Lead-Free Dual-Phase Halide Perovskites for Preconditioned Conducting-Bridge Memory

Electrochemical Random-Access Memory: Progress, Perspectives, and Opportunities

Non-von Neumann computing using neuromorphic systems based on analogue synaptic and neuronal elements has emerged as a potential solution to tackle the growing need for more efficient data processing, but progress toward practical systems has been stymied due to a lack of materials and devices with the appropriate attributes. Recently, solid state electrochemical ion-insertion, also known as electrochemical random access memory (ECRAM) has emerged as a promising approach to realize the needed device characteristics. ECRAM is a three terminal device that operates by tuning electronic conductance in functional materials through solid-state electrochemical redox reactions. This mechanism can be considered as a gate-controlled bulk modulation of dopants and/or phases in the channel. Early work demonstrating that ECRAM can achieve nearly ideal analogue synaptic characteristics has sparked tremendous interest in this approach. More recently, the realization that electrochemical ion insertion can be used to tune the electronic properties of many types of materials including transition metal oxides, layered two-dimensional materials, organic and coordination polymers, and that the changes in conductance can span orders of magnitude has further attracted interest in ECRAM as the basis for analogue synaptic elements for inference accelerators as well as for dynamical devices that can emulate a wide range of neuronal characteristics for implementation in analogue spiking neural networks. At its core, ECRAM shares many fundamental aspects with rechargeable batteries, where ion insertion materials are used extensively for their ability to reversibly store charge and energy. Computing applications, however, present drastically different requirements: systems will require many millions of devices, scaled down to tens of nanometers, all while achieving reliable electronic-state tuning at scaled-up rates and endurances, and with minimal energy dissipation and noise. In this review, we discuss the history, basic concepts, recent progress, as well as the challenges and opportunities for different types of ECRAM, broadly grouped by their primary mobile ionic charge carrier, including Li, protons, and oxygen vacancies.

Memristive Ion Dynamics to Enable Biorealistic Computing

Conventional artificial intelligence (AI) systems are facing bottlenecks due to the fundamental mismatches between AI models, which rely on parallel, in-memory, and dynamic computation, and traditional transistors, which have been designed and optimized for sequential logic operations. This calls for the development of novel computing units beyond transistors. Inspired by the high efficiency and adaptability of biological neural networks, computing systems mimicking the capabilities of biological structures are gaining more attention. Ion-based memristive devices (IMDs), owing to the intrinsic functional similarities to their biological counterparts, hold significant promise for implementing emerging neuromorphic learning and computing algorithms. In this article, we review the fundamental mechanisms of IMDs based on ion drift and diffusion to elucidate the origins of their diverse dynamics. We then examine how these mechanisms operate within different materials to enable IMDs with various types of switching behaviors, leading to a wide range of applications, from emulating biological components to realizing specialized computing requirements. Furthermore, we explore the potential for IMDs to be modified and tuned to achieve customized dynamics, which positions them as one of the most promising hardware candidates for executing bioinspired algorithms with unique specifications. Finally, we identify the challenges currently facing IMDs that hinder their widespread usage and highlight emerging research directions that could significantly benefit from incorporating IMDs.

Linearly programmable two-dimensional halide perovskite memristor arrays for neuromorphic computing

The exotic properties of three-dimensional halide perovskites, such as mixed ionic-electronic conductivity and feasible ion migration, have enabled them to challenge traditional memristive materials. However, the poor moisture stability and difficulty in controlling ion transport due to their polycrystalline nature have hindered their use as a neuromorphic hardware. Recently, two-dimensional (2D) halide perovskites have emerged as promising artificial synapses owing to their phase versatility, microstructural anisotropy in electrical and optoelectronic properties, and excellent moisture resistance. However, their asymmetrical and nonlinear conductance changes still limit the efficiency of training and accuracy of inference. Here we achieve highly linear and symmetrical conductance changes in Dion-Jacobson 2D perovskites. We further build a 7 × 7 crossbar array based on analogue perovskite synapses, achieving a high device yield, low variation with synaptic weight storing capability, multi-level analogue states with long retention, and moisture stability over 7 months. We explore the potential of such devices in large-scale image inference via simulations and show an accuracy within 0.08% of the theoretical limit. The excellent device performance is attributed to the elimination of gaps between inorganic layers, allowing the halide vacancies to migrate homogeneously regardless of grain boundaries. This was confirmed by first-principles calculations and experimental analysis.

