochemical metallization (ECM) memristors are extensively researched because of their linear conductance controllability. Two-dimensional materials as switching medium of ECM memristors give advantages of fast speed, low power consumption, and high switching uniformity. However, the multistate retention in the switching conductance range for the long-term reliable neuromorphic system has not been achieved using two-dimensional materials-based ECM memristors. In this study, the copper migration-controlled ECM memristor showing excellent multistate retention characteristics in the switching conductance range using molybdenum disulfide (MoS2 ) and aluminum oxide (Al2 O3 ) is proposed. The fabricated device exhibits gradual resistive switching with low switching voltage (<0.5 V), uniform switching (σ/µ ∼ 0.07), and a wide switching range (>12). Importantly, excellent reliabilities with robustness to cycling stress and retention over 104 s for more than 5-bit states in the switching conductance range are achieved. Moreover, the contribution of the Al2 O3 layer to the retention characteristic is investigated through filament morphology observation using transmission electron microscopy (TEM) and copper migration component analysis. This study provides a practical approach to developing highly reliable memristors with exceptional switching performance.

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.

Revealing the effect of substitutional cation doping in the A-site of nanoscale APbI3 perovskite layers for enhanced retention and endurance in optoelectronic resistive switching for non-volatile bipolar memory devices

The effect of substitutional cation doping in the A-site of the nanoscale APbI₃ perovskite layer has been systematically investigated to achieve improvements in the charge-carrier dynamics and endurance of non-volatile bipolar (NVB) memory devices. We successfully adopted an energy-efficient, ultra-fast microwave-assisted solvothermal (MW-ST) synthesis route to prepare a sequence of APbI₃ (A = MA⁺, FA⁺, MAFA⁺, CsMA⁺ and CsMAFA⁺) perovskite powders with morphological transitions from cube-like polyhedrons to mixed polyhedrons and rods within 10 minutes at 120 °C without requiring any inert-gas atmosphere under high-humid ambient conditions. As-prepared APbI₃ powders were dissolved in DMSO:DMF, followed by the fabrication of a thin film via spin-coating. Upon annealing at 120 °C, the nanoscale self-assembled thin-film layer was formed. We observed that devices with the inorganic Cs⁺ cation with organic cations, (CsMAPI and CsMAFAPI) device showed improved endurance (3500 and 5000 cycles, respectively) and outstanding retention (60 000 s) owing to effective charge-carrier dynamics, compared to organic cation-based MAPI, FAPI and MAFAPI (1800, 1200 and 1300 cycles, respectively). Significantly, various cation-doped APbI₃-powders obtained via the MW-ST method remained to be stable for up to 5-months under high-humid conditions. Thus, enhanced optoelectronic-memory performance studies could provide an opportunity for next-generation nanoscale ORSNVB-memory devices for artificial intelligence (AI) and Internet of Things (IoT) applications.

Synapse-Mimetic Hardware-Implemented Resistive Random-Access Memory for Artificial Neural Network

Memristors mimic synaptic functions in advanced electronics and image sensors, thereby enabling brain-inspired neuromorphic computing to overcome the limitations of the von Neumann architecture. As computing operations based on von Neumann hardware rely on continuous memory transport between processing units and memory, fundamental limitations arise in terms of power consumption and integration density. In biological synapses, chemical stimulation induces information transfer from the pre- to the post-neuron. The memristor operates as resistive random-access memory (RRAM) and is incorporated into the hardware for neuromorphic computing. Hardware composed of synaptic memristor arrays is expected to lead to further breakthroughs owing to their biomimetic in-memory processing capabilities, low power consumption, and amenability to integration; these aspects satisfy the upcoming demands of artificial intelligence for higher computational loads. Among the tremendous efforts toward achieving human-brain-like electronics, layered 2D materials have demonstrated significant potential owing to their outstanding electronic and physical properties, facile integration with other materials, and low-power computing. This review discusses the memristive characteristics of various 2D materials (heterostructures, defect-engineered materials, and alloy materials) used in neuromorphic computing for image segregation or pattern recognition. Neuromorphic computing, the most powerful artificial networks for complicated image processing and recognition, represent a breakthrough in artificial intelligence owing to their enhanced performance and lower power consumption compared with von Neumann architectures. A hardware-implemented CNN with weight control based on synaptic memristor arrays is expected to be a promising candidate for future electronics in society, offering a solution based on non-von Neumann hardware. This emerging paradigm changes the computing algorithm using entirely hardware-connected edge computing and deep neural networks.

