Enhancing Short-Term Plasticity by Inserting a Thin TiO2 Layer in WOx-Based Resistive Switching Memory

Purely self-rectifying memristor-based passive crossbar array for artificial neural network accelerators

Memristor-integrated passive crossbar arrays (CAs) could potentially accelerate neural network (NN) computations, but studies on these devices are limited to software-based simulations owing to their poor reliability. Herein, we propose a self-rectifying memristor-based 1 kb CA as a hardware accelerator for NN computations. We conducted fully hardware-based single-layer NN classification tasks involving the Modified National Institute of Standards and Technology database using the developed passive CA, and achieved 100% classification accuracy for 1500 test sets. We also investigated the influences of the defect-tolerance capability of the CA, impact of the conductance range of the integrated memristors, and presence or absence of selection functionality in the integrated memristors on the image classification tasks. We offer valuable insights into the behavior and performance of CA devices under various conditions and provide evidence of the practicality of memristor-integrated passive CAs as hardware accelerators for NN applications.

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 Cells and Quantized Conductance Characteristics of Al2O3-Based RRAM Device for Neuromorphic System

Recently, various resistance-based memory devices are being studied to replace charge-based memory devices to satisfy high-performance memory requirements. Resistance random access memory (RRAM) shows superior performances such as fast switching speed, structural scalability, and long retention. This work presented the different filament control by the DC voltages and verified its characteristics as a synaptic device by pulse measurement. Firstly, two current-voltage (I-V) curves are characterized by controlling a range of DC voltages. The retention and endurance for each different I-V curve were measured to prove the reliability of the RRAM device. The detailed voltage manipulation confirmed the characteristics of multi-level cell (MLC) and conductance quantization. Lastly, synaptic functions such as potentiation and depression, paired-pulse depression, excitatory post-synaptic current, and spike-timing-dependent plasticity were verified. Collectively, we concluded that Pt/Al₂O₃/TaN is appropriate for the neuromorphic device.

Frequency-Dependent Synapse Weight Tuning in 1S1R with a Short-Term Plasticity TiOx-Based Exponential Selector

Short-term plasticity (STP) is an important synaptic characteristic in the hardware implementation of artificial neural networks (ANN), as it enables the temporal information processing (TIP) capability. However, the STP feature is rather challenging to reproduce from a single nonvolatile resistive random-access memory (RRAM) element, as it requires a certain degree of volatility. In this work, a Pt/TiO_x/Pt exponential selector is introduced not only to suppress the sneak current but also to enable the TIP feature in a one selector-one RRAM (1S1R) synaptic device. Our measurements reveal that the exponential selector exhibits the STP characteristic, while a Pt/HfO_x/Ti RRAM enables the long-term memory capability of the synapse. Thereafter, we experimentally demonstrated pulse frequency-dependent multilevel switching in the 1S1R device, exhibiting the TIP capability of the developed 1S1R synapse. The observed STP of the selector is strongly influenced by the bottom metal-oxide interface, in which Ar plasma treatment on the bottom Pt electrode resulted in the annihilation of the STP feature in the selector. A mechanism is thus proposed to explain the observed STP, using the local electric field enhancement induced at the metal-oxide interface coupled with the drift-diffusion model of mobile O^2- and Ti³⁺ ions. This work therefore provides a reliable means of producing the STP feature in a 1S1R device, which demonstrates the TIP capability sought after in hardware-based ANN.

Standards for the Characterization of Endurance in Resistive Switching Devices

Resistive switching (RS) devices are emerging electronic components that could have applications in multiple types of integrated circuits, including electronic memories, true random number generators, radiofrequency switches, neuromorphic vision sensors, and artificial neural networks. The main factor hindering the massive employment of RS devices in commercial circuits is related to variability and reliability issues, which are usually evaluated through switching endurance tests. However, we note that most studies that claimed high endurances >10⁶ cycles were based on resistance versus cycle plots that contain very few data points (in many cases even <20), and which are collected in only one device. We recommend not to use such a characterization method because it is highly inaccurate and unreliable (i.e., it cannot reliably demonstrate that the device effectively switches in every cycle and it ignores cycle-to-cycle and device-to-device variability). This has created a blurry vision of the real performance of RS devices and in many cases has exaggerated their potential. This article proposes and describes a method for the correct characterization of switching endurance in RS devices; this method aims to construct endurance plots showing one data point per cycle and resistive state and combine data from multiple devices. Adopting this recommended method should result in more reliable literature in the field of RS technologies, which should accelerate their integration in commercial products.

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.

Synaptic Characteristics from Homogeneous Resistive Switching in Pt/Al2O3/TiN Stack

In this work, we propose three types of resistive switching behaviors by controlling operation conditions. We confirmed well-known filamentary switching in Al₂O₃-based resistive switching memory using the conventional device working operation with a forming process. Here, filamentary switching can be classified into two types depending on the compliance current. On top of that, the homogeneous switching is obtained by using a negative differential resistance effect before the forming or setting process in a negative bias. The variations of the low-resistance and high-resistance states in the homogeneous switching are comparable to the filamentary switching cases. However, the drift characteristics of the low-resistance and high-resistance states in the homogeneous switching are unstable with time. Therefore, the short-term plasticity effects, such as the current decay in repeated pulses and paired pulses facilitation, are demonstrated when using the resistance drift characteristics. Finally, the conductance can be increased and decreased by 50 consecutive potentiation pulses and 50 consecutive depression pulses, respectively. The linear conductance update in homogeneous switching is achieved compared to the filamentary switching, which ensures the high pattern-recognition accuracy.