Resonant Activation of Resistive Switching in ZrO2(Y) Based Memristors

Semiconductors - We report on a comparative study of resistive switching in the memristors based on ZrO2(Y) films and on ZrO2(Y)/Ta2O5 bilayer stacks by triangle voltage pulses with superimposed...     

Resistive Switching in Memristors Based on Ge/Si(001) Epitaxial Layers

Semiconductors - The Ag/Ge/Si(001) stacks with threading dislocations growing through the Ge epitaxial layers (ELs) manifested bipolar resistive switching (RS) between two metastable resistance...     

Nanoscale Resistive Switching in Amorphous Perovskite Oxide (a‐SrTiO3) Memristors

Memristive devices are the precursors to high density nanoscale memories and the building blocks for neuromorphic computing. In this work, a unique room temperature synthesized perovskite oxide (amorphous SrTiO₃: a‐STO) thin film platform with engineered oxygen deficiencies is shown to realize high performance and scalable metal‐oxide‐metal (MIM) memristive arrays demonstrating excellent uniformity of the key resistive switching parameters. a‐STO memristors exhibit nonvolatile bipolar resistive switching with significantly high (10³–10⁴) switching ratios, good endurance (>10⁶I–V sweep cycles), and retention with less than 1% change in resistance over repeated 10⁵ s long READ cycles. Nano‐contact studies utilizing in situ electrical nanoindentation technique reveal nanoionics driven switching processes that rely on isolatedly controllable nano‐switches uniformly distributed over the device area. Furthermore, in situ electrical nanoindentation studies on ultrathin a‐STO/metal stacks highlight the impact of mechanical stress on the modulation of non‐linear ionic transport mechanisms in perovskite oxides while confirming the ultimate scalability of these devices. These results highlight the promise of amorphous perovskite memristors for high performance CMOS/CMOL compatible memristive systems.     

基于忆阻器的可切换带通滤波器

将忆阻器作为射频开关,采用传统双模谐振器与改进式短路阶跃阻抗谐振器的组合结构,设计了一款 可切换的三频带通滤波器。通过合理设计忆阻器偏置电路的位置,利用忆阻器阻抗的非易失性来实现滤波器通带的可 切换功能。当滤波器工作时忆阻器处于断电的记忆状态,从而改善了可切换滤波器的互调性能。文中设计的滤波器拥 有四种可切换工作状态,包括三频、双频、单频和全阻工作模式。测试结果表明,所提出的滤波器在三频情况下各通带 的插入损耗分别是 -1. 38 dB@ 1. 75 GHz、-0. 94 dB@ 3. 10 GHz、-3. 13 dB@ 3. 70 GHz;双频工作模式下各通带的插入损 耗为-1. 67 dB@ 1. 75 GHz、-3. 10 dB@ 3. 70 GHz;单频工作模式下通带的插入损耗为-1. 35 dB@ 3. 10 GHz;全阻模式下 阻带衰减高于24. 85 dB。文中采用替代测试法进行测试,测试结果与仿真结果取得了较好的一致性。     

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.     

Towards neuromorphic electronics: Memristors on foldable silicon fabric

The advantages associated with neuromorphic computation are rich areas of complex research. We address the fabrication challenge of building neuromorphic devices on structurally foldable platform with high integration density. We present a CMOS compatible fabrication process to demonstrate for the first time memristive devices fabricated on bulk monocrystalline silicon (100) which is next transformed into a flexible thin sheet of silicon fabric with all the pre-fabricated devices. This process preserves the ultra-high integration density advantage unachievable on other flexible substrates. In addition, the memristive devices are of the size of a motor neuron and the flexible/folded architectural form factor is critical to match brain cortex׳s folded pattern for ultra-compact design.     

Synapse‐Like Organic Thin Film Memristors

Exploring new type of synapse–like electronic devices with fusion of computing and memory is a promising strategy to fundamentally approach to intelligent machines. Herein, organic thin film memristors (OTFMs) are achieved, functioning as electrically programmable and erasable analog memory with continuous and nonvolatile device states. The memristive characteristics of OTFMs stem from the asymmetric electrode configuration and the cumulative charge trapping/detrapping in a polymer electret layer, which enables the state–dependent current modulation analogous to the synaptic weight change in biological synapses. OTFMs are demonstrated to successfully emulate the essential synaptic functions, including the reversible potentiation and depression, and the short‐term plasticity such as the paired‐pulse facilitation and the long‐term plasticity such as the spike–timing dependent plasticity.     

