Asymmetric carrier transport in flexible interface-type memristor enables artificial synapses with sub-femtojoule energy consumption

Low-Power Memristor for Neuromorphic Computing: From Materials to Applications

As an emerging memory device, memristor shows great potential in neuromorphic computing applications due to its advantage of low power consumption. This review paper focuses on the application of low-power-based memristors in various aspects. The concept and structure of memristor devices are introduced. The selection of functional materials for low-power memristors is discussed, including ion transport materials, phase change materials, magnetoresistive materials, and ferroelectric materials. Two common types of memristor arrays, 1T1R and 1S1R crossbar arrays are introduced, and physical diagrams of edge computing memristor chips are discussed in detail. Potential applications of low-power memristors in advanced multi-value storage, digital logic gates, and analogue neuromorphic computing are summarized. Furthermore, the future challenges and outlook of neuromorphic computing based on memristor are deeply discussed.

Operating Mechanism Principles and Advancements for Halide Perovskite-Based Memristors and Neuromorphic Devices

With the advent of the generation of artificial intelligence (AI) based on big data-processing technologies, next-generation memristor and memristive neuromorphic devices have been actively studied with great interest to overcome the von Neumann bottleneck limits. Among various candidates, halide perovskites (HPs) have been in the spotlight as potential candidates for these devices due to their unique switching characteristics with low energy consumption and flexible integration compatibility across various sources for scalability. We outline the characteristics and operating principles of HP-based memristors and their neuromorphic devices. We explain filamentary- and interface-type switching according to the type of conducting pathway occurring inside the active HP layer and the operating mechanisms depending on the species that make up this conducting pathway. We summarize the types and mechanisms of current changes beneficial for neuromorphic device applications and finally organize various suggested analysis tools and physical models to enable experimental determination of switching mechanisms from various perspectives.

Artificial Synapse Based on a δ-FAPbI3/Atomic-Layer-Deposited SnO2 Bilayer Memristor

Halide perovskite-based resistive switching memory (memristor) has potential in an artificial synapse. However, an abrupt switch behavior observed for a formamidinium lead triiodide (FAPbI3)-based memristor is undesirable for an artificial synapse. Here, we report on the δ-FAPbI3/atomic-layer-deposited (ALD)-SnO2 bilayer memristor for gradual analogue resistive switching. In comparison to a single-layer δ-FAPbI3 memristor, the heterojunction δ-FAPbI3/ALD-SnO2 bilayer effectively reduces the current level in the high-resistance state. The analog resistive switching characteristics of δ-FAPbI3/ALD-SnO2 demonstrate exceptional linearity and potentiation/depression performance, resembling an artificial synapse for neuromorphic computing. The nonlinearity of long-term potentiation and long-term depression is notably decreased from 12.26 to 0.60 and from -8.79 to -3.47, respectively. Moreover, the δ-FAPbI3/ALD-SnO2 bilayer achieves a recognition rate of ≤94.04% based on the modified National Institute of Standards and Technology database (MNIST), establishing its potential in an efficient artificial synapse.

1-Selector 1-Memristor Configuration with Multifunctional a-IGZO Memristive Devices Fabricated at Room Temperature

Serving as neuromorphic hardware accelerators, memristors play a crucial role in large-scale neuromorphic computing. Herein, two-terminal memristors utilizing amorphous indium-gallium-zinc oxide (a-IGZO) are fabricated through room-temperature sputtering. The electrical characteristics of these memristors are effectively modulated by varying the oxygen flow during the deposition process. The optimized a-IGZO memristor, fabricated under 3 sccm oxygen flow, presents a 5 × 103 ratio between its high- and low-resistance states, which can be maintained over 1 × 104 s with minimal degradation. Meanwhile, desirable properties such as electroforming-free and self-compliance, crucial for low-energy consumption, are also obtained in the a-IGZO memristor. Moreover, analog conductance switching is observed, demonstrating an interface-type behavior, as evidenced by its device-size-dependent performance. The coexistence of negative differential resistance with analog switching is attributed to the migration of oxygen vacancies and the trapping/detrapping of charges. Furthermore, the device demonstrates optical storage capabilities by exploiting the optical properties of a-IGZO, which can stably operate for up to 50 sweep cycles. Various synaptic functions have been demonstrated, including paired-pulse facilitation and spike-timing-dependent plasticity. These functionalities contribute to a simulated recognition accuracy of 90% for handwritten digits. Importantly, a one-selector one-memristor (1S1M) architecture is successfully constructed at room temperature by integrating a-IGZO memristor on a TaOx-based selector. This architecture exhibits a 107 on/off ratio, demonstrating its potential to suppress sneak currents among adjacent units in a memristor crossbar.

