Vacancy‐Induced Synaptic Behavior in 2D WS2 Nanosheet–Based Memristor for Low‐Power Neuromorphic Computing

Memristors with nonvolatile memory characteristics have been expected to open a new era for neuromorphic computing and digital logic. However, existing memristor devices based on oxygen vacancy or metal‐ion conductive filament mechanisms generally have large operating currents, which are difficult to meet low‐power consumption requirements. Therefore, it is very necessary to develop new materials to realize memristor devices that are different from the mechanisms of oxygen vacancy or metal‐ion conductive filaments to realize low‐power operation. Herein, high‐performance and low‐power consumption memristors based on 2D WS₂ with 2H phase are demonstrated, which show fast ON (OFF) switching times of 13 ns (14 ns), low program current of 1 µA in the ON state, and SET (RESET) energy reaching the level of femtojoules. Moreover, the memristor can mimic basic biological synaptic functions. Importantly, it is proposed that the generation of sulfur and tungsten vacancies and electron hopping between vacancies are dominantly responsible for the resistance switching performance. Density functional theory calculations show that the defect states formed by sulfur and tungsten vacancies are at deep levels, which prevent charge leakage and facilitate the realization of low‐power consumption for neuromorphic computing application.     

Scaling Effect on Silicon Nitride Memristor with Highly Doped Si Substrate

A feasible approach is reported to reduce the switching current and increase the nonlinearity in a complementary metal–oxide–semiconductor (CMOS)‐compatible Ti/SiN_x/p⁺‐Si memristor by simply reducing the cell size down to sub‐100 nm. Even though the switching voltages gradually increase with decreasing device size, the reset current is reduced because of the reduced current overshoot effect. The scaled devices (sub‐100 nm) exhibit gradual reset switching driven by the electric field, whereas that of the large devices (≥1 µm) is driven by Joule heating. For the scaled cell (60 nm), the current levels are tunable by adjusting the reset stop voltage for multilevel cells. It is revealed that the nonlinearity in the low‐resistance state is attributed to Fowler–Nordheim tunneling dominating in the high‐voltage regime (≥1 V) for the scaled cells. The experimental findings demonstrate that the scaled metal–nitride–silicon memristor device paves the way to realize CMOS‐compatible high‐density crosspoint array applications.     

Physically Transient Threshold Switching Device Based on Magnesium Oxide for Security Application

Transient memristors are prospective candidates for both secure memory systems and biointegrated electronics, which are capable to physically disappear at a programmed time with a triggered operation. However, the sneak current issue has been a considerable obstacle to achieve high‐density transient crossbar array of memristors. To solve this problem, it is necessary to develop a transient switch device to turn the memory device on and off controllably. Here, a dissolvable and flexible threshold switching (TS) device with a vertically crossed structure is introduced, which exhibits a high selectivity of 10⁷, steep turn‐on slope of <8 mV dec⁻¹, and fast ON/OFF switch speed within 50/25 ns. Triggered failure could be achieved after soaking the device in deionized water for 8 min at room temperature. Furthermore, a water‐assisted transfer printing method is used to fabricate flexible and transient TS device arrays for bioresorbable systems, in which none of any significant degradation is observed under a bending radius of 2 mm. Integrating the selector with a transient memristor is capable of 10⁷ Gb memory implementation, indicating that the transient TS device could provide great opportunities to achieve highly integrated transient memory arrays.     

All‐Inorganic Ionic Polymer‐Based Memristor for High‐Performance and Flexible Artificial Synapse

The implementation of memristors that are wearable and transparent has attracted significant attention. However, the development of high‐performance memristors that simultaneously possess high flexibility and environmental stability has remained a tremendous challenge suffering from limited choice of materials with both good ion‐electron mobility and structural flexibility. Inspired by the unique poly‐ionic nature of ammonium polyphosphate (APP), a novel Au/APP/ITO memristor with favorable flexibility and stability is prepared. Synaptic behaviors can be stimulated by voltage pulses that are 20 ns in width, 0.1 V in amplitude, and repeatable under 10⁴ pulse cycles, thereby outperforming several other benchmark memristors. Further, the device, prepared on conductive silicone, can sustain its synaptic performance even under 360° bending. Furthermore, the device can sustain its synaptic behaviors even after exposure to fire for 60 s and 5.6 kGy of ionic irradiation. Additionally, APP is determined to be nontoxic, biodegradable, and transparent when compared with all the organics and inorganics used in previous memristors. The results of this study will inspire the development of more inorganic polymers for their utilization in future environmentally stable and flexible electronics.     