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.     

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 (CMOS) technology is gradually fading and is unable to meet the growing needs of neuromorphic computing. Besides, the existing artificial neuron circuits are complex owing to the limited bio-plausibility of CMOS devices. Memristors with volatile threshold switching (TS) behaviors and rich dynamics are promising candidates to emulate the biological spiking neurons beyond the CMOS technology and build high-efficient neuromorphic systems. Herein, the state-of-the-art about the fundamental knowledge of SNNs is reviewed. Moreover, we review the implementation of TS memristor-based neurons and their systems, and point out the challenges that should be further considered from devices to circuits in the system demonstrations. We hope that this review could provide clues and be helpful for the future development of neuromorphic computing with memristors.     

Circuit Elements With Memory: Memristors, Memcapacitors, and Meminductors

We extend the notion of memristive systems to capacitive and inductive elements, namely, capacitors and inductors whose properties depend on the state and history of the system. All these elements typically show pinched hysteretic loops in the two constitutive variables that define them: current-voltage for the memristor, charge-voltage for the memcapacitor, and current-flux for the meminductor. We argue that these devices are common at the nanoscale, where the dynamical properties of electrons and ions are likely to depend on the history of the system, at least within certain time scales. These elements and their combination in circuits open up new functionalities in electronics and are likely to find applications in neuromorphic devices to simulate learning, adaptive, and spontaneous behavior.     

Temperature-Frequency Study of Germanium Selenide Memristors with a Self-Directed Current-Conducting Channel

Russian Microelectronics - The experimental data on the measurement of resistance and electrical conductivity in a low-resistance mode of operation of a memristor based on germanium selenide with a...     

Flexible Oxide Thin Film Transistors,Memristors, and Their Integration

Flexible electronics have seen extensive research over the past years due to their potential stretchability and adaptability to non-flat surfaces. They are key to realizing low-power sensors and circuits for wearable electronics and Internet of Things (IoT) applications. Semiconducting metal-oxides are a prime candidate for implementing flexible electronics as their conformal deposition methods lend themselves to the idiosyncrasies of non-rigid substrates. They are also a major component for the development of resistive memories (memristors) and as such their monolithic integration with thin film electronics has the potential to lead to novel all-metal-oxide devices combining memory and computing on a single node. This review focuses on exploring the recent advances across all these fronts starting from types of suitable substrates and their mechanical properties, different types of fabrication methods for thin film transistors and memristors applicable to flexible substrates (vacuum- or solution-based), applications and comparison with rigid substrates while additionally delving into matters associated with their monolithic integration.     

Emerging MXene-Based Memristors for In-Memory,Neuromorphic Computing,and Logic Operation

Confronted by the difficulties of the von Neumann bottleneck and memory wall, traditional computing systems are gradually inadequate for satisfying the demands of future data-intensive computing applications. Recently, memristors have emerged as promising candidates for advanced in-memory and neuromorphic computing, which pave one way for breaking through the dilemma of current computing architecture. Till now, varieties of functional materials have been developed for constructing high-performance memristors. Herein, the review focuses on the emerging 2D MXene materials-based memristors. First, the mainstream synthetic strategies and characterization methods of MXenes are introduced. Second, the different types of MXene-based memristive materials and their widely adopted switching mechanisms are overviewed. Third, the recent progress of MXene-based memristors for data storage, artificial synapses, neuromorphic computing, and logic circuits is comprehensively summarized. Finally, the challenges, development trends, and perspectives are discussed, aiming to provide guidelines for the preparation of novel MXene-based memristors and more engaging information technology applications.     

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.     

High‐Speed and Low‐Energy Nitride Memristors

High‐performance memristors based on AlN films have been demonstrated, which exhibit ultrafast ON/OFF switching times (≈85 ps for microdevices with waveguide) and relatively low switching current (≈15 μA for 50 nm devices). Physical characterizations are carried out to understand the device switching mechanism, and rationalize speed and energy performance. The formation of an Al‐rich conduction channel through the AlN layer is revealed. The motion of positively charged nitrogen vacancies is likely responsible for the observed switching.