Laser-Assisted Interface Engineering for Functional Interfacial Layer of Al/ZnO/Al Resistive Random Access Memory (RRAM)

Forming-Free, Low-Voltage, and High-Speed Resistive Switching in Ag/Oxygen-Deficient Vanadium Oxide(VOx)/Pt Device through Two-Step Resistance Change by Ag Filament Formation

Forming-free, low-voltage, and high-speed resistive switching is demonstrated in an Ag/oxygen-deficient vanadium oxide (VOx)/Pt device via the facilitated formation and rupture of Ag filaments. Direct current (DC) voltage sweep measurements exhibit forming-free switching from a high-resistance state (HRS) to a low-resistance state (LRS), called SET, at an average VSET of +0.23 V. The reverse RESET transition occurs at an average VRESET of -0.07 V with a low RESET current of <1 mA. Reversible switching operations are stable with an HRS/LRS resistance ratio >103 during repeated measurements for thousands of cycles. In pulse measurements, switching occurs within 100 ns at an amplitude of +1.5 V. Notably, a two-step resistance change is observed in the SET operation, where the resistance first partially decreases due to Ag+ ion accumulation in VOx and then further decreases to the LRS after hundreds of nanoseconds upon complete filament formation. The VOx layer deposited to be mostly amorphous with oxygen deficiency from V2O5 has abundant vacancies and expedites Ag+ ion migration, thus realizing forming-free, high-speed, and low-voltage switching. These characteristics of the facilitated Ag filament formation using the substoichiometric VOx layer are highly beneficial for use as stand-alone nonvolatile memory and in-memory computing elements.

Enhanced linear and symmetric synaptic weight update characteristics in a Pt/p-LiCoOx/p-NiO/Pt memristor through interface energy barrier modulation by Li ion redistribution

Artificial synaptic devices have been extensively investigated for neuromorphic computing systems, which require synaptic behaviors mimicking the biological ones. In particular, a highly linear and symmetric weight update with a conductance (or resistance) change for potentiation and depression operation is one of the essential requirements for energy-efficient neuromorphic computing; however, it is not sufficiently met. In this study, a memristor with a Pt/p-LiCoOx/p-NiO/Pt structure is investigated, where a low interface energy barrier between the Pt electrode and the NiO layer makes for a more linear and symmetric conductance change. In addition, the use of voltage-driven Li+ ion redistribution in the NiO layer facilitates the analog conductance change at a low voltage. Besides the linear and symmetric potentiation and depression weight updates, the memristor exhibits various synaptic characteristics such as the dependence of weight update on the pulse amplitude and number, paired pulse facilitation, and short-term and long-term plasticity. The conductance modulation is thought to be induced by a tunable interface energy barrier at the NiO layer and Pt bottom electrode, as a result of Li+ ion diffusion in NiO supplied from the LiCoOx layer and their redistribution. Thanks to the use of Li+ ion redistribution, the conductance change could be achieved at a voltage <4 V within the time of μs range. These results verify the potential of artificial synapses with the Pt/LiCoOx/NiO/Pt memristor operated by voltage-driven Li+ ion redistribution under the low interface energy barrier conditions, realizing a highly linear and symmetric weight update at a low voltage with a high speed for energy-efficient neuromorphic computing systems.

Artificial sensory system based on memristive devices

In the biological nervous system, the integration and cooperation of parallel system of receptors, neurons, and synapses allow efficient detection and processing of intricate and disordered external information. Such systems acquire and process environmental data in real-time, efficiently handling complex tasks with minimal energy consumption. Memristors can mimic typical biological receptors, neurons, and synapses by implementing key features of neuronal signal-processing functions such as selective adaption in receptors, leaky integrate-and-fire in neurons, and synaptic plasticity in synapses. External stimuli are sensitively detected and filtered by "artificial receptors," encoded into spike signals via "artificial neurons," and integrated and stored through "artificial synapses." The high operational speed, low power consumption, and superior scalability of memristive devices make their integration with high-performance sensors a promising approach for creating integrated artificial sensory systems. These integrated systems can extract useful data from a large volume of raw data, facilitating real-time detection and processing of environmental information. This review explores the recent advances in memristor-based artificial sensory systems. The authors begin with the requirements of artificial sensory elements and then present an in-depth review of such elements demonstrated by memristive devices. Finally, the major challenges and opportunities in the development of memristor-based artificial sensory systems are discussed.