Introduction of defects in hexagonal boron nitride for vacancy-based 2D memristors

Two-dimensional (2D) materials have become potential resistive switching (RS) layers to prepare emerging non-volatile memristors. The atomically thin thickness and the highly controllable defect density contribute to the construction of ultimately scaled memory cells with stable switching behaviors. Although the conductive bridge random-access memory based on 2D hexagonal boron nitride has been widely studied, the realization of RS completely relying on vacancies in 2D materials has performance superiority. Here, we synthesize carbon-doped h-BN (C-h-BN) with a certain number of defects by controlling the weight percentage of carbon powder in the source. These defects can form a vacancy-based conductive filament under an applied electric field. The memristor displays bipolar non-volatile memory with a low SET voltage of 0.85 V and shows a long retention time of up to 10⁴ s at 120 °C. The response times of the SET and RESET process are less than 80 ns and 240 ns, respectively. The current mapping by conductive atomic force microscopy demonstrates the electric-field-induced current tunneling from defective sites of the C-h-BN flake, revealing the defect-based RS in the C-h-BN memristor. Moreover, C-h-BN with excellent flexibility can be applied to wearable devices, maintaining stable RS performance in a variety of bending environments and after multiple bending cycles. The vacancy-based 2D memristor provides a new strategy for developing ultra-scaled memory units with high controllability.

SET Kinetics of Ag/HfO2-Based Diffusive Memristors under Various Counter-Electrode Materials

The counter-electrode (CE) material in electrochemical metallization memory (ECM) cells plays a crucial role in the switching process by affecting the reactions at the CE/electrolyte interface. This is due to the different electrocatalytic activity of the CE material towards reduction-oxidation reactions, which determines the metal ion concentration in the electrolyte and ultimately impacts the switching kinetics. In this study, the focus is laid on Pt, TiN, and W, which are relevant in standard chip technology. For these, the influence of CE metal on the switching kinetics of Ag/HfO₂-based volatile ECM cells is investigated. Rectangular voltage pulses of different amplitudes were applied, and the SET times were analyzed from the transient curves. The results show that CE material has a significant effect on the SET kinetics, with differences being observed depending on the voltage regime. The formation of interfacial oxides at the CE/electrolyte interface, particularly for non-noble metals, is also discussed in relation to the findings. Overall, this work highlights the important role of the CE material in the switching process of Ag/HfO₂-based diffusive memristors and the importance of considering interfacial oxide formation in the design of these devices.

Hexagonal Boron Nitride for Next-Generation Photonics and Electronics

Hexagonal boron nitride (h-BN), an insulating 2D layered material, has recently attracted tremendous interest motivated by the extraordinary properties it shows across the fields of optoelectronics, quantum optics, and electronics, being exotic material platforms for various applications. At an early stage of h-BN research, it is explored as an ideal substrate and insulating layers for other 2D materials due to its atomically flat surface that is free of dangling bonds and charged impurities, and its high thermal conductivity. Recent discoveries of structural and optical properties of h-BN have expanded potential applications into emerging electronics and photonics fields. h-BN shows a very efficient deep-ultraviolet band-edge emission despite its indirect-bandgap nature, as well as stable room-temperature single-photon emission over a wide wavelength range, showing a great potential for next-generation photonics. In addition, h-BN is extensively being adopted as active media for low-energy electronics, including nonvolatile resistive switching memory, radio-frequency devices, and low-dielectric-constant materials for next-generation electronics.

