Tunable Synaptic Characteristics of a Ti/TiO2/Si Memory Device for Reservoir Computing

An overview of critical applications of resistive random access memory

The rapid advancement of new technologies has resulted in a surge of data, while conventional computers are nearing their computational limits. The prevalent von Neumann architecture, where processing and storage units operate independently, faces challenges such as data migration through buses, leading to decreased computing speed and increased energy loss. Ongoing research aims to enhance computing capabilities through the development of innovative chips and the adoption of new system architectures. One noteworthy advancement is Resistive Random Access Memory (RRAM), an emerging memory technology. RRAM can alter its resistance through electrical signals at both ends, retaining its state even after power-down. This technology holds promise in various areas, including logic computing, neural networks, brain-like computing, and integrated technologies combining sensing, storage, and computing. These cutting-edge technologies offer the potential to overcome the performance limitations of traditional architectures, significantly boosting computing power. This discussion explores the physical mechanisms, device structure, performance characteristics, and applications of RRAM devices. Additionally, we delve into the potential future adoption of these technologies at an industrial scale, along with prospects and upcoming research directions.

Unique Coexistence of Two Resistive Switching Modes in a Memristor Device Enables Multifunctional Neuromorphic Computing Properties

We report on hybrid memristor devices consisting of germanium dioxide nanoparticles (GeO2 NP) embedded within a poly(methyl methacrylate) (PMMA) thin film. Besides exhibiting forming-free resistive switching and an uncommon "ON" state in pristine conditions, the hybrid (nanocomposite) devices demonstrate a unique form of mixed-mode switching. The observed stopping voltage-dependent switching enables state-of-the-art bifunctional synaptic behavior with short-term (volatile/temporal) and long-term (nonvolatile/nontemporal) modes that are switchable depending on the stopping voltage applied. The short-term memory mode device is demonstrated to further emulate important synaptic functions such as short-term potentiation (STP), short-term depression (STD), paired-pulse facilitation (PPF), post-tetanic potentiation (PTP), spike-voltage-dependent plasticity (SVDP), spike-duration-dependent plasticity (SDDP), and, more importantly, the "learning-forgetting-rehearsal" behavior. The long-term memory mode gives additional long-term potentiation (LTP) and long-term depression (LTD) characteristics for long-term plasticity applications. The work shows a unique coexistence of the two resistive switching modes, providing greater flexibility in device design for future adaptive and reconfigurable neuromorphic computing systems at the hardware level.

Master Memory Function for Delay-Based Reservoir Computers With Single-Variable Dynamics

We show that many delay-based reservoir computers considered in the literature can be characterized by a universal master memory function (MMF). Once computed for two independent parameters, this function provides linear memory capacity for any delay-based single-variable reservoir with small inputs. Moreover, we propose an analytical description of the MMF that enables its efficient and fast computation. Our approach can be applied not only to single-variable delay-based reservoirs governed by known dynamical rules, such as the Mackey-Glass or Stuart-Landau-like systems, but also to reservoirs whose dynamical model is not available.

2Memristor-1Capacitor Integrated Temporal Kernel for High-Dimensional Data Mapping

Compact but precise feature-extracting ability is core to processing complex computational tasks in neuromorphic hardware. Physical reservoir computing (RC) offers a robust framework to map temporal data into a high-dimensional space using the time dynamics of a material system, such as a volatile memristor. However, conventional physical RC systems have limited dynamics for the given material properties, restricting the methods to increase their dimensionality. This study proposes an integrated temporal kernel composed of a 2-memristor and 1-capacitor (2M1C) using a W/HfO2/TiN memristor and TiN/ZrO2/Al2O3/ZrO2/TiN capacitor to achieve higher dimensionality and tunable dynamics. The kernel elements are carefully designed and fabricated into an integrated array, of which performances are evaluated under diverse conditions. By optimizing the time dynamics of the 2M1C kernel, each memristor simultaneously extracts complementary information from input signals. The MNIST benchmark digit classification task achieves a high accuracy of 94.3% with a (196×10) single-layer network. Analog input mapping ability is tested with a Mackey-Glass time series prediction, and the system records a normalized root mean square error of 0.04 with a 20×1 readout network, the smallest readout network ever used for Mackey-Glass prediction in RC. These performances demonstrate its high potential for efficient temporal data analysis.