Ligand Exchange Reaction Enables Digital-To-Analog Resistive Switching and Artificial Synapse within Metal Nanoparticles

The transition between digital and analog resistive switching in a single memristive device is beneficial for the reduction in power consumption and circuit complexity, the development of in-memory neuromorphic computing, and the discovery of new switching mechanisms. However, achieving such transition is a challenge due to the complex switching mechanisms and device designs. Here, it is shown that the digital-to-analog resistive switching can be realized by the ligand exchange reaction of metal nanoparticles. The field-injected copper cations migrate within carboxyl-functionalized gold nanoparticle (AuNP) layer that are subsequently reduced into metallic filaments, enabling an abrupt resistive switching. Importantly, when the carboxyl groups on the gold nanoparticle are replaced by amino-carboxyl ligands, the copper cations coordinate with the new ligands and create the conductance bridges to reduce the electron tunneling/hopping energy barriers, leading to continuous modulation in conductivity. This analog resistive switching allows to implement several important synaptic functions such as potentiation/depression, paired-pulse facilitation, learning behaviors including forgetting curves and spaced learning effect. In the end, due to the non-volatile characteristics, the gold nanoparticle synapse is used to build single layer perceptron for pattern classification with 100% accuracy.     

All-Optically Controlled Retinomorphic Memristor for Image Processing and Stabilization

Image stabilization is a crucial field in machine vision, aiming to eliminate image blurring or distortion caused by the camera or object jitter. However, traditional image stabilization techniques often suffer from the drawbacks of requiring complex equipment or extensive computing resources, resulting in inefficiencies. In contrast, the human retina performs a highly efficient all-in-one system, encompassing the detection and processing of light stimuli. In this study, an all-optically controlled retinomorphic memristor based on the Cs_xFA_yMA_1-x-yPb(I_zBr_1-z)₃ is proposed, which integrates perception, storage, and processing functions. This memristor exhibits significant advantages in image stabilization. It is capable of positively and negatively modulating its conductance using specific intensities (11.8 and 0.9 mW cm⁻², respectively) of red light (630 nm). To demonstrate the effectiveness of the proposed approach, handwritten digit recognition simulations are conducted. The application of specific light stimuli effectively highlights the characteristics of blurred images. The processed images are then fed into a conductance-mapped neural network for rapid recognition. Remarkably, the recognition rates of the processed images reach 83.5% after 19 000 iterations, surpassing the performance of blurred images (only 56.2% after 19 000 iterations). These results highlight the immense potential of retinomorphic memristors as the hardware foundation for next-generation image stabilization systems.     

Graphene Oxide Quantum Dots Based Memristors with Progressive Conduction Tuning for Artificial Synaptic Learning

Memristors as electronic artificial synapses have attracted increasing attention in neuromorphic computing. Emulation of both “learning” and “forgetting” processes requires a bidirectional progressive adjustment of memristor conductance, which is a challenge for cutting‐edge artificial intelligence. In this work, a memristor device with a structure of Ag/Zr_0.5Hf_0.5O₂:graphene oxide quantum dots/Ag is presented with the feature of bidirectional progressive conductance tuning. The conductance of proposed memristor is adjusted through voltage pulse number, amplitude, and width. A series of voltage pulses with an amplitude of 0.6 V and a width of 30 ns is enough to modulate conductance. The impacts of pulses with different parameters on conductance modulation are investigated, and the potential relationship between pulse amplitude and energy is revealed. Furthermore, it is proved that the pulse with low energy can realize the almost linear conductance regulation, which is beneficial to improve the accuracy of pattern recognition. The bidirectional progressive conduction modulation mimics various plastic synapses, such as spike‐timing‐dependent plasticity and paired‐pulse facilitation. This progressive conduction tuning mechanism might be attributed to the coexistence of tunneling effect and extrinsic electrochemical metallization effect. This work provides one way for memristor to attain attractive features such as bidirectional tuning, low‐power consumption, and fast speed switching that is in urgent demand for further evolution of neuromorphic chips.     

忆阻器网络等效分析电路及其特性研究

随着忆阻器理论研究的不断深入和圆满实现,加上其独特的物理性质,其应用前景将非常乐观。该文提出了忆阻器有P型和N型两种,对其物理性质进行了分析研究,得出了它们具有对偶特征。基于此,忆阻器间可以通过适当的连接和参数选择,对外呈现出线性特性,而各自仍具备忆阻器的固有属性。同时,给出了忆阻器及其串并联的一种等效分析电路拓扑结构,仿真结果表明,该方法有效、结论正确,从而为忆阻器的理论和应用研究,特别是忆阻器网路的构建开辟了新途径。     

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.