Tailoring Multilevel‐Stable Remanence States in Exchange‐Biased System through Spin‐Orbit Torque

Multilevel remanence states have potential applications in ultra‐high‐density storage and neuromorphic computing. Continuous tailoring of the multilevel remanence states by spin‐orbit torque (SOT) is reported in perpendicularly magnetized Pt/Co/IrMn heterostructures. Double‐biased hysteresis loops with only one remanence state can be tuned from the positively or negatively single‐biased loops by SOT controlled sign of the exchange‐bias field. The remanence states associated with the heights of the sub‐loops are continually changed by tuning the ratio of the positively and negatively oriented ferromagnetic domains. The multilevel storage cells are demonstrated by reading the remanent Hall resistance through changing the sign and/or the magnitude of current pulse. The synaptic plasticity behaviors for neuromorphic computing are also simulated by varying the remanent Hall resistance under the consecutive current pulses. This work demonstrates that SOT is an effective method to tailor the remanence states in the double‐biased heavy metal/ferromagnetic/antiferromagnetic system. The multilevel‐stable remanence states driven by SOT show potential applications in future multilevel memories and neuromorphic computing devices.     

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.     

Small‐Molecule‐Based Organic Field‐Effect Transistor for Nonvolatile Memory and Artificial Synapse

With the incorporation of tailorable organic electronic materials as channel and storage materials, organic field‐effect transistor (OFET)‐based memory has become one of the most promising data storage technologies for hosting a variety of emerging memory applications, such as sensory memory, storage memory, and neuromorphic computing. Here, the recent state‐of‐the‐art progresses in the use of small molecules for OFET nonvolatile memory and artificial synapses are comprehensively reviewed, focusing on the characteristic features of small molecules in versatile functional roles (channel, storage, modifier, and dopant). Techniques for optimizing the storage capacity, speed, and reliability of nonvolatile memory devices are addressed in detail. Insight into the use of small molecules in artificial synapses constructed on OFET memory is also obtained in this emerging field. Finally, the strategies of molecular design for improving memory performance in view of small molecules as storage mediums are discussed systematically, and challenges are addressed to shed light on the future development of this vital research field.     

Ferroelectric Domain Wall Memristor

A domain wall‐enabled memristor is created, in thin film lithium niobate capacitors, which shows up to twelve orders of magnitude variation in resistance. Such dramatic changes are caused by the injection of strongly inclined conducting ferroelectric domain walls, which provide conduits for current flow between electrodes. Varying the magnitude of the applied electric‐field pulse, used to induce switching, alters the extent to which polarization reversal occurs; this systematically changes the density of the injected conducting domain walls in the ferroelectric layer and hence the resistivity of the capacitor structure as a whole. Hundreds of distinct conductance states can be produced, with current maxima achieved around the coercive voltage, where domain wall density is greatest, and minima associated with the almost fully switched ferroelectric (few domain walls). Significantly, this “domain wall memristor” demonstrates a plasticity effect: when a succession of voltage pulses of constant magnitude is applied, the resistance changes. Resistance plasticity opens the way for the domain wall memristor to be considered for artificial synapse applications in neuromorphic circuits.     

Designing Negative Differential Resistance Devices Based on Self‐Heating

There are a number of important emerging applications that would benefit from simple circuit elements that exhibit tunable current‐controlled negative differential resistance (NDR). The advent of such devices would enable, for example, key components for threshold logic and neuromorphic computing such as threshold switches, periodic and chaotic oscillators, and small signal amplifiers. Circuit elements that provide NDR with modifiable electrical characteristics would also be useful for creating optimized “selectors” that enable addressing of individual memory cells in large resistance‐based memory arrays. Currently, there are no simple, bipolar, two‐terminal commercial devices that exhibit current‐controlled NDR. This paper demonstrates that current‐controlled NDR can, in principal, arise from any electrical conduction mechanism that depends superlinearly on temperature, and that in practice a broad spectrum of materials can be utilized to yield NDR. A model is presented that accurately predicts conditions under which NDR can be observed and guidelines are provided for choosing materials that result in desired electrical characteristics. This model accurately predicts the behavior of some existing structures and can be used to tailor future circuit elements for emerging applications. It may also explain the onset of a number of “soft breakdown” phenomena.     

