Organic and perovskite memristors for neuromorphic computing

Organic and perovskite memristors have superior characteristics both in material and structural perspectives, and therefore have been evaluated for possible integration as bio-realistic components of artificial intelligent hardware systems. This application will require the brain-inspired integrated systems that can process and memorize large amounts of complex information; requirements include highly uniform and reliable memristors that can be operated at low energy and integrated at high density. Here, we review the progress in development of organic and perovskite memristors to obtain various synaptic behaviors, with focus on material and underlying mechanism aspects. Then we address various approaches to meet the needs for constructing applications of neuromorphic computing, including low energy consumption, high uniformity and reliability of the memristors, and high-density integration. Lastly, we suggest future research directions toward realizing neuromorphic computing.     

The Future of Memristors: Materials Engineering and Neural Networks

From Deep Blue to AlphaGo, artificial intelligence and machine learning are booming, and neural networks have become the hot research direction. However, due to the size limit of complementary metal–oxide–semiconductor (CMOS) transistors, von Neumann-based computing systems are facing multiple challenges (such as memory walls). As the number of transistors required by the neural network increases, the development of neural networks based on the von Neumann computer is limited by volume and energy consumption. As the fourth basic circuit element, memristor shines in the field of neuromorphic computing. The new computer architecture based on memristor is widely considered as a substitute for the von Neumann architecture and has great potential to deal with the neural network and big data era challenge. This article reviews existing materials and structures of memristors, neurophysiological simulations based on memristors, and applications of memristor-based neural networks. The feasibility and advancement of implementing neural networks using memristors are discussed, the difficulties that need to be overcome at this stage are put forward, and their development prospects and challenges faced are also discussed.     

Threshold Switching of Ag or Cu in Dielectrics: Materials,Mechanism, and Applications

Threshold switches with Ag or Cu active metal species are volatile memristors (also termed diffusive memristors) featuring spontaneous rupture of conduction channels. The temporal dynamics of the conductance evolution is closely related to the electrochemical and diffusive dynamics of the active metals which could be modulated by electric field strength, biasing duration, temperature, and so on. Microscopic pictures by electron microscopy and quantitative thermodynamics modeling are examined to give insights into the underlying physics of the switching. Depending on the time scale of the relaxation process, such devices find a variety of novel applications in electronics, ranging from selector devices for memories to synaptic devices for neuromorphic computing.     

Towards engineering in memristors for emerging memory and neuromorphic computing:A review

Resistive random-access memory(RRAM),also known as memristors,having a very simple device structure with two terminals,fulfill almost all of the fundamental requirements of volatile memory,nonvolatile memory,and neuromorphic characteristics.Its memory and neuromorphic behaviors are currently being explored in relation to a range of materials,such as biological materials,perovskites,2D materials,and transition metal oxides.In this review,we discuss the different electrical behaviors exhibited by RRAM devices based on these materials by briefly explaining their corresponding switching mechanisms.We then discuss emergent memory technologies using memristors,together with its potential neuromorphic applications,by elucidating the different material engineering techniques used during device fabrication to improve the memory and neuromorphic performance of devices,in areas such as ION/IOFF ratio,endurance,spike time-dependent plasticity(STDP),and paired-pulse facilitation(PPF),among others.The emulation of essential biological synaptic functions realized in various switching materials,including inorganic metal oxides and new organic materials,as well as diverse device structures such as single-layer and multilayer hetero-structured devices,and crossbar arrays,is analyzed in detail.Finally,we discuss current challenges and future prospects for the development of inorganic and new materials-based memristors.     