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.

Halide Perovskites-Based Diffusive Memristors for Artificial Mechano-Nociceptive System

Numerous efforts for emulating organ systems comprised of multiple functional units have driven substantial advancements in bio-realistic electronics and systems. The resistance change behavior observed in diffusive memristors shares similarities with the potential change in biological neurons. Here, the diffusive threshold switching phenomenon in Ag-incorporated organometallic halide perovskites is utilized to demonstrate the functions of afferent neurons. Halide perovskites-based diffusive memristors show a low threshold voltage of ≈0.2 V with little variation, attributed to the facile migration of Ag ions uniformly dispersed within the halide matrix. Based on the reversible and reliable volatile threshold switching, the memristors successfully demonstrate fundamental nociceptive functions including threshold firing, relaxation, and sensitization. Furthermore, to replicate the biological mechano-nociceptive phenomenon at a system level, an artificial mechano-nociceptive system is built by integrating a diffusive memristor with a force-sensing resistor. The presented system is capable of detecting and discerning the detrimental impact caused by a heavy steel ball, effectively exhibiting the corresponding sensitization response. By further extending the single nociceptive system into a 5 × 5 array, successful stereoscopic nociception of uneven impulses is achieved in the artificial skin system through array-scale sensitization. These results represent significant progress in the field of bio-inspired electronics and systems.

Advances in the Application of Perovskite Materials

Nowadays, the soar of photovoltaic performance of perovskite solar cells has set off a fever in the study of metal halide perovskite materials. The excellent optoelectronic properties and defect tolerance feature allow metal halide perovskite to be employed in a wide variety of applications. This article provides a holistic review over the current progress and future prospects of metal halide perovskite materials in representative promising applications, including traditional optoelectronic devices (solar cells, light-emitting diodes, photodetectors, lasers), and cutting-edge technologies in terms of neuromorphic devices (artificial synapses and memristors) and pressure-induced emission. This review highlights the fundamentals, the current progress and the remaining challenges for each application, aiming to provide a comprehensive overview of the development status and a navigation of future research for metal halide perovskite materials and devices.

Multilevel resistive switching in stable all-inorganic n-i-p double perovskite memristor

Memristors are promising information storage devices for commercial applications because of their long endurance and low power consumption. Particularly, perovskite memristors have revealed excellent resistive switching (RS) properties owing to the fast ion migration and solution fabrication process. Here, an n-i-p type double perovskite memristor with "ITO/SnO₂/Cs₂AgBiBr₆/NiO_x/Ag" architecture was developed and demonstrated to reveal three resistance states because of the p-n junction electric field coupled with ion migration. The devices exhibited reliable filamentary with an on/off ratio exceeding 50. The RS characteristics remained unchanged after 1000 s read and 300 switching cycles. The synaptic functions were examined through long-term depression and potentiation measurements. Significantly, the device still worked after one year to reveal long-term stability because of the all-inorganic layers. This work indicates a novel idea for designing a multistate memristor by utilizing the p-n junction unidirectional conductivity during the forward and reverse scanning.

The Role of Polymers in Halide Perovskite Resistive Switching Devices

Currently, halide perovskites (HPs) are gaining traction in multiple applications, such as photovoltaics and resistive switching (RS) devices. In RS devices, the high electrical conductivity, tunable bandgap, good stability, and low-cost synthesis and processing make HPs promising as active layers. Additionally, the use of polymers in improving the RS properties of lead (Pb) and Pb-free HP devices was described in several recent reports. Thus, this review explored the in-depth role of polymers in optimizing HP RS devices. In this review, the effect of polymers on the ON/OFF ratio, retention, and endurance properties was successfully investigated. The polymers were discovered to be commonly utilized as passivation layers, charge transfer enhancement, and composite materials. Hence, further HP RS improvement integrated with polymers revealed promising approaches to delivering efficient memory devices. Based on the review, detailed insights into the significance of polymers in producing high-performance RS device technology were effectively understood.