Highly Reliable 3D Channel Memory and Its Application in a Neuromorphic Sensory System for Hand Gesture Recognition

Brain-inspired neuromorphic computing systems, based on a crossbar array of two-terminal multilevel resistive random-access memory (RRAM), have attracted attention as promising technologies for processing large amounts of unstructured data. However, the low reliability and inferior conductance tunability of RRAM, caused by uncontrollable metal filament formation in the uneven switching medium, result in lower accuracy compared to the software neural network (SW-NN). In this work, we present a highly reliable CoOx-based multilevel RRAM with an optimized crystal size and density in the switching medium, providing a three-dimensional (3D) grain boundary (GB) network. This design enhances the reliability of the RRAM by improving the cycle-to-cycle endurance and device-to-device stability of the I-V characteristics with minimal variation. Furthermore, the designed 3D GB-channel RRAM (3D GB-RRAM) exhibits excellent conductance tunability, demonstrating high symmetricity (624), low nonlinearity (βLTP/βLTD ∼ 0.20/0.39), and a large dynamic range (Gmax/Gmin ∼ 31.1). The cyclic stability of long-term potentiation and depression also exceeds 100 cycles (105 voltage pulses), and the relative standard deviation of Gmax/Gmin is only 2.9%. Leveraging these superior reliability and performance attributes, we propose a neuromorphic sensory system for finger motion tracking and hand gesture recognition as a potential elemental technology for the metaverse. This system consists of a stretchable double-layered photoacoustic strain sensor and a crossbar array neural network. We perform training and recognition tasks on ultrasonic patterns associated with finger motion and hand gestures, attaining a recognition accuracy of 97.9% and 97.4%, comparable to that of SW-NN (99.8% and 98.7%).

Resistive random access memory: introduction to device mechanism, materials and application to neuromorphic computing

The modern-day computing technologies are continuously undergoing a rapid changing landscape; thus, the demands of new memory types are growing that will be fast, energy efficient and durable. The limited scaling capabilities of the conventional memory technologies are pushing the limits of data-intense applications beyond the scope of silicon-based complementary metal oxide semiconductors (CMOS). Resistive random access memory (RRAM) is one of the most suitable emerging memory technologies candidates that have demonstrated potential to replace state-of-the-art integrated electronic devices for advanced computing and digital and analog circuit applications including neuromorphic networks. RRAM has grown in prominence in the recent years due to its simple structure, long retention, high operating speed, ultra-low-power operation capabilities, ability to scale to lower dimensions without affecting the device performance and the possibility of three-dimensional integration for high-density applications. Over the past few years, research has shown RRAM as one of the most suitable candidates for designing efficient, intelligent and secure computing system in the post-CMOS era. In this manuscript, the journey and the device engineering of RRAM with a special focus on the resistive switching mechanism are detailed. This review also focuses on the RRAM based on two-dimensional (2D) materials, as 2D materials offer unique electrical, chemical, mechanical and physical properties owing to their ultrathin, flexible and multilayer structure. Finally, the applications of RRAM in the field of neuromorphic computing are presented.

Nonvolatile Ternary Memristor Based on Fluorene-Benzimidazole Copolymer/Au NP Composites

A donor-acceptor type polymer of poly [2,7-(9,9-dioctyl)-fluorene-alt-7H-benzimidazo-[2,1-a]benzo[de]isoquinolin-7-one] (PF-BBO) based on benzimidazole groups was synthesized. This material was incorporated into data storage devices that exhibited good data storage characteristics. In order to improve the storage properties of the device, Au NPs were compounded in this material. We observed an increase in the ratio of switching current for the device with the PF-BBO/Au NP composite as the active layer. The device comprising 8 wt% Au NPs demonstrated optimal storage performance with a switching current ratio of 1:3.4 × 10²:1.0 × 10⁵ and a threshold voltage of -0.40 V/-0.85 V, respectively. The number of cycle times of this device was over 3000, which indicates excellent stability. Thus, the devices containing PF-BBO/Au NP composite as active materials offer a new dimension for future application prospects of high-density data storage.

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.

Configurable switching behavior in polymer-based resistive memories by adopting unique electrode/electrolyte arrangement

The difference in resistive switching characteristics by modifying the device configuration provides a unique operating principle, which is essential for both fundamental studies and the development of future memory devices. Here, we demonstrate the poly(methyl methacrylate) (PMMA)-based resistive switching characteristics using four different combinations of electrode/electrolyte arrangement in the device geometry. From the current-voltage (I-V) measurements, all the PMMA-based devices revealed nonvolatile memory behavior with a higher ON/OFF resistance ratio (∼105-107). Significantly, the current conduction in the filament and resistive switching behavior depend majorly on the presence of Al electrode and electrochemically active silver (Ag) element in the PMMA matrix. A trap-controlled space charge limited conduction (SCLC) mechanism constitutes the resistive switching in the Al/PMMA/Al device, whereas the current conduction governed by ohmic behavior influences the resistive switching in the Ag-including devices. The depth-profiling X-ray photoelectron spectroscopy (XPS) study evidences the conducting filament formation processes in the PMMA-based devices. These results with different conduction mechanisms provide further insights into the understanding of the resistive switching behavior in the polymer-based devices by simply rearranging the device configuration.