2D Material Based Synaptic Devices for Neuromorphic Computing

The demand for computing power has been increasing exponentially since the emergence of artificial intelligence (AI), internet of things (IoT), and machine learning (ML), where novel computing primitives are required. Brain inspired neuromorphic computing systems, capable of combining analog computing and data storage at the device level, have drawn great attention recently. In addition, the basic electronic devices mimicking the biological synapse have achieved significant progress. Owing to their atomic thickness and reduced screening effect, the physical properties of 2D materials could be easily modulated by various stimuli, which is quite beneficial for synaptic applications. In this article, aiming at high-performance and functional neuromorphic computing applications, a comprehensive review of synaptic devices based on 2D materials is provided, including the advantages of 2D materials and heterostructures, various robust multifunctional 2D synaptic devices, and associated neuromorphic applications. Challenges and strategies for the future development of 2D synaptic devices are also discussed. This review will provide an insight into the design and preparation of 2D synaptic devices and their applications in neuromorphic computing.     

Visualizing Thermally Activated Memristive Switching in Percolating Networks of Solution-Processed 2D Semiconductors

Memristive systems present a low-power alternative to silicon-based electronics for neuromorphic and in-memory computation. 2D materials have been increasingly explored for memristive applications due to their novel biomimetic functions, ultrathin geometry for ultimate scaling limits, and potential for fabricating large-area, flexible, and printed neuromorphic devices. While the switching mechanism in memristors based on single 2D nanosheets is similar to conventional oxide memristors, the switching mechanism in nanosheet composite films is complicated by the interplay of multiple physical processes and the inaccessibility of the active area in a two-terminal vertical geometry. Here, the authors report thermally activated memristors fabricated from percolating networks of diverse solution-processed 2D semiconductors including MoS₂, ReS₂, WS₂, and InSe. The mechanisms underlying threshold switching and negative differential resistance are elucidated by designing large-area lateral memristors that allow the direct observation of filament and dendrite formation using in situ spatially resolved optical, chemical, and thermal analyses. The high switching ratios (up to 10³) that are achieved at low fields (≈4 kV cm⁻¹) are explained by thermally assisted electrical discharge that preferentially occurs at the sharp edges of 2D nanosheets. Overall, this work establishes percolating networks of solution-processed 2D semiconductors as a platform for neuromorphic architectures.     

Memristor Kinetics and Diffusion Characteristics for Mixed Anionic‐Electronic SrTiO3‐δ Bits: The Memristor‐Based Cottrell Analysis Connecting Material to Device Performance

Memristors based on mixed anionic‐electronic conducting oxides are promising devices for future data storage and information technology with applications such as non‐volatile memory or neuromorphic computing. Unlike transistors solely operating on electronic carriers, these memristors rely, in their switch characteristics, on defect kinetics of both oxygen vacancies and electronic carriers through a valence change mechanism. Here, Pt|SrTiO_3‐δ|Pt structures are fabricated as a model material in terms of its mixed defects which show stable resistive switching. To date, experimental proof for memristance is characterized in hysteretic current–voltage profiles; however, the mixed anionic‐electronic defect kinetics that can describe the material characteristics in the dynamic resistive switching are still missing. It is shown that chronoamperometry and bias‐dependent resistive measurements are powerful methods to gain complimentary insights into material‐dependent diffusion characteristics of memristors. For example, capacitive, memristive and limiting currents towards the equilibrium state can successfully be separated. The memristor‐based Cottrell analysis is proposed to study diffusion kinetics for mixed conducting memristor materials. It is found that oxygen diffusion coefficients increase up to 3 × 10^–15 m²s^–1 for applied bias up to 3.8 V for SrTiO_3‐δ memristors. These newly accessible diffusion characteristics allow for improving materials and implicate field strength requirements to optimize operation towards enhanced performance metrics for valence change memristors.     