相变材料Ge2Sb2Te5的性质及其面向新型数据存储的应用

信息时代产生的海量数据驱动着计算机存储架构的革新,高性能的非易失性存储器和存算一体的神经形态计算成为存储体系的发展方向。首先,介绍了相变材料Ge2Sb2Te5的阻变性质的机理与应用,详细阐述了相变存储器的发展以及神经形态计算的实现。然后,讨论了基于Ge2Sb2Te5铁电性质的存储器、基于Ge2Sb2Te5介电性质的光子存储单元和基于Ge2Sb2Te5应变作用的高迁移率晶体管。最后,讨论了Ge2Sb2Te5和n型硅等材料的异质结结构在器件中的应用。基于Ge2Sb2Te5材料多种特性的新型存储器件必将在未来存算一体的数据处理中扮演重要的角色。     

Linear Classification Function Emulated by Pectin-Based Polysaccharide-Gated Multiterminal Neuron Transistors

Neuromorphic computing, which merges learning and memory functions, is a new computing paradigm surpassing traditional von Neumann architecture. Apart from the plasticity of artificial synapses, the simulation of neurons’ multi-input signal integration is also of great significance to realize efficient neuromorphic computing. Since the structure of transistors and neurons is strikingly similar, capacitively coupled multi-terminal pectin-gated oxide electric double layer transistors are proposed here as artificial neurons for classification. In this work, the free logic switching of “AND” and “OR” is realized in the device with triple in-plane gates. More importantly, the linear classification function on a single neuron transistor is demonstrated experimentally for the first time. All the results obtained in this work indicate that the prepared artificial neuron can improve the efficiency of artificial neural networks and thus will play an important role in neuromorphic computing.     

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.     

A Reliable All-2D Materials Artificial Synapse for High Energy-Efficient Neuromorphic Computing

High-performance artificial synaptic devices are indispensable for developing neuromorphic computing systems with high energy efficiency. However, the reliability and variability issues of existing devices such as nonlinear and asymmetric weight update are the major hurdles in their practical applications for energy-efficient neuromorphic computing. Here, a two-terminal floating-gate memory (2TFGM) based artificial synapse built from all-2D van der Waals materials is reported. The 2TFGM synaptic device exhibits excellent linear and symmetric weight update characteristics with high reliability and tunability. In particular, the high linearity and symmetric synaptic weight realized by simple programming with identical pulses can eliminate the additional latency and power consumption caused by the peripheral circuit design and achieve an ultralow energy consumption for the synapses in the neural network implementation. A large number of states up to ≈3000, high switching speed of 40 ns and low energy consumption of 18 fJ for a single pulse have been demonstrated experimentally. A high classification accuracy up to 97.7% (close to the software baseline of 98%) has been achieved in the Modified National Institute of Standards and Technology (MNIST) simulations based on the experimental data. These results demonstrate the potential of all-2D 2TFGM for high-speed and low-power neuromorphic computing.     

Enhancing Memory Window Efficiency of Ferroelectric Transistor for Neuromorphic Computing via Two-Dimensional Materials Integration

In-memory computing, particularly neuromorphic computing, has emerged as a promising solution to overcome the energy and time-consuming challenges associated with the von Neumann architecture. The ferroelectric field-effect transistor (FeFET) technology, with its fast and energy-efficient switching and nonvolatile memory, is a potential candidate for enabling both computing and memory within a single transistor. In this study,  the capabilities of an integrated ferroelectric HfO₂ and 2D MoS₂ channel FeFET in achieving high-performance 4-bit per cell memory with low variation and power consumption synapses, while retaining the ability to implement diverse learning rules, are demonstrated. Notably, this device accurately recognizes MNIST handwritten digits with over 94% accuracy using online training mode. These results highlight the potential of FeFET-based in-memory computing for future neuromorphic computing applications.     

Rational Design of Small Molecules to Implement Organic Quaternary Memory Devices

Organic small‐molecule‐based devices with multilevel electroresistive memory behaviors have attracted more and more attentions due to their super‐high data‐storage density. However, up to now, only ternary memory molecules have been reported, and ternary storage devices may not be compatible with the binary computing systems perfectly. In this work, a donor–acceptor structured molecule containing three electron acceptors is rationally designed and the field‐induced charge‐transfer processes can occur from the donors. Organic quaternary memory devices based on this molecule are successfully demonstrated for the first time. The switching threshold voltages of the memory device are –2.04, –2.73, and –3.96 V, and the current ratio of the “0,” “1,” “2,” and “3” states is 1:10^1.78:10^3.47:10^5.36, which indicate a low possibility of read and write errors. The results represent a further step in organic high‐density data‐storage devices and will inspire the further study in this field.     

Beyond von‐Neumann Computing with Nanoscale Phase‐Change Memory Devices

Historically, the application of phase‐change materials and devices has been limited to the provision of non‐volatile memories. Recently, however, the potential has been demonstrated for using phase‐change devices as the basis for new forms of brain‐like computing, by exploiting their multilevel resistance capability to provide electronic mimics of biological synapses. Here, a different and previously under‐explored property that is also intrinsic to phase‐change materials and devices, namely accumulation, is exploited to demonstrate that nanometer‐scale electronic phase‐change devices can also provide a powerful form of arithmetic computing. Complicated arithmetic operations are carried out, including parallel factorization and fractional division, using simple nanoscale phase‐change cells that process and store data simultaneously and at the same physical location, promising a most efficient and effective means for implementing beyond von‐Neumann computing. This same accumulation property can be used to provide a particularly simple form phase‐change integrate‐and‐fire “neuron”, which, by combining both phase‐change synapse and neuron electronic mimics, potentially opens up a route to the realization of all‐phase‐change neuromorphic processing.     

