The Old Polyoxometalates in New Application as Molecular Resistive Switching Memristors

The coming big-data era has created a huge demand for next-generation memory technologies with characters of higher data-storage densities, faster access speeds, lower power consumption and better environmental compatibility. In this field, the design of resistive switching active materials is pivotal but challengeable. Polyoxometalates (POMs) are promising candidates for next-generation molecular memristors due to their versatile redox characters, excellent electron reservoirs and good compatibility/convenience in microelectronics processing. In this review, five kinds of POM-based active materials in nonvolatile memories (inorganic POMs, crystalline organic-inorganic hybrid POMOFs, polymer modified POMs, POM/transition metal oxides composites and the deposition of POM on metal surfaces) were described. The components of POMs active materials, device fabrications, device parameters, and resistive switching mechanisms relative to their structures were summarized. Finally, challenges and future perspectives of POMs-based memristors were also presented.     

Neurotransmitter-Mediated Plasticity in 2D Perovskite Memristor for Reinforcement Learning

The neuromorphic computing architecture is a promising artificial intelligence for implementing hierarchical processing, in-memory computing, event-driven operation and functional specialization in computing systems. However, current investigations mainly focus on unisensory processing without objective experience which is contrary to the flexible sensory learning capability in the human brain that can sense and process information according to the ever-changing environment. For example, a dominant paradigm for reconfigurable bio-learning features is the emotional experience. The neurotransmitter dopamine is released during arousal, influencing the vital brain functions involved in cognition, reward learning, movement and motivation. Here, the on-demand configuration of a biorealistic synaptic connection based on a 2D CaTa₂O₇ (CTO) device is demonstrated that can be adaptively reconfigured for a reinforcement learning purpose by the light-active resistive switching, which originated from the photon-regulated metaplasticity. The low energy consumption of 12.4 fJ endows the reinforcement learning system with high power efficiency and reliability. Finally, in-sensor computing with a CTO synapse is implemented with a filtering function to process digital data in a neuromorphic engineering manner. This work demonstrates the feasibility of 2D perovskite neuromorphic device with enhanced biological plausibility in the approaching post-Moore era.     

Parallel Density-Based Spatial Clustering with Dual-Functional Memristive Crossbar Array

Analog and digital switching performances in a Ta/HfO₂/RuO₂ (THR) memristor are studied to implement a density-based spatial clustering of applications with noise (DBSCAN) algorithm in a low-power, parallel-computing memristor crossbar structure. In the analog mode THR memristor, more than 256 states can be stored through a fine-tuning process with a denoising scheme. The analog mode crossbar array facilitates Euclidean distance calculation between any points in the given graphic dataset. In the digital mode, the on/off ratio of more than three orders of magnitude between the binary states is achieved, providing functionality to cluster the data points with a reduced number of operations. The parallel computing capacity of the adopted crossbar decreases the time complexity of the original DBSCAN from O(n²) to O(n). Through array-level simulations, the effectiveness of hardware functionality is validated using representative synthetic datasets and single-cell RNA sequences datasets.     

Performance Enhancement and In Situ Observation of Resistive Switching and Magnetic Modulation by a Tunable Two-Level System of Mn Dopants in a-Gallium Oxide-based Memristor

Purely gallium oxide-based memristors (GOMRs) show great potentials in resistive random-access-memory (RRAM) due to their chemical stability and resistive switching characteristics with R_off/R_on ratios up to 10²; indeed, GOMRs with higher R_off/R_on ratios and more functionalities are more expected. In this study, ferromagnetic amorphous gallium oxide (a-GMO) films with a tunable two-level system of Mn dopants, i.e., Mn²⁺ and Mn³⁺ ions, are prepared by scalable polymer assisted deposition. The Pt/a-GMO/Pt memristors show a high R_off/R_on ratio of 10³, at least one order of magnitude higher than those of previously reported purely GOMRs, thanks to the abundant oxygen vacancies (V_Os)-induced low resistance state and Mn²⁺-enhanced high resistance state. Meanwhile, magnetic modulation (MM) is realized electrically in the a-GOMRs during the RS, through the tuning of bound magnetopolarons (BMPs) by bias voltage-induced V_Os variations, which may be useful for quaternary information coding. Notably, the transition between Mn³⁺ and Mn²⁺ions is observed in the GOMRs, which is closely related to the variations of V_O concentration and BMP amount, providing an in situ tool to probe the V_O-induced RS and BMP-dependent MM. The results give insights to Mn-doped GOMRs and may be useful for design, fabrication, and testing of multifunctional high-performance RRAMs.     