Electrolyte-gated transistors for neuromorphic applications

Von Neumann computers are currently failing to follow Moore’s law and are limited by the von Neumann bottleneck.To enhance computing performance,neuromorphic computing systems that can simulate the function of the human brain are being developed.Artificial synapses are essential electronic devices for neuromorphic architectures,which have the ability to perform signal processing and storage between neighboring artificial neurons.In recent years,electrolyte-gated transistors(EGTs)have been seen as promising devices in imitating synaptic dynamic plasticity and neuromorphic applications.Among the various electronic devices,EGT-based artificial synapses offer the benefits of good stability,ultra-high linearity and repeated cyclic symmetry,and can be constructed from a variety of materials.They also spatially separate“read”and“write”operations.In this article,we provide a review of the recent progress and major trends in the field of electrolyte-gated transistors for neuromorphic applications.We introduce the operation mechanisms of electric-double-layer and the structure of EGT-based artificial synapses.Then,we review different types of channels and electrolyte materials for EGT-based artificial synapses.Finally,we review the potential applications in biological functions.     

Ion Channels and Electroosmosis in Porous Geopolymers: A Novel Category of Low-Cost Memristors

Memristors are electric components that emulate the memory and computational properties of biological synapses by remembering the current that flows through them. Here, for the first time, the memristive properties of geopolymers, inexpensive ceramic materials manufactured at room temperature from alkaline activation of amorphous aluminosilicate precursors, are presented. It is demonstrated that geopolymers present all the fingerprints of memristors, and a physics-based model is proposed, which demonstrates that electroosmosis in the bulk geopolymer pores induces ion channels that foster change in the overall conductance of the bulk material, contributing to the observed memristive behavior. This model opens the door to a new category of porous electroosmosis-based bulk memristors. Synaptic functions such as short-term plasticity and long-term plasticity, as well as endurance and retention capabilities are also demonstrated. The reported findings pave the way to the use of geopolymers for low-cost applications in neuromorphic computing.     

Accelerated Ionic Motion in Amorphous Memristor Oxides for Nonvolatile Memories and Neuromorphic Computing

Memristive devices based on mixed ionic–electronic resistive switches have an enormous potential to replace today's transistor‐based memories and Von Neumann computing architectures thanks to their ability for nonvolatile information storage and neuromorphic computing. It still remains unclear however how ionic carriers are propagated in amorphous oxide films at high local electric fields. By using memristive model devices based on LaFeO₃ with either amorphous or epitaxial nanostructures, we engineer the structural local bonding units and increase the oxygen‐ionic diffusion coefficient by one order of magnitude for the amorphous oxide, affecting the resistive switching operation. We show that only devices based on amorphous LaFeO₃ films reveal memristive behavior due to their increased oxygen vacancy concentration. We achieved stable resistive switching with switching times down to microseconds and confirm that it is predominantly the oxygen‐ionic diffusion character and not electronic defect state changes that modulate the resistive switching device response. Ultimately, these results show that the local arrangement of structural bonding units in amorphous perovskite films at room temperature can be used to largely tune the oxygen vacancy (defect) kinetics for resistive switches (memristors) that are both theoretically challenging to predict and promising for future memory and neuromorphic computing applications.     

六方氮化硼忆阻器的研究进展

六方氮化硼是一种与石墨烯结构相似的材料,以六方氮化硼作为阻变介质层的忆阻器,具有良好的散热性能,不易发生介电击穿,能够实现小尺寸、低功耗和大的开关比;在计算机运算存储研究、人工神经网络和神经形态(即类脑)计算领域有极大的应用前景。文章主要介绍了忆阻器的分类,分析了六方氮化硼忆阻器的阻变机制,综述了六方氮化硼忆阻器的研究现状。最后,指出了六方氮化硼忆阻器当前面临的挑战,并展望了未来的发展方向。     

Advanced Optoelectronic Devices for Neuromorphic Analog Based on Low-Dimensional Semiconductors

Neuromorphic systems can parallelize the perception and computation of information, making it possible to break through the von Neumann bottleneck. Neuromorphic engineering has been developed over a long period of time based on Hebbian learning rules. The optoelectronic neuromorphic analog device combines the advantages of electricity and optics, and can simulate the biological visual system, which has a very strong development potential. Low-dimensional materials play a very important role in the field of optoelectronic neuromorphic devices due to their flexible bandgap tuning mechanism and strong light-matter coupling efficiency. This review introduces the basic synaptic plasticity of neuromorphic devices. According to the different number of terminals, two-terminal neuromorphic memristors, three-terminal neuromorphic transistors and artificial visual system are introduced from the aspects of the action mechanism and device structure. Finally, the development prospect of optoelectronic neuromorphic analog devices based on low-dimensional materials is prospected.     