Power-Efficient Multisensory Reservoir Computing Based on Zr-Doped HfO2 Memcapacitive Synapse Arrays

Hardware implementation tailored to requirements in reservoir computing would facilitate lightweight and powerful temporal processing. Capacitive reservoirs would boost power efficiency due to their ultralow static power consumption but have not been experimentally exploited yet. Here, this work reports an oxide-based memcapacitive synapse (OMC) based on Zr-doped HfO2 (HZO) for a power-efficient and multisensory processing reservoir computing system. The nonlinearity and state richness required for reservoir computing could originate from the capacitively coupled polarization switching and charge trapping of hafnium-oxide-based devices. The power consumption (≈113.4 fJ per spike) and temporal processing versatility outperform most resistive reservoirs. This system is verified by common benchmark tasks, and it exhibits high accuracy (>94%) in recognizing multisensory information, including acoustic, electrophysiological, and mechanic modalities. As a proof-of-concept, a touchless user interface for virtual shopping based on the OMC-based reservoir computing system is demonstrated, benefiting from its interference-robust acoustic and electrophysiological perception. These results shed light on the development of highly power-efficient human-machine interfaces and machine-learning platforms.

SnO2-Based Memory Device with Filamentary Switching Mechanism for Advanced Data Storage and Computing

In this study, we fabricate a Pt/TiN/SnOx/Pt memory device using reactive sputtering to explore its potential for neuromorphic computing. The TiON interface layer, formed when TiN comes into contact with SnO2, acts as an oxygen vacancy reservoir, aiding the creation of conductive filaments in the switching layer. Our SnOx-based device exhibits remarkable endurance, with over 200 DC cycles, ON/FFO ratio (>20), and 104 s retention. Set and reset voltage variabilities are impressively low, at 9.89% and 3.2%, respectively. Controlled negative reset voltage and compliance current yield reliable multilevel resistance states, mimicking synaptic behaviors. The memory device faithfully emulates key neuromorphic characteristics, encompassing both long-term potentiation (LTP) and long-term depression (LTD). The filamentary switching mechanism in the SnOx-based memory device is explained by an oxygen vacancy concentration gradient, where current transport shifts from Ohmic to Schottky emission dominance across different resistance states. These findings exemplify the potential of SnOx-based devices for high-density data storage memory and revolutionary neuromorphic computing applications.

Improved Uniformity of TaOx-Based Resistive Switching Memory Device by Inserting Thin SiO2 Layer for Neuromorphic System

RRAM devices operating based on the creation of conductive filaments via the migration of oxygen vacancies are widely studied as promising candidates for next-generation memory devices due to their superior memory characteristics. However, the issues of variation in the resistance state and operating voltage remain key issues that must be addressed. In this study, we propose a TaOx/SiO2 bilayer device, where the inserted SiO2 layer localizes the conductive path, improving uniformity during cycle-to-cycle endurance and retention. Transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy (XPS) confirm the device structure and chemical properties. In addition, various electric pulses are used to investigate the neuromorphic system properties of the device, revealing its good potential for future memory device applications.

A Review on the Progress of Optoelectronic Devices Based on TiO2 Thin Films and Nanomaterials

Titanium dioxide (TiO₂) is a kind of wide-bandgap semiconductor. Nano-TiO₂ devices exhibit size-dependent and novel photoelectric performance due to their quantum limiting effect, high absorption coefficient, high surface-volume ratio, adjustable band gap, etc. Due to their excellent electronic performance, abundant presence, and high cost performance, they are widely used in various application fields such as memory, sensors, and photodiodes. This article provides an overview of the most recent developments in the application of nanostructured TiO₂-based optoelectronic devices. Various complex devices are considered, such as sensors, photodetectors, light-emitting diodes (LEDs), storage applications, and field-effect transistors (FETs). This review of recent discoveries in TiO₂-based optoelectronic devices, along with summary reviews and predictions, has important implications for the development of transitional metal oxides in optoelectronic applications for researchers.