High-Density Artificial Synapse Array Consisting of Homogeneous Electrolyte-Gated Transistors

The artificial synapse array with an electrolyte-gated transistor (EGT) as an array unit presents considerable potential for neuromorphic computation. However, the integration of EGTs faces the drawback of the conflict between the polymer electrolytes and photo-lithography. This study presents a scheme based on a lateral-gate structure to realize high-density integration of EGTs and proposes the integration of 100 × 100 EGTs into a 2.5 × 2.5 cm2 glass, with a unit density of up to 1600 devices cm-2 . Furthermore, an electrolyte framework is developed to enhance the array performance, with ionic conductivity of up to 2.87 × 10-3  S cm-1 owing to the porosity of zeolitic imidazolate frameworks-67. The artificial synapse array realizes image processing functions, and exhibits high performance and homogeneity. The handwriting recognition accuracy of a representative device reaches 92.80%, with the standard deviation of all the devices being limited to 9.69%. The integrated array and its high performance demonstrate the feasibility of the scheme and provide a solid reference for the integration of EGTs.

Recent Advances and Opportunities of Eco-Friendly Ternary Copper Halides: A New Superstar in Optoelectronic Applications

Recently, the newly-emerging lead-free metal-halide materials with less toxicity and superior optoelectronic properties have received wide attention as the safer and potentially more robust alternatives to lead-based perovskite counterparts. Among them, ternary copper halides (TCHs) have become a vital group due to their unique features, including abundant structural diversity, ease of synthesis, unprecedented optoelectronic properties, high abundance, and low cost. Although the recent efforts in this field have made certain progresses, some scientific and technological issues still remain unresolved. Herein, a comprehensive and up-to-date overview of recent progress on the fundamental characteristics of TCH materials and their versatile applications is presented, which contains topics such as: i) crystal and electronic structure features and synthesis strategies; ii) mechanisms of self-trapped excitons, luminescence regulation, and environmental stability; and iii) their burgeoning optoelectronic devices of phosphor-converted white light-emitting diodes (WLEDs), electroluminescent LEDs, anti-counterfeiting, X-ray scintillators, photodetectors, sensors, and memristors. Finally, the current challenges together with future perspectives on the development of TCH materials and applications are also critically described, which is considered to be critical for accelerating the commercialization of these rapidly evolving technologies.

Emulating learning behavior in a flexible device with self-formed Ag dewetted nanostructure as active element

Neuromorphic devices are a promising alternative to the traditional von Neumann architecture. These devices have the potential to achieve high-speed, efficient, and low-power artificial intelligence. Flexibility is required in these devices so that they can bend and flex without causing damage to the underlying electronics. This feature shows a possible use in applications that require flexible electronics, such as robotics and wearable electronics. Here, we report a flexible self-formed Ag-based neuromorphic device that emulates various brain-inspired synaptic activities, such as short-term plasticity and long-term potentiation (STP and LTP) in both the flat and bent states. Half and full-integer quantum conductance jumps were also observed in the flat and bent states. The device showed excellent switching and endurance behaviors. The classical conditioning could be emulated even in the bent state.

A review of memristor: material and structure design, device performance, applications and prospects

With the booming growth of artificial intelligence (AI), the traditional von Neumann computing architecture based on complementary metal oxide semiconductor devices are facing memory wall and power wall. Memristor based in-memory computing can potentially overcome the current bottleneck of computer and achieve hardware breakthrough. In this review, the recent progress of memory devices in material and structure design, device performance and applications are summarized. Various resistive switching materials, including electrodes, binary oxides, perovskites, organics, and two-dimensional materials, are presented and their role in the memristor are discussed. Subsequently, the construction of shaped electrodes, the design of functional layer and other factors influencing the device performance are analyzed. We focus on the modulation of the resistances and the effective methods to enhance the performance. Furthermore, synaptic plasticity, optical-electrical properties, the fashionable applications in logic operation and analog calculation are introduced. Finally, some critical issues such as the resistive switching mechanism, multi-sensory fusion, system-level optimization are discussed.