Advances in Emerging Photonic Memristive and Memristive-Like Devices

Possessing the merits of high efficiency, low consumption, and versatility, emerging photonic memristive and memristive-like devices exhibit an attractive future in constructing novel neuromorphic computing and miniaturized bionic electronic system. Recently, the potential of various emerging materials and structures for photonic memristive and memristive-like devices has attracted tremendous research efforts, generating various novel theories, mechanisms, and applications. Limited by the ambiguity of the mechanism and the reliability of the material, the development and commercialization of such devices are still rare and in their infancy. Therefore, a detailed and systematic review of photonic memristive and memristive-like devices is needed to further promote its development. In this review, the resistive switching mechanisms of photonic memristive and memristive-like devices are first elaborated. Then, a systematic investigation of the active materials, which induce a pivotal influence in the overall performance of photonic memristive and memristive-like devices, is highlighted and evaluated in various indicators. Finally, the recent advanced applications are summarized and discussed. In a word, it is believed that this review provides an extensive impact on many fields of photonic memristive and memristive-like devices, and lay a foundation for academic research and commercial applications.

Room-Temperature Fabricated Multilevel Nonvolatile Lead-Free Cesium Halide Memristors for Reconfigurable In-Memory Computing

Recently, conductive-bridging memristors based on metal halides, such as halide perovskites, have been demonstrated as promising components for brain-inspired hardware-based neuromorphic computing. However, realizing devices that simultaneously fulfill all of the key merits (low operating voltage, high dynamic range, multilevel nonvolatile storage capability, and good endurance) remains a great challenge. Herein, we describe lead-free cesium halide memristors incorporating a MoO_X interfacial layer as a type of conductive-bridging memristor. With this design, we obtained highly uniform and reproducible memristors that exhibited all-around resistive switching characteristics: ultralow operating voltages (<0.18 V), low variations (<30 mV), long retention times (>10⁶ s), high endurance (>10⁵, full on/off cycles), record-high on/off ratios (>10¹⁰, smaller devices having areas <5 × 10^-4 mm²), fast switching (<200 ns), and multilevel programming abilities (>64 states). With these memristors, we successfully implemented stateful logic functions in a reconfigurable architecture and accomplished a high classification accuracy (ca. 90%) in the simulated hand-written-digits classification task, suggesting their versatility in future in-memory computing applications. In addition, we exploited the room-temperature fabrication of the devices to construct a fully functional three-dimensional stack of memristors, which demonstrates their potential of high-density integration desired for data-intensive neuromorphic computing. High-performance, environmentally friendly cesium halide memristors provide opportunities toward next-generation electronics beyond von Neumann architectures.

Ambient Stable All Inorganic CsCu2I3 Artificial Synapses for Neurocomputing

In resistive switching memories or artificial synaptic devices, halide perovskites have attracted attention for their unusual features such as rapid ion migration, adjustable composition, and facile synthesis. Herein, the environmentally friendly and highly air stable CsCu₂I₃ perovskite films are used as the active layer in the Au/CsCu₂I₃/ITO/glass artificial synapses. The device shows variable synaptic plasticities such as long-term and short-term synaptic plasticity, paired-pulse facilitation, and spike-timing-dependent plasticity by combining potentiation and depression along the formation of conductive filaments. The performances of the devices are maintained for 160 days under ambient conditions. Additionally, the accuracy evaluation of the CsCu₂I₃-based artificial synapses performs exceptionally well with the MNIST and Fashion MNIST data sets, demonstrating high learning accuracy in deep neural networks. Using the novel B-site engineered halide perovskite material with extreme air stability, this study paves the way for artificial synaptic devices for next-generation in-memory hardware.