Nanoscale and ultra-high extinction ratio optical memristive switch based on plasmonic waveguide with square cavity

A resistive switch effect-based optical memristive switch with an ultra-high extinction ratio and ultra-compact size working at 1550 nm is proposed. The device is composed of a metal-insulator-metal waveguide and a square resonator with active electrodes. The formation and rupture of conductive filaments in the resonant cavity can alter the resonant wavelength, which triggers the state of the optical switch ON or OFF. The numerical results demonstrate that the structure has an ultra-compact size (less than 1 µm) and ultra-high extinction ratio (37 dB). The proposed device is expected to address the problems of high-power consumption and large-scale optical switches and can be adopted in optical switches, optical modulation, optical storage and computing, and large-scale photonic integrated devices.

Memristors with Nociceptor Characteristics Using Threshold Switching of Pt/HfO2/TaOx/TaN Devices

As artificial intelligence technology advances, it is necessary to imitate various biological functions to complete more complex tasks. Among them, studies have been reported on the nociceptor, a critical receptor of sensory neurons that can detect harmful stimuli. Although a complex CMOS circuit is required to electrically realize a nociceptor, a memristor with threshold switching characteristics can implement the nociceptor as a single device. Here, we suggest a memristor with a Pt/HfO2/TaOx/TaN bilayer structure. This device can mimic the characteristics of a nociceptor including the threshold, relaxation, allodynia, and hyperalgesia. Additionally, we contrast different electrical properties according to the thickness of the HfO2 layer. Moreover, Pt/HfO2/TaOx/TaN with a 3 nm thick HfO2 layer has a stable endurance of 1000 cycles and controllable threshold switching characteristics. Finally, this study emphasizes the importance of the material selection and fabrication method in the memristor by comparing Pt/HfO2/TaOx/TaN with Pt/TaOx/TaN, which has insufficient performance to be used as a nociceptor.

Conduction Mechanism Analysis of Abrupt- and Gradual-Switching InGaZnO Memristors

In this work, two types of InGaZnO (IGZO) memristors were fabricated to confirm the conduction mechanism and degradation characteristics of memristors with different electrode materials. The IGZO memristor exhibits abrupt switching characteristics with the Pd electrode owing to the formation and destruction of conductive filaments but shows gradual switching characteristics with the p-type Si electrode according to the amount of generated oxygen vacancy. The electrical characteristics and conduction mechanisms of the device are analyzed using an energy band diagram and experimentally verified with random telegraph noise characteristics confirming the trap effects on the device conduction.

Convolutional Neural Network Based on Crossbar Arrays of (Co-Fe-B)x(LiNbO3)100-x Nanocomposite Memristors

Convolutional neural networks (CNNs) have been widely used in image recognition and processing tasks. Memristor-based CNNs accumulate the advantages of emerging memristive devices, such as nanometer critical dimensions, low power consumption, and functional similarity to biological synapses. Most studies on memristor-based CNNs use either software models of memristors for simulation analysis or full hardware CNN realization. Here, we propose a hybrid CNN, consisting of a hardware fixed pre-trained and explainable feature extractor and a trainable software classifier. The hardware part was realized on passive crossbar arrays of memristors based on nanocomposite (Co-Fe-B)_x(LiNbO₃)_100-x structures. The constructed 2-kernel CNN was able to classify the binarized Fashion-MNIST dataset with ~ 84% accuracy. The performance of the hybrid CNN is comparable to the other reported memristor-based systems, while the number of trainable parameters for the hybrid CNN is substantially lower. Moreover, the hybrid CNN is robust to the variations in the memristive characteristics: dispersion of 20% leads to only a 3% accuracy decrease. The obtained results pave the way for the efficient and reliable realization of neural networks based on partially unreliable analog elements.

Compact SPICE Model of Memristor with Barrier Modulated Considering Short- and Long-Term Memory Characteristics by IGZO Oxygen Content

This paper introduces a compact SPICE model of a two-terminal memory with a Pd/Ti/IGZO/p⁺-Si structure. In this paper, short- and long-term components are systematically separated and applied in each model. Such separations are conducted by the applied bias and oxygen flow rate (OFR) during indium gallium zinc oxide (IGZO) deposition. The short- and long-term components in the potentiation and depression curves are modeled by considering the process (OFR of IGZO) and bias conditions. The compact SPICE model with the physical mechanism of SiO₂ modulation is introduced, which can be useful for optimizing the specification of memristor devices.