Functional Materials for Memristor-Based Reservoir Computing: Dynamics and Applications

The booming development of artificial intelligence (AI) requires faster physical processing units as well as more efficient algorithms. Recently, reservoir computing (RC) has emerged as an alternative brain-inspired framework for fast learning with low training cost, since only the weights associated with the output layers should be trained. Physical RC becomes one of the leading paradigms for computation using high-dimensional, nonlinear, dynamic substrates. Among them, memristor appears to be a simple, adaptable, and efficient framework for constructing physical RC since they exhibit nonlinear features and memory behavior, while memristor-implemented artificial neural networks display increasing popularity towards neuromorphic computing. In this review, the memristor-implemented RC systems from the following aspects: architectures, materials, and applications are summarized. It starts with an introduction to the RC structures that can be simulated with memristor blocks. Specific interest then focuses on the dynamic memory behaviors of memristors based on various material systems, optimizing the understanding of the relationship between the relaxation behaviors and materials, which provides guidance and references for building RC systems coped with on-demand application scenarios. Furthermore, recent advances in the application of memristor-based physical RC systems are surveyed. In the end, the further prospects of memristor-implemented RC system in a material view are envisaged.     

Exploring Multifunctionality in MgO-Based Magnetic Tunnel Junctions with Coexisting Magnetoresistance and Memristive Properties

Magnetic tunnel junctions (MTJs) and memristors are two key emerging nanotechnologies that attracted significant interest for potential applications at the forefront of the digital revolution, including sensing, data storage, and non-conventional computation. The co-integration of these phenomena into a single multifunctional device is an important step toward harnessing the re-programmability of memristive systems with the high yield and varied functionality of MTJs. This study demonstrates the co-existence of magnetoresistance and memristive properties on MgO-based MTJs. These devices show a magnetoresistance with a linear response as a function of a magnetic field and no hysteresis, which are the requirements for good magnetic field sensors, as well as demonstrating a non-volatile and quasi-analogue memristive behavior as a function of an applied electrical field down to nanosecond pulses. Furthermore, by doping the oxide barrier, the memristive power consumption is lowered by 20% giving the multi-functionality of the devices a promising scalability potential. This study also shows that, memristive switching can be reversibly used to completely suppress and recover the spintronic functionalities. These results can pave the way for a seamless co-integration of memristors and spintronic devices in complex reprogrammable circuits addressing applications such as reprogrammable multifunctional field sensor arrays and neuromorphic computing.     

Understanding Microscopic Operating Mechanisms of a van der Waals Planar Ferroelectric Memristor

Ferroelectric memristors represent a promising new generation of devices that have a wide range of applications in memory, digital information processing, and neuromorphic computing. Recently, van der Waals ferroelectric In₂Se₃ with unique interlinked out-of-plane and in-plane polarizations has enabled multidirectional resistance switching, providing unprecedented flexibility in planar and vertical device integrations. However, the operating mechanisms of these devices have remained unclear. Here, through the demonstration of van der Waals In₂Se₃-based planar ferroelectric memristors with the device resistance continuously tunable over three orders of magnitude, and by correlating device resistance states, ferroelectric domain configurations, and surface electric potential, the studies reveal that the resistive switching is controlled by the multidomain formations and the associated energy barriers between domains, as opposed to the commonly assumed Schottky barrier modulations at the metal-ferroelectric interface. The findings reveal new device physics through elucidating the microscopic operating mechanisms of this new generation of devices, and provide a critical guide for future device development and integration efforts.     

