Voltage Amplitude-Controlled Synaptic Plasticity from Complementary Resistive Switching in Alloying HfOx with AlOx-Based RRAM

The Enhanced Performance of Neuromorphic Computing Hardware in an ITO/ZnO/HfOx/W Bilayer-Structured Memory Device

This study discusses the potential application of ITO/ZnO/HfOx/W bilayer-structured memory devices in neuromorphic systems. These devices exhibit uniform resistive switching characteristics and demonstrate favorable endurance (>102) and stable retention (>104 s). Notably, the formation and rupture of filaments at the interface of ZnO and HfOx contribute to a higher ON/OFF ratio and improve cycle uniformity compared to RRAM devices without the HfOx layer. Additionally, the linearity of potentiation and depression responses validates their applicability in neural network pattern recognition, and spike-timing-dependent plasticity (STDP) behavior is observed. These findings collectively suggest that the ITO/ZnO/HfOx/W structure holds the potential to be a viable memory component for integration into neuromorphic systems.

Improved Resistive and Synaptic Characteristics in Neuromorphic Systems Achieved Using the Double-Forming Process

In this study, we investigate the electrical properties of ITO/ZrOx/TaN RRAM devices for neuromorphic computing applications. The thickness and material composition of the device are analyzed using transmission electron microscopy. Additionally, the existence of TaON interface layers was confirmed using dispersive X-ray spectroscopy and X-ray photoelectron analysis. The forming process of the ZrOx-based device can be divided into two categories, namely single- and double forming, based on the initial lattice oxygen vacancies. The resistive switching behaviors of the two forming methods are compared in terms of the uniformity properties of endurance and retention. The rationale behind each I-V forming process was determined as follows: in the double-forming method case, an energy band diagram was constructed using F-N tunneling; conversely, in the single-forming method case, the ratio of oxygen vacancies was extracted based on XPS analysis to identify the conditions for filament formation. Subsequently, synaptic simulations for the applications of neuromorphic systems were conducted using a pulse scheme to achieve potentiation and depression with a deep neural network-based pattern recognition system to display the achieved recognition accuracy. Finally, high-order synaptic plasticity (spike-timing-dependent plasticity (STDP)) is emulated based on the Hebbian rule.

Resistive Switching Characteristics of Alloyed AlSiOx Insulator for Neuromorphic Devices

Charge-based memories, such as NAND flash and dynamic random-access memory (DRAM), have reached scaling limits and various next-generation memories are being studied to overcome their issues. Resistive random-access memory (RRAM) has advantages in structural scalability and long retention characteristics, and thus has been studied as a next-generation memory application and neuromorphic system area. In this paper, AlSiO_x, which was used as an alloyed insulator, was used to secure stable switching. We demonstrate synaptic characteristics, as well as the basic resistive switching characteristics with multi-level cells (MLC) by applying the DC sweep and pulses. Conduction mechanism analysis for resistive switching characteristics was conducted to understand the resistive switching properties of the device. MLC, retention, and endurance are evaluated and potentiation/depression curves are mimicked for a neuromorphic device.

Multi-Level Resistive Switching of Pt/HfO2/TaN Memory Device

This work characterizes resistive switching and neuromorphic simulation of Pt/HfO2/TaN stack as an artificial synaptic device. A stable bipolar resistive switching operation is performed by repetitive DC sweep cycles. Furthermore, endurance (DC 100 cycles) and retention (5000 s) are demonstrated for reliable resistive operation. Low-resistance and high-resistance states follow the Ohmic conduction and Poole–Frenkel emission, respectively, which is verified through the fitting process. For practical operation, the set and reset processes are performed through pulses. Further, potentiation and depression are demonstrated for neuromorphic application. Finally, neuromorphic system simulation is performed through a neural network for pattern recognition accuracy of the Fashion Modified National Institute of Standards and Technology dataset.