Dimension-Dependent Bandgap Narrowing and Metallization in Lead-Free Halide Perovskite Cs3Bi2X9 (X = I, Br, and Cl) under High Pressure

Low-toxicity, air-stable cesium bismuth iodide Cs3Bi2X9 (X = I, Br, and Cl) perovskites are gaining substantial attention owing to their excellent potential in photoelectric and photovoltaic applications. In this work, the lattice constants, band structures, density of states, and optical properties of the Cs3Bi2X9 under high pressure perovskites are theoretically studied using the density functional theory. The calculated results show that the changes in the bandgap of the zero-dimensional Cs3Bi2I9, one-dimensional Cs3Bi2Cl9, and two-dimensional Cs3Bi2Br9 perovskites are 3.05, 1.95, and 2.39 eV under a pressure change from 0 to 40 GPa, respectively. Furthermore, it was found that the optimal bandgaps of the Shockley-Queisser theory for the Cs3Bi2I9, Cs3Bi2Br9, and Cs3Bi2Cl9 perovskites can be reached at 2-3, 21-26, and 25-29 GPa, respectively. The Cs3Bi2I9 perovskite was found to transform from a semiconductor into a metal at a pressure of 17.3 GPa. The lattice constants, unit-cell volume, and bandgaps of the Cs3Bi2X9 perovskites exhibit a strong dependence on dimension. Additionally, the Cs3Bi2X9 perovskites have large absorption coefficients in the visible region, and their absorption coefficients undergo a redshift with increasing pressure. The theoretical calculation results obtained in this work strengthen the fundamental understanding of the structures and bandgaps of Cs3Bi2X9 perovskites at high pressures, providing a theoretical support for the design of materials under high pressure.

Halide Perovskites for Memristive Data Storage and Artificial Synapses

Halide perovskites have been noted for their exotic properties such as fast ion migration, tunable composition, facile synthetic routes, and flexibility in addition to large light absorption coefficients, long carrier diffusion lengths, and high defect tolerance. These properties have made halide perovskites promising materials for memristors. Applications in the field of resistive switching memory devices and artificial synapses for neuromorphic computing are especially noteworthy. This Perspective covers state-of-the-art perovskite-based memristive devices. Moreover, the fundamental mechanisms and characteristics of perovskite-based memristors are elucidated. Interesting opportunities to improve the performance of perovskite-based memristors for commercialization are provided, including improving film uniformity and air stability, controlling the stoichiometry, finding new all-inorganic and lead-free halide perovskites, and making perovskites into single crystals or quantum dots. We expect our Perspective to be the foundation of realizing next-generation halide perovskite-based memristors.

Optical and Structural Property Tuning in Physical Vapor Deposited Bismuth Halides Cs3Bi2(I1-xBrx)9 (0 ≤ x ≤ 1)

Crystalline films of lead-free all-inorganic Cs3Bi2X9 (X = Br, I) perovskites have been deposited by radio frequency (RF)-magnetron sputtering providing high-quality, single-phase films as confirmed by structural, morphological, and optical property characterization. Progressive tuning of crystal structure characteristics and optical absorbance has been achieved in mixed Br/I phases Cs3Bi2(I1-xBrx)9 (0 ≤ x ≤ 1), highlighting a shift of the band gap from about 2.0 eV for Cs3Bi2I9 to 2.64 eV for Cs3Bi2Br9. X-ray diffraction and Raman scattering allowed defining the range of alloyed compositions where single-phase compositions are found. Finally, preliminary photocatalytic activity tests on the degradation of methylene blue provided solid data indicating the future possible exploitation of Bi-based perovskite derivative materials as active photocatalysts.

Advances in Halide Perovskite Memristor from Lead-Based to Lead-Free Materials

Memristors have attracted considerable attention as one of the four basic circuit elements besides resistors, capacitors, and inductors. Especially, the nonvolatile memory devices have become a promising candidate for the new-generation information storage, due to their excellent write, read, and erase rates, in addition to the low-energy consumption, multistate storage, and high scalability. Among them, halide perovskite (HP) memristors have great potential to achieve low-cost practical information storage and computing. However, the usual lead-based HP memristors face serious problems of high toxicity and low stability. To alleviate the above issues, great effort has been devoted to develop lead-free HP memristors. Here, we have summarized and discussed the advances in HP memristors from lead-based to lead-free materials including memristive properties, stability, neural network applications, and memristive mechanism. Finally, the challenges and prospects of lead-free HP memristors have been discussed.