Low-power flexible organic memristor based on PEDOT:PSS/pentacene heterojunction for artificial synapse

Organic synaptic memristors are of considerable interest owing to their attractive characteristics and potential applications to flexible neuromorphic electronics. In this work, an organic type-II heterojunction consisting of poly(3,4-ethylenedioxythiophene): polystyrene sulfonate (PEDOT:PSS) and pentacene was adopted for low-voltage and flexible memristors. The conjugated polymer PEDOT:PSS serves as the flexible resistive switching (RS) layer, while the thin pentacene layer plays the role of barrier adjustment. This heterojunction enabled the memristor device to be triggered with low-energy RS operations (V < ± 1.0 V and I < 9.0 μA), and simultaneously providing high mechanical bending stability (bending radius of ≈2.5 mm, bending times = 1,000). Various synaptic properties have been successfully mimicked. Moreover, the memristors presented good potentiation/depression stability with a low cycle-to-cycle variation (CCV) of less than 8%. The artificial neural network consisting of this flexible memristor exhibited a high accuracy of 89.0% for the learning with MNIST data sets, even after 1,000 tests of 2.5% stress-strain. This study paves the way for developing low-power and flexible synaptic devices utilizing organic heterojunctions.

Actual origin and precise control of asymmetrical hysteresis in an individual CH3NH3PbI3 micro/nanowire for optical memory and logic operation

Although CH₃NH₃PbI₃ can present an excellent photoresponse to visible light, its application in solar cells and photodetectors is seriously hindered due to hysteresis behaviour. Moreover, for its origin, there exist different opinions. Herein, we demonstrate a route to realize precise control for the electrical transport of a single CH₃NH₃PbI₃ micro/nanowire by constructing a two-terminal device with asymmetric Ag and C electrodes, and its hysteresis can be clearly identified as a synergistic effect of the redox reaction at the interface of the Ag electrode and the injection and ejection of holes in the interfacial traps of the C electrode rather than its bulk effect. The device can show superior bias amplitude and illumination intensity dependence of hysteresis loops with typical bipolar resistive switching features. Thus, an excellent multilevel nonvolatile optical memory can be effectively realized by the modulation of the illumination and bias, and moreover a logic OR gate operation can be successfully implemented with voltage and illumination as input signals as well. This work clearly reveals and provides a new insight of hysteresis origin that can be attributed to a synergistic effect of two asymmetrical electrode interfaces, and therefore precisely controlling its electrical transport to realize an outstanding application potential in multifunctional devices integrated with optical nonvolatile memory and logic OR gate operation.

Mixed-Dimensional Formamidinium Bismuth Iodides Featuring In-Situ Formed Type-I Band Structure for Convolution Neural Networks

For valence change memory (VCM)-type synapses, a large number of vacancies help to achieve very linearly changed dynamic range, and also, the low activation energy of vacancies enables low-voltage operation. However, a large number of vacancies increases the current of artificial synapses by acting like dopants, which aggravates low-energy operation and device scalability. Here, mixed-dimensional formamidinium bismuth iodides featuring in-situ formed type-I band structure are reported for the VCM-type synapse. As compared to the pure 2D and 0D phases, the mixed phase increases defect density, which induces a better dynamic range and higher linearity. In addition, the mixed phase decreases conductivity for non-paths despite a large number of defects providing lots of conducting paths. Thus, the mixed phase-based memristor devices exhibit excellent potentiation/depression characteristics with asymmetricity of 3.15, 500 conductance states, a dynamic range of 15, pico ampere-scale current level, and energy consumption per spike of 61.08 aJ. A convolutional neural network (CNN) simulation with the Canadian Institute for Advanced Research-10 (CIFAR-10) dataset is also performed, confirming a maximum recognition rate of approximately 87%. This study is expected to lay the groundwork for future research on organic bismuth halide-based memristor synapses usable for a neuromorphic computing system.