Demonstration of Threshold Switching and Bipolar Resistive Switching in Ag/SnOx/TiN Memory Device

In this work, we observed the duality of threshold switching and non-volatile memory switching of Ag/SnOx/TiN memory devices by controlling the compliance current (CC) or pulse amplitude. The insulator thickness and chemical analysis of the device stack were confirmed by transmission electron microscope (TEM) images of the Ag/SnOx/TiN stack and X-ray photoelectron spectroscopy (XPS) of the SnOx film. The threshold switching was achieved at low CC (50 μA), showing volatile resistive switching. Optimal CC (5 mA) for bipolar resistive switching conditions with a gradual transition was also found. An unstable low-resistance state (LRS) and negative-set behavior were observed at CCs of 1 mA and 30 mA, respectively. We also demonstrated the pulse operation for volatile switching, set, reset processes, and negative-set behaviors by controlling pulse amplitude and polarity. Finally, the potentiation and depression characteristics were mimicked by multiple pulses, and MNIST pattern recognition was calculated using a neural network, including the conductance update for a hardware-based neuromorphic system.

Resistive Switching Characteristics of ZnO-Based RRAM on Silicon Substrate

In this work, we conducted the following analysis of Ni/ZnO (20 nm)/n-type Si RRAM device with three different compliance currents (CCs). We compared I–V curves, including set, reset voltages, and resistance of LRS, HRS states for each CCs. For an accurate comparison of each case, statistical analysis is presented. In each case, the average value and the relative standard deviation (RSD) of resistance are calculated to analyze the characteristics of the distribution. The best variability is observed at higher CC (5 mA). In addition, we validated the non-volatile properties of the device using the retention data for each of the CCs. Based on this comparison, we proposed the most appropriate CC of the device operation. Also, a pulse was applied to measure the current waveform and demonstrate the regular operation of the device. Finally, the resistance of LRS and HRS states was measured by pulse. We statistically compared the measured pulse data with the DC data.

Volatile Resistive Switching Characteristics of Pt/HfO2/TaOx/TiN Short-Term Memory Device

In this work, we study the threshold switching and short-term memory plasticity of a Pt/HfO2/TaOx/TiN resistive memory device for a neuromorphic system. First, we verify the thickness and elemental characterization of the device stack through transmission electron microscopy (TEM) and an energy-dispersive X-ray spectroscopy (EDS) line scan. Volatile resistive switching with low compliance current is observed under the DC sweep in a positive bias. Uniform cell-to-cell and cycle-to-cycle DC I-V curves are achieved by means of a repetitive sweep. The mechanism of volatile switching is explained by the temporal generation of traps. Next, we initiate the accumulation of the conductance and a natural decrease in the current by controlling the interval time of the pulses. Finally, we conduct a neuromorphic simulation to calculate the pattern recognition accuracy. These results can be applicable to short-term memory applications such as temporal learning in a neuromorphic system.

Gradually Modified Conductance in the Self-Compliance Region of an Atomic-Layer-Deposited Pt/TiO2/HfAlOx/TiN RRAM Device

This study presents conductance modulation in a Pt/TiO2/HfAlOx/TiN resistive memory device in the compliance region for neuromorphic system applications. First, the chemical and material characteristics of the atomic-layer-deposited films were verified by X-ray photoelectron spectroscopy depth profiling. The low-resistance state was effectively controlled by the compliance current, and the high-resistance state was adjusted by the reset stop voltage. Stable endurance and retention in bipolar resistive switching were achieved. When a compliance current of 1 mA was imposed, only gradual switching was observed in the reset process. Self-compliance was used after an abrupt set transition to achieve a gradual set process. Finally, 10 cycles of long-term potentiation and depression were obtained in the compliance current region for neuromorphic system applications.

Improved Synaptic Device Properties of HfAlOx Dielectric on Highly Doped Silicon Substrate by Partial Reset Process

This work demonstrates the synaptic properties of the alloy-type resistive random-access memory (RRAM). We fabricated the HfAlOx-based RRAM for a synaptic device in a neuromorphic system. The deposition of the HfAlOx film on the silicon substrate was verified by X-ray photoelectron spectroscopy (XPS) analysis. It was found that both abrupt and gradual resistive switching could be implemented, depending on the reset stop voltage. In the reset process, the current gradually decreased at weak voltage, and at strong voltage, it tended to decrease rapidly by Joule heating. The type of switching determined by the first reset process was subsequently demonstrated to be stable switching by successive set and reset processes. A gradual switching type has a much smaller on/off window than abrupt switching. In addition, retention maintained stability up to 2000 s in both switching cases. Next, the multiple current states were tested in the gradual switching case by identical pulses. Finally, we demonstrated the potentiation and depression of the Cu/HfAlOx/Si device as a synapse in an artificial neural network and confirmed that gradual resistive switching was suitable for artificial synapses, using neuromorphic system simulation.