Redox Memristors with Volatile Threshold Switching Behavior for Neuromorphic Computing

The spiking neural network(SNN), closely inspired by the human brain, is one of the most powerful platforms to enable highly efficient, low cost, and robust neuromorphic computations in hardware using traditional or emerging electron devices within an integrated system. In the hardware implementation, the building of artificial spiking neurons is fundamental for constructing the whole system. However, with the slowing down of Moore’s Law,the traditional complementary metal-oxide-semiconductor(CM...     

Novel Electronics for Flexible and Neuromorphic Computing

Emerging classes of flexible electronic systems that can be attached to a wide range of surfaces from wearable clothes to internal organs have driven significant advances in communication protocols (e.g., Internet of Things, augmented reality) and clinical research, shifting today's personal computing paradigm. The field of “system on plastic” is on the verge of an innovative breakthrough toward a hypercognitive society by being fused with current neuromorphic applications in the spotlight, which can offer intelligent services such as personalized feedback therapy and autonomous driving. The novel concept of electronics for flexible and neuromorphic computing requires an important research leap in micro‐/nanoelectronics on plastics, system‐level integration techniques (interconnection and packaging), and synaptic devices. Here, representative advances and developments in the area of flexible and neuromorphic technologies are reviewed with regard to device configurations, materials, fabrication processes, and their potential research fields.     

Low-Energy Oxygen Plasma Injection of 2D Bi2Se3 Realizes Highly Controllable Resistive Random Access Memory

Resistive random access memory (RRAM) based on ultrathin 2D materials is considered to be a very feasible solution for future data storage and neuromorphic computing technologies. However, controllability and stability are the problems that need to be solved for practical applications. Here, by introducing a damage-less ion implantation technology using ultralow-energy plasma, the transport mechanisms of space charge limited current and Schottky emission are successfully realized and controlled in RRAM based on 2D Bi₂Se₃ nanosheets. The memristors exhibit stable resistive switching behavior with a high resistive switching ratio (>10⁴), excellent cycling endurances (300 cycles), and great retention performance (>10⁴ s). The reliability and controllability of Bi₂Se₃ memory endowed by oxygen plasma injection demonstrate the great potential of this ultralow-energy ion implantation technology in the application of 2D RRAM.     

Biorealistic Implementation of Synaptic Functions with Oxide Memristors through Internal Ionic Dynamics

Memristors have attracted broad interest as a promising candidate for future memory and computing applications. Particularly, it is believed that memristors can effectively implement synaptic functions and enable efficient neuromorphic systems. Most previous studies, however, focus on implementing specific synaptic learning rules by carefully engineering external programming parameters instead of focusing on emulating the internal cause that leads to the apparent learning rules. Here, it is shown that by taking advantage of the different time scales of internal oxygen vacancy (V_O) dynamics in an oxide‐based memristor, diverse synaptic functions at different time scales can be implemented naturally. Mathematically, the device can be effectively modeled as a second‐order memristor with a simple set of equations including multiple state variables. Not only is this approach more biorealistic and easier to implement, by focusing on the fundamental driving mechanisms it allows the development of complete theoretical and experimental frameworks for biologically inspired computing systems.     

Photon‐Memristive System for Logic Calculation and Nonvolatile Photonic Storage

Memristor‐based architectures have shown great potential for developing future computing systems beyond the era of von Neumann and Moore's law. However, the monotonous electrical input for dynamic resistance regulation limits the developments of memristors. Here, a concept of a photon‐memristive system, which realizes memristance depending on number of photons (optical inputs), is proposed. A detailed theoretical derivation is performed and the memristive characteristics, as stimulated by the optical inputs based on a hybrid system, consisting of a low‐dimension photoelectric semiconductor and a ferroelectric substrate are determined. The photon‐memristive system is also suitable for nonvolatile photonic memory since it possesses three or more‐bit data storage, desirable resistance‐change space, and an ON/OFF ratio of nearly 10⁷. The integrated circuit based on several photon‐memristive systems also realizes available photon‐triggered in‐memory computing. The photon‐memristive system expands the definition of memristors and emerges as a new data storage cell for future photonic neuromorphic computational architectures.