Non-Volatile Memory and Synaptic Characteristics of TiN/CeOx/Pt RRAM Devices

In this study, we investigate the synaptic characteristics and the non-volatile memory characteristics of TiN/CeOx/Pt RRAM devices for a neuromorphic system. The thickness and chemical properties of the CeOx are confirmed through TEM, EDS, and XPS analysis. A lot of oxygen vacancies (ions) in CeOx film enhance resistive switching. The stable bipolar resistive switching characteristics, endurance cycling (>100 cycles), and non-volatile properties in the retention test (>10,000 s) are assessed through DC sweep. The filamentary switching model and Schottky emission-based conduction model are presented for TiN/CeOx/Pt RRAM devices in the LRS and HRS. The compliance current (1~5 mA) and reset stop voltage (−1.3~−2.2 V) are used in the set and reset processes, respectively, to implement multi-level cell (MLC) in DC sweep mode. Based on neural activity, a neuromorphic system is performed by electrical stimulation. Accordingly, the pulse responses achieve longer endurance cycling (>10,000 cycles), MLC (potentiation and depression), spike-timing dependent plasticity (STDP), and excitatory postsynaptic current (EPSC) to mimic synapse using TiN/CeOx/Pt RRAM devices.

Flexible Threshold Switching Based on CsCu2I3 with Low Threshold Voltage and High Air Stability

Halide perovskites featuring remarkable optoelectronic properties hold great potential for threshold switching devices (TSDs) that are of primary importance to next-generation memristors and neuromorphic computers. However, such devices are still in their infancy due to the unsolved challenges of high threshold voltage, poor stability, and lead-containing features. Herein, a unipolar TSD based on an all-inorganic halide perovskite of CsCu₂I₃ is demonstrated, exhibiting the fascinating attributes of a low threshold voltage of 0.54 V, a high ON/OFF ratio of 10⁴, robust air stability over 70 days, a steep switching slope of 6.2 mV·decade^-1, and lead-free composition. Moreover, the threshold voltage can be further reduced to 0.23 V using UV illumination to reduce the barrier of iodide ion migration. The multilevel threshold switching behavior can be realized through the modulation of either the compliance current or the scan rate. The TSD with mechanical compliance and transparency is also demonstrated. This work enriches TSDs with expanded perovskite materials, boosting the related applications of this emerging class of device families.

Conductive Bridge Random Access Memory (CBRAM): Challenges and Opportunities for Memory and Neuromorphic Computing Applications

Due to a rapid increase in the amount of data, there is a huge demand for the development of new memory technologies as well as emerging computing systems for high-density memory storage and efficient computing. As the conventional transistor-based storage devices and computing systems are approaching their scaling and technical limits, extensive research on emerging technologies is becoming more and more important. Among other emerging technologies, CBRAM offers excellent opportunities for future memory and neuromorphic computing applications. The principles of the CBRAM are explored in depth in this review, including the materials and issues associated with various materials, as well as the basic switching mechanisms. Furthermore, the opportunities that CBRAMs provide for memory and brain-inspired neuromorphic computing applications, as well as the challenges that CBRAMs confront in those applications, are thoroughly discussed. The emulation of biological synapses and neurons using CBRAM devices fabricated with various switching materials and device engineering and material innovation approaches are examined in depth.

Amorphous Boron Nitride Memristive Device for High-Density Memory and Neuromorphic Computing Applications

Although two-dimensional (2D) nanomaterials are promising candidates for use in memory and synaptic devices owing to their unique physical, chemical, and electrical properties, the process compatibility, synthetic reliability, and cost-effectiveness of 2D materials must be enhanced. In this context, amorphous boron nitride (a-BN) has emerged as a potential material for future 2D nanoelectronics. Therefore, we explored the use of a-BN for multilevel resistive switching (MRS) and synaptic learning applications by fabricating a complementary metal-oxide-semiconductor (CMOS)-compatible Ag/a-BN/Pt memory device. The redox-active Ag and boron vacancies enhance the mixed electrochemical metallization and valence change conduction mechanism. The synthesized a-BN switching layer was characterized using several analyses. The fabricated memory devices exhibited bipolar resistive switching with low set and reset voltages (+0.8 and -2 V, respectively) and a small operating voltage distribution. In addition, the switching voltages of the device were modeled using a time-series analysis, for which the Holt's exponential smoothing technique provided good modeling and prediction results. According to the analytical calculations, the fabricated Ag/a-BN/Pt device was found to be memristive, and its MRS ability was investigated by varying the compliance current. The multilevel states demonstrated a uniform resistance distribution with a high endurance of up to 10⁴ direct current (DC) cycles and memory retention characteristics of over 10⁶ s. Conductive atomic force microscopy was performed to clarify the resistive switching mechanism of the device, and the likely mixed electrochemical metallization and valence change mechanisms involved therein were discussed based on experimental results. The Ag/a-BN/Pt memristive devices mimicked potentiation/depression and spike-timing-dependent plasticity-based Hebbian-learning rules with a high pattern accuracy (90.8%) when implemented in neural network simulations.