Irregular Resistive Switching Behaviors of Al2O3-Based Resistor with Cu Electrode

In this work, we examined the irregular resistive switching behaviors of a complementary metal–oxide–semiconductor (CMOS)-compatible Cu/Al2O3/Si resistor device. X-ray photoelectron spectroscopy (XPS) analysis confirmed the chemical and material compositions of a Al2O3 thin film layer and Si substrate. Bipolar resistive switching occurred in a more stable manner than the unipolar resistive switching in the device did. Five cells were verified over 50 endurance cycles in terms of bipolar resistive switching, and a good retention was confirmed for 10,000 s in the high-resistance state (HRS) and the low-resistance state (LRS). Both high reset current (~10 mA) and low reset current (<100 μA) coexisted in the bipolar resistive switching. We investigated nonideal resistive switching behaviors such as negative-set and current overshoot, which could lead to resistive switching failure.

Gradually Tunable Conductance in TiO2/Al2O3 Bilayer Resistors for Synaptic Device

In this work, resistive switching and synaptic behaviors of a TiO2/Al2O3 bilayer device were studied. The deposition of Pt/Ti/TiO2/Al2O3/TiN stack was confirmed by transmission electron microscopy (TEM) and energy X-ray dispersive spectroscopy (EDS). The initial state before the forming process followed Fowler-Nordheim (FN) tunneling. A strong electric field was applied to Al2O3 with a large energy bandgap for FN tunneling, which was confirmed by the I-V fitting process. Bipolar resistive switching was conducted by the set process in a positive bias and the reset process in a negative bias. High-resistance state (HRS) followed the trap-assisted tunneling (TAT) model while low-resistance state (LRS) followed the Ohmic conduction model. Set and reset operations were verified by pulse. Moreover, potentiation and depression in the biological synapse were verified by repetitive set pulses and reset pulses. Finally, the device showed good pattern recognition accuracy (~88.8%) for a Modified National Institute of Standards and Technology (MNIST) handwritten digit database in a single layer neural network including the conductance update of the device.

Bipolar and Complementary Resistive Switching Characteristics and Neuromorphic System Simulation in a Pt/ZnO/TiN Synaptic Device

In this work, a ZnO-based resistive switching memory device is characterized by using simplified electrical conduction models. The conventional bipolar resistive switching and complementary resistive switching modes are accomplished by tuning the bias voltage condition. The material and chemical information of the device stack including the interfacial layer of TiON is well confirmed by transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy (XPS) analysis. The device exhibits uniform gradual bipolar resistive switching (BRS) with good endurance and self-compliance characteristics. Moreover, complementary resistive switching (CRS) is achieved by applying the compliance current at negative bias and increasing the voltage at positive bias. The synaptic behaviors such as long-term potentiation and long-term depression are emulated by applying consecutive pulse input to the device. The CRS mode has a higher array size in the cross-point array structure than the BRS mode due to more nonlinear I–V characteristics in the CRS mode. However, we reveal that the BRS mode shows a better pattern recognition rate than the CRS mode due to more uniform conductance update.

Improved Pulse-Controlled Conductance Adjustment in Trilayer Resistors by Suppressing Current Overshoot

In this work, we demonstrate the enhanced synaptic behaviors in trilayer dielectrics (HfO₂/Si₃N₄/SiO₂) on highly doped n-type silicon substrate. First, the three dielectric layers were subjected to material and chemical analyses and thoroughly investigated via transmission electron microscopy and X-ray photoelectron spectroscopy. The resistive switching and synaptic behaviors were improved by inserting a Si₃N₄ layer between the HfO₂ and SiO₂ layers. The electric field within SiO₂ was mitigated, thus reducing the current overshoot in the trilayer device. The reset current was considerably reduced in the trilayer device compared to the bilayer device without a Si₃N₄ layer. Moreover, the nonlinear characteristics in the low-resistance state are helpful for implementing high-density memory. The higher array size in the trilayer device was verified by cross-point array simulation. Finally, the multiple conductance adjustment was demonstrated in the trilayer device by controlling the gradual set and reset switching behavior.