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.     

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.     

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.     

Introducing pinMOS Memory: A Novel,Nonvolatile Organic Memory Device

In recent decades, organic memory devices have been researched intensely and they can, among other application scenarios, play an important role in the vision of an internet of things. Most studies concentrate on storing charges in electronic traps or nanoparticles while memory types where the information is stored in the local charge up of an integrated capacitance and presented by capacitance received far less attention. Here, a new type of programmable organic capacitive memory called p‐i‐n‐metal‐oxide‐semiconductor (pinMOS) memory is demonstrated with the possibility to store multiple states. Another attractive property is that this simple, diode‐based pinMOS memory can be written as well as read electrically and optically. The pinMOS memory device shows excellent repeatability, an endurance of more than 10⁴ write‐read‐erase‐read cycles, and currently already over 24 h retention time. The working mechanism of the pinMOS memory under dynamic and steady‐state operations is investigated to identify further optimization steps. The results reveal that the pinMOS memory principle is promising as a reliable capacitive memory device for future applications in electronic and photonic circuits like in neuromorphic computing or visual memory systems.     

Measurement of Onset of Structural Relaxation in Melt-Quenched Phase Change Materials

Chalcogenide phase change materials enable non-volatile, low-latency storage-class memory. They are also being explored for new forms of computing such as neuromorphic and in-memory computing. A key challenge, however, is the temporal drift in the electrical resistance of the amorphous states that encode data. Drift, caused by the spontaneous structural relaxation of the newly recreated melt-quenched amorphous phase, has consistently been observed to have a logarithmic dependence in time. Here, it is shown that this observation is valid only in a certain observable timescale. Using threshold-switching voltage as the measured variable, based on temperature-dependent and short timescale electrical characterization, the onset of drift is experimentally measured. This additional feature of the structural relaxation dynamics serves as a new benchmark to appraise the different classical models to explain drift.     

Origin of Current‐Controlled Negative Differential Resistance Modes and the Emergence of Composite Characteristics with High Complexity

Current‐controlled negative differential resistance has significant potential as a fundamental building block in brain‐inspired neuromorphic computing. However, achieving the desired negative differential resistance characteristics, which is crucial for practical implementation, remains challenging due to a lack of consensus on the underlying mechanism and design criteria. Here, a material‐independent model of current‐controlled negative differential resistance is reported to explain a broad range of characteristics, including the origin of the discontinuous snap‐back response observed in many transition metal oxides. This is achieved by explicitly accounting for a non‐uniform current distribution in the oxide film and its impact on the effective circuit of the device rather than a material‐specific phase transition. The predictions of the model are then compared with experimental observations to show that the continuous S‐type and discontinuous snap‐back characteristics serve as fundamental building blocks for composite behavior with higher complexity. Finally, the potential of our approach is demonstrated for predicting and engineering unconventional compound behavior with novel functionality for emerging electronic and neuromorphic computing applications.     

Optoelectronic Perovskite Synapses for Neuromorphic Computing

Simulating the human brain for neuromorphic computing has attractive prospects in the field of artificial intelligence. Optoelectronic synapses have been considered to be important cornerstones of neuromorphic computing due to their ability to process optoelectronic input signals intelligently. In this work, optoelectronic synapses based on all‐inorganic perovskite nanoplates are fabricated, and the electronic and photonic synaptic plasticity is investigated. Versatile synaptic functions of the nervous system, including paired‐pulse facilitation, short‐term plasticity, long‐term plasticity, transition from short‐ to long‐term memory, and learning‐experience behavior, are successfully emulated. Furthermore, the synapses exhibit a unique memory backtracking function that can extract historical optoelectronic information. This work could be conducive to the development of artificial intelligence and inspire more research on optoelectronic synapses.     

Tunable Volatility of Ge2Sb2Te5 in Integrated Photonics

The operation of a single class of optical materials in both a volatile and nonvolatile manner is becoming increasingly important in many applications. This is particularly true in the newly emerging field of photonic neuromorphic computing, where it is desirable to have both volatile (short‐term transient) and nonvolatile (long‐term static) memory operation, for instance, to mimic the behavior of biological neurons and synapses. The search for such materials thus far have focused on phase change materials where typically two different types are required for the two different operational regimes. In this paper, a tunable volatile/nonvolatile response is demonstrated in a photonic phase‐change memory cell based on the commonly employed nonvolatile material Ge₂Sb₂Te₅ (GST). A time‐dependent, multiphysics simulation framework is developed to corroborate the experimental results, allowing us to spatially resolve the recrystallization dynamics within the memory cell. It is then demonstrated that this unique approach to photonic memory enables both data storage with tunable volatility and detection of coincident events between two pulse trains on an integrated chip. Finally, improved efficiency and all‐optical routing with controlled volatility are demonstrated in a ring resonator. These crucial results show that volatility is intrinsically tunable in normally nonvolatile GST which can be used in both regimes interchangeably.