High-Throughput Screening Thickness-Dependent Resistive Switching in SrTiO3 Thin Films for Robust Electronic Synapse

The functionalities and applications of oxide thin films are highly dependent on their thickness. Most thickness-dependent studies on oxide thin films require the preparation of independent samples, which is labor-intensive and time-consuming and inevitably introduces experimental errors. To address this challenge, a general strategy based on high-throughput pulsed laser deposition technology is proposed to precisely control the thin-film thickness in local regions under similar growth conditions. The as-proposed synthesis strategy is demonstrated using typical complex oxide materials of SrTiO₃ (STO). Consequently, high-throughput STO thin films with nine gradient thicknesses ranging from 10.1 to 30.5 nm are fabricated. Notably, a transition from the unipolar to the bipolar resistive switching mode is observed with increasing STO thickness. Moreover, a physical mechanism based on the heterostructure-mediated redistribution of oxygen vacancies is employed to interpret the transition between the two memristive patterns. The screening of STO thin films with different resistive switching behaviors revealed that the STO thin film with a thickness of 20.3 nm exhibit excellent conductance modulation properties under the application of electrical pulses as well as significant reliability for the emulation of various synaptic functions, rendering it a promising material for artificial neuromorphic computing applications.     

Role of Metal Contacts on Halide Perovskite Memristors

Halide perovskites are promising candidates for resistive memories (memristors) due to their mixed electronic/ionic conductivity and the real activation mechanism is currently under debate. In order to unveil the role of the metal contact and its connection with the activation process, four model systems are screened on halide perovskite memristors: Nearly inert metals (Au and Pt), low reactivity contacts (Cu), highly reactive contact (Ag and Al), and pre-oxidized metal in the form of AgI. It is revealed that the threshold voltage for activation of the memory effect is highly connected with the electrochemical activity of the metals. Redox/capacitive peaks are observed for reactive metals at positive potentials and charged ions are formed that can follow the electrical field. Activation proceeds by formation of conductive filaments, either by the direct migration of the charged metals or by an increase in the concentration of halide vacancies generated by this electrochemical reaction. Importantly, the use of pre-oxidized Ag⁺ ions leads to very low threshold voltages of ≈0.2 V indicating that an additional electrochemical reaction is not needed in this system to activate the memristor. Overall, the effect of the metal contact is clarified, and it is revealed that AgI is a very promising interfacial layer for low-energy applications.     

N-P Reconfigurable Dual-Mode Memtransistors for Compact Bio-Inspired Feature Extractor with Inhibitory-Excitatory Spiking Capability

Competitive-learning-based spiking neural networks are capable of rapid, highly accurate pattern recognition with minimal data through denoising mechanisms provide by adaptive interneuron inhibition. However, hardware implementations of such networks are currently area-inefficient due to the high device count require to execute dual excitatory-inhibitory synapses. To mitigate this, n-/p- reconfigurable tungsten diselenide memtransistors is introduced that can execute excitatory and inhibitory synapses in a highly compact bio-inspired feature extractor hardware architecture. The reconfigurability is realized through a dual mode memory device with a flash-memory-like floating-gate for n-/p- programing and a memristor-like selenium vacancy-based resistive switching that varies in memristive output with majority carrier modulation. Through a device-system codesign, an effective 27% device count reduction in the peripheral circuits is achieved , which ameliorates circuit component congestion and circuit complexity. Compared to the prevalent winner-takes-all approach, the proposed machine learning with adaptive interneuron inhibition achieves high-accuracy convergence with up to five times smaller training dataset. This accelerated learning can potentially enable edge-artificial intelligence (AI) processors capable of ultra-low-energy training with limited data.     

Controlling Threshold and Resistive Switch Functionalities in Ag-Incorporated Organometallic Halide Perovskites for Memristive Crossbar Array

Halide perovskites (HPs) can be the effective functional materials for the sneak-path current issue in the memristive crossbar array. Herein, an efficient strategy is proposed to integrate the HPs-based bidirectional threshold and bipolar resistive switches (TS and RS). The resistance change characteristics from volatile threshold to nonvolatile resistive switching are modulated by controlling Ag doping concentration in the MAPbI₃. HPs provide the diffusive condition and the quantity of Ag regulates the radius of its network. A low amount of Ag contributes to weak network with a short lifetime. However, when the amount of Ag increases, the conductive filament becomes more robust, showing a long lifetime. A MAPbI₃:Ag TS with a low Ag content is developed, showing a steep switching slope (1 mV per decade), fast switching speed (< 80 ns), and low off-current (10 nA). And, a MAPbI₃:Ag RS with a high Ag content is developed, showing multilevel storage capability and long retention time (1400 s). Finally, these TS and RS coupled into the 1S-1R integrated component, resulting the development of the maximum crossbar array size to 1.4 × 10¹². This study offers an efficient methodology for tailoring the resistance change characteristics and a promising strategy for practical HPs-based memristive crossbar application.