Suppressed Stochastic Switching Behavior and Improved Synaptic Functions in an Atomic Switch Embedded with a 2D NbSe2 Material

We investigated chemical vapor-deposited (CVD) two-dimensional (2D) niobium diselenide (NbSe₂) material for the resistive switching and synaptic characteristics. Three different atomic switch devices with Ag/HfO₂/Pt, Ag/Ti/HfO₂/Pt, and Ag/NbSe₂/HfO₂/Pt were studied as both memory and neuromorphic devices. Both the inserted Ti and NbSe₂ buffer layers effectively control the stochastic Ag-ion diffusion, leading to suppressed variation of switching characteristics, which is a critical issue in an atomic switch device. Especially, the device with the 2D NbSe₂ buffer layer strikingly enhanced the device reliability in both endurance and retention. In conjunction with scanning transmission electron microscopy (STEM) and energy-dispersive spectrometry (EDS) analysis of the control of the Ag-ion migration, it was understood that filament connection is interrelated with the SET and RESET processes. Besides resistive behaviors in the memory device, various synapse functions such as spike-rate-dependent plasticity (SRDP), forgetting curve, potentiation, and depression were demonstrated with an atomic switch with the 2D NbSe₂ buffer layer. Furthermore, the emulated long-term synaptic property was simulated using the MNIST 28 × 28 pixel database. Using adopting a CVD 2D NbSe₂ blocking layer, the stochastic Ag-ion diffusion behavior is well-controlled and therefore stable switching and synapse functions are attained.

Improved Stability and Controllability in ZrN-Based Resistive Memory Device by Inserting TiO2 Layer

In this work, the enhanced resistive switching of ZrN-based resistive switching memory is demonstrated by embedding TiO2 layer between Ag top electrode and ZrN switching layer. The Ag/ZrN/n-Si device exhibits unstable resistive switching as a result of the uncontrollable Ag migration. Both unipolar and bipolar resistive switching with high RESET current were observed. Negative-SET behavior in the Ag/ZrN/n-Si device makes set-stuck, causing permanent resistive switching failure. On the other hand, the analogue switching in the Ag/TiO2/ZrN/n-Si device, which could be adopted for the multi-bit data storage applications, is obtained. The gradual switching in Ag/TiO2/ZrN/n-Si device is achieved, possibly due to the suppressed Ag diffusion caused by TiO2 inserting layer. The current-voltage (I-V) switching characteristics of Ag/ZrN/n-Si and Ag/TiO2/ZrN/n-Si devices can be well verified by pulse transient. Finally, we established that the Ag/TiO2/ZrN/n-Si device is suitable for neuromorphic application through a comparison study of conductance update. This paper paves the way for neuromorphic application in nitride-based memristor devices.

Emulation of Biological Synapse Characteristics from Cu/AlN/TiN Conductive Bridge Random Access Memory

Here, we present the synaptic characteristics of AlN-based conductive bridge random access memory (CBRAM) as a synaptic device for neuromorphic systems. Both non-volatile and volatile memory are observed by simply controlling the strength of the Cu filament inside the AlN film. For non-volatile switching induced by high compliance current (CC), good retention with a strong Cu metallic filament is verified. Low-resistance state (LRS) and high-resistance state (HRS) conduction follow metallic Ohmic and trap-assisted tunneling (TAT), respectively, which are supported by I-V fitting and temperature dependence. The transition from long-term plasticity (LTP) to short-term plasticity (STP) is demonstrated by increasing the pulse interval time for synaptic device application. Also, paired-pulse facilitation (PPF) in the nervous system is mimicked by sending two identical pulses to the CBRAM device to induce STP. Finally, potentiation and depression are achieved by gradually increasing the set and reset voltage in pulse transient mode.