Aliovalent Ta-Doping-Engineered Oxygen Vacancy Configurations for Ultralow-Voltage Resistive Memory Devices: A DFT-Supported Experimental Study

Alteration of transport properties of any material, especially metal oxides, by doping suitable impurities is not straightforward as it may introduce multiple defects like oxygen vacancies (V_o) in the system. It plays a decisive role in controlling the resistive switching (RS) performance of metal oxide-based memory devices. Therefore, a judicious choice of dopants and their atomic concentrations is crucial for achieving an optimum V_o configuration. Here, we show that the rational designing of RS memory devices with cationic dopants (Ta), in particular, Au/Ti_1-xTa_xO_2-δ/Pt devices, is promising for the upcoming non-volatile memory technology. Indeed, a current window of ∼10⁴ is realized at an ultralow voltage as low as 0.25 V with significant retention (∼10⁴ s) and endurance (∼10⁵ cycles) of the device by considering 1.11 at % Ta doping. The obtained device parameters are compared with those in the available literature to establish its excellent performance. Furthermore, using detailed experimental analyses and density functional theory (DFT)-based first-principles calculations, we comprehend that the meticulous presence of V_o configurations and the columnar-like dendritic structures is crucial for achieving ultralow-voltage bipolar RS characteristics. In fact, the dopant-mediated V_o interactions are found to be responsible for the enhancement in local current conduction, as evidenced from the DFT-simulated electron localization function plots, and these, in turn, augment the device performance. Overall, the present study on cationic-dopant-controlled defect engineering could pave a neoteric direction for future energy-efficient oxide memristors.

Robust Resistive Switching Constancy and Quantum Conductance in High-k Dielectric-Based Memristor for Neuromorphic Engineering

For neuromorphic computing and high-density data storage memory, memristive devices have recently gained a lot of interest. So far, memristive devices have suffered from switching parameter instability, such as distortions in resistance values of low- and high-resistance states (LRSs and HRSs), dispersion in working voltage (set and reset voltages), and a small ratio of high and low resistance, among other issues. In this context, interface engineering is a critical technique for addressing the variation issues that obstruct the use of memristive devices. Herein, we engineered a high band gap, low Gibbs free energy Al₂O₃ interlayer between the HfO₂ switching layer and the tantalum oxy-nitride electrode (TaN) bottom electrode to operate as an oxygen reservoir, increasing the resistance ratio between HRS and LRS and enabling multilayer data storage. The Pt/HfO₂/Al₂O₃/TaN memristive device demonstrates analog bipolar resistive switching behavior with a potential ratio of HRS and LRS of > 10⁵ and the ability to store multi-level data with consistent retention and uniformity. On set and reset voltages, statistical analysis is used; the mean values (µ) of set and reset voltages are determined to be - 2.7 V and + 1.9 V, respectively. There is a repeatable durability over DC 1000 cycles, 10⁵ AC cycles, and a retention time of 10⁴ s at room temperature. Quantum conductance was obtained by increasing the reset voltage with step of 0.005 V with delay time of 0.1 s. Memristive device has also displayed synaptic properties like as potentiation/depression and paired-pulse facilitation (PPF). Results show that engineering of interlayer is an effective approach to improve the uniformity, ratio of high and low resistance, and multiple conductance quantization states and paves the way for research into neuromorphic synapses.

Bio-Inspired In-Sensor Compression and Computing Based on Phototransistors

The biological nervous system possesses a powerful information processing capability, and only needs a partial signal stimulation to perceive the entire signal. Likewise, the hardware implementation of an information processing system with similar capabilities is of great significance, for reducing the dimensions of data from sensors and improving the processing efficiency. Here, it is reported that indium-gallium-zinc-oxide thin film phototransistors exhibit the optoelectronic switching and light-tunable synaptic characteristics for in-sensor compression and computing. Phototransistor arrays can compress the signal while sensing, to realize in-sensor compression. Additionally, a reservoir computing network can also be implemented via phototransistors for in-sensor computing. By integrating these two systems, a neuromorphic system for high-efficiency in-sensor compression and computing is demonstrated. The results reveal that even for cases where the signal is compressed by 50%, the recognition accuracy of reconstructed signal still reaches ≈96%. The work paves the way for efficient information processing of human-computer interactions and the Internet of Things.

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.