The coexistence of threshold and memory switching characteristics of ALD HfO2 memristor synaptic arrays for energy-efficient neuromorphic computing

The development of bioinspired electronic devices that can mimic the biological synapses is an essential step towards the development of efficient neuromorphic systems to simulate the functions of the human brain. Among various materials that can be utilized to attain electronic synapses, the existing semiconductor industry-compatible conventional materials are more favorable due to their low cost, easy fabrication and reliable switching properties. In this work, atomic layer deposited HfO2-based memristor synaptic arrays are fabricated. The coexistence of threshold switching (TS) and memory switching (MS) behaviors is obtained by modulating the device current. The TS characteristics are exploited to emulate essential synaptic functions. The Ag diffusive dynamics of our electronic synapses, analogous to the Ca2+ dynamics in biological synapses, is utilized to emulate synaptic functions. Electronic synapses successfully emulate paired-pulse facilitation (PPF), post-tetanic potentiation (PTP), spike-timing-dependent plasticity (STDP), short-term potentiation (STP), long-term potentiation (LTP) and transition from STP to LTP with rehearsals. The psychological memorization model of short-term memory (STM) to long-term memory (LTM) transition is mimicked by image memorization in crossbar array devices. Reliable and repeatable bipolar MS behaviors with a low operating voltage are obtained by a higher compliance current for energy-efficient nonvolatile memory applications.

Reversible Transformation between Bipolar Memory Switching and Bidirectional Threshold Switching in 2D Layered K-Birnessite Nanosheets

Birnessite-related manganese dioxides (MnO₂) have recently been studied owing to their diverse low-dimensional layered structures and potential applications in energy devices. The birnessite MnO₂ possesses a layered structure with edge-shared MnO₆ octahedra layer stacked with interlayer of cations. The unique layered structure may provide some distinct electrical properties for the 2D layered nanosheets. In this work, layered K-birnessite MnO₂ samples are synthesized by a hydrothermal method. The resistive switching (RS) devices based on single K-birnessite MnO₂ nanosheets are fabricated by transferring the nanosheets onto SiO₂/Si substrates through a facile and feasible method of mechanical exfoliation. The device exhibits nonvolatile memory switching (MS) behaviors with high current ON/OFF ratio of ∼2 × 10⁵. And more importantly, reversible transformation between the nonvolatile MS and volatile threshold switching (TS) can be achieved in the single layered nanosheet through tuning the magnitude of compliance current (I_cc). To be more specific, a relatively high I_cc (1 mA) can trigger the nonvolatile MS behaviors, while a relatively low I_cc (≤100 μA) can generate volatile TS characteristics. This work not only demonstrates the memristor based on single birnessite-related MnO₂ nanosheet, but also offers an insight into understanding the complex resistive switching types and relevant physical mechanisms of the 2D layered oxide nanosheets.

Memristive and Synaptic Characteristics of Nitride-Based Heterostructures on Si Substrate

Brain-inspired artificial synaptic devices and neurons have the potential for application in future neuromorphic computing as they consume low energy. In this study, the memristive switching characteristics of a nitride-based device with two amorphous layers (SiN/BN) is investigated. We demonstrate the coexistence of filamentary (abrupt) and interface (homogeneous) switching of Ni/SiN/BN/n⁺⁺-Si devices. A better gradual conductance modulation is achieved for interface-type switching as compared with filamentary switching for an artificial synaptic device using appropriate voltage pulse stimulations. The improved classification accuracy for the interface switching (85.6%) is confirmed and compared to the accuracy of the filamentary switching mode (75.1%) by a three-layer neural network (784 × 128 × 10). Furthermore, the spike-timing-dependent plasticity characteristics of the synaptic device are also demonstrated. The results indicate the possibility of achieving an artificial synapse with a bilayer SiN/BN structure.

Comparison of the Electrical Response of Cu and Ag Ion-Conducting SDC Memristors Over the Temperature Range 6 K to 300 K

Electrical performance of self-directed channel (SDC) ion-conducting memristors which use Ag and Cu as the mobile ion source are compared over the temperature range of 6 K to 300 K. The Cu-based SDC memristors operate at temperatures as low as 6 K, whereas Ag-based SDC memristors are damaged if operated below 125 K. It is also observed that Cu reversibly diffuses into the active Ge2Se3 layer during normal device shelf-life, thus changing the state of a Cu-based memristor over time. This was not observed for the Ag-based SDC devices. The response of each device type to sinusoidal excitation is provided and shows that the Cu-based devices exhibit hysteresis lobe collapse at lower frequencies than the Ag-based devices. In addition, the pulsed response of the device types is presented.