Electrostatic Gating of Spin Dynamics of a Quasi-2D Kagome Magnet.

Electrostatic gating has emerged as a powerful technique for tailoring the magnetic properties of two-dimensional (2D) magnets, offering exciting prospects including enhancement of magnetic anisotropy, boosting Curie temperature, and strengthening exchange coupling effects. Here, we focus on electrical control of the ferromagnetic resonance of the quasi-2D Kagome magnet Cu(1,3-bdc). By harnessing an electrostatic field through ionic liquid gating, significant shifts are observed in the ferromagnetic resonance field in both out-of-plane and in-plane measurements. Moreover, the effective magnetization and gyromagnetic ratios display voltage-dependent variations. A closer examination reveals that the voltage-induced changes can modulate magnetocrystalline anisotropy by several hundred gauss, while the impact on orbital magnetization remains relatively subtle. Density functional theory (DFT) calculations reveal varying d-orbital hybridizations at different voltages. This research unveils intricate physics within the Kagome lattice magnet and further underscores the potential of electrostatic manipulation in steering magnetism with promising implications for the development of spintronic devices.

Manipulating 2D Materials through Strain Engineering.

This review explores the growing interest in 2D layered materials, such as graphene, h-BN, transition metal dichalcogenides (TMDs), and black phosphorus (BP), with a specific focus on recent advances in strain engineering. Both experimental and theoretical results are delved into, highlighting the potential of strain to modulate physical properties, thereby enhancing device performance. Various strain engineering methods are summarized, and the impact of strain on the electrical, optical, magnetic, thermal, and valleytronic properties of 2D materials is thoroughly examined. Finally, the review concludes by addressing potential applications and challenges in utilizing strain engineering for functional devices, offering valuable insights for further research and applications in optoelectronics, thermionics, and spintronics.

Structure, bonding and electronic characteristics of amorphous Se.

Ovonic threshold switching (OTS) selectors can effectively improve the storage density and suppress the leakage current of advanced phase-change memory devices. As a prototypical OTS material, amorphous GeSe is widely investigated. But the attention paid to amorphous Se (i.e., the functional constituent in amorphous GeSe) has been very limited up to now. Here we have explored the structure, bonding and electronic characteristics of amorphous Se using ab initio molecular dynamics simulations. The results reveal that the Se atoms in amorphous Se tend to form 2-coordinated configurations, and they connect with each other to form long chains. The fraction of the vibrational density of state located in the high frequency range is relatively large, and the formation energy of the Se-Se bond is as large as 4.44 eV, hinting that the Se-Se bonds in chains possess high stability. In addition, the mid-gap state related to the OTS behavior is also found in amorphous Se despite the small proportion. Our findings enrich the knowledge of amorphous Se, which aids the applications of Se-based OTS selectors.

Detection of Biomolecules Using Solid-State Nanopores Fabricated by Controlled Dielectric Breakdown.

Nanopore sensor technology is widely used in biomolecular detection due to its advantages of low cost and easy operation. In a variety of nanopore manufacturing methods, controlled dielectric breakdown has the advantages of a simple manufacturing process and low cost under the premise of ensuring detection performance. In this paper, we have made enhancements to the applied pulses in controlled dielectric breakdown and utilized the improved dielectric breakdown technique to fabricate silicon nitride nanopores with diameters of 5 to 15 nm. Our improved fabrication method offers the advantage of precise control over the nanopore diameter (±0.4 nm) and enhances the symmetry of the nanopore. After fabrication, we performed electrical characterization on the nanopores, and the IV characteristics exhibited high linearity. Subsequently, we conducted detection experiments for DNA and protein using the prepared nanopores to assess the detection performance of the nanopores fabricated using our method. In addition, we also give a physical model of molecule translocation through the nanopores to give a reasonable explanation of the data processing results.

2D van der Waals Vertical Heterojunction Transistors for Ternary Neural Networks.

Compared with binary systems, ternary computing systems can utilize fewer devices to realize the same information density. However, most ternary computing systems based on binary CMOS circuits require additional devices to bridge binary processing and ternary computing. Exploring new device architectures for direct ternary processing and computing becomes the key to promoting ternary computing systems. Here, we demonstrated a 2D van der Waals vertical heterojunction transistor (V-HTR) with three flat conductance states, which can be the basic cell in ternary circuits to perform ternary processing and computing, without additional devices. A ternary neural network (TNN) and a ternary inverter were demonstrated based on the V-HTRs. The TNN can eliminate fuzzy data and output only clear data by building a ternary quantization function. By demonstrating both ternary logic and a TNN on the same device architecture, the 2D V-HTR shows potential as a basic hardware unit for future ternary computing systems.

Enhanced stretchability towards a flexible and wearable reflective display coating using chalcogenide phase change materials.

Reflective color switching coatings based on chalcogenide phase change material (PCM) is becoming one of the most promising display technologies in the future. However, it is still a challenge to kindly control the stress and enhance the stretchability for flexible display coatings. Here, we report crack-reduced reflective color coatings on a flexible substrate by using buckling structure to regulate the distribution of vacancies in PCM. It significantly suppresses the formation of cracks and improves the robustness of optical and electrical properties during stretching of the display device, which opens the doors of opportunity for phase change display applications in a wide range of flexible and wearable display fields.

On the self-consistency of DFT-1/2.

Density functional theory (DFT)-1/2 is an efficient bandgap rectification method for DFT under local density approximation (LDA) or generalized gradient approximation. It was suggested that non-self-consistent DFT-1/2 should be used for highly ionic insulators like LiF, whereas self-consistent DFT-1/2 should still be used for other compounds. Nevertheless, there is no quantitative criterion prescribed for which implementation should work for an arbitrary insulator, which leads to severe ambiguity in this method. In this work, we analyze the impact of self-consistency in DFT-1/2 and shell DFT-1/2 calculations in insulators or semiconductors with ionic bonds, covalent bonds, and intermediate cases and show that self-consistency is required even for highly ionic insulators for globally better electronic structure details. The self-energy correction renders electrons more localized around the anions in self-consistent LDA-1/2. The well-known delocalization error of LDA is rectified, but with strong overcorrection, due to the presence of additional self-energy potential. However, in non-self-consistent LDA-1/2 calculations, the electron wave functions indicate that such localization is much more severe and beyond a reasonable range because the strong Coulomb repulsion is not counted in the Hamiltonian. Another common drawback of non-self-consistent LDA-1/2 is that the ionicity of the bonding gets substantially enhanced, and the bandgap can be enormously high in mixed ionic-covalent compounds like TiO2.

Emerging memristive neurons for neuromorphic computing and sensing.

Inspired by the principles of the biological nervous system, neuromorphic engineering has brought a promising alternative approach to intelligence computing with high energy efficiency and low consumption. As pivotal components of neuromorphic system, artificial spiking neurons are powerful information processing units and can achieve highly complex nonlinear computations. By leveraging the switching dynamic characteristics of memristive device, memristive neurons show rich spiking behaviors with simple circuit. This report reviews the memristive neurons and their applications in neuromorphic sensing and computing systems. The switching mechanisms that endow memristive devices with rich dynamics and nonlinearity are highlighted, and subsequently various nonlinear spiking neuron behaviors emulated in these memristive devices are reviewed. Then, recent development is introduced on neuromorphic system with memristive neurons for sensing and computing. Finally, we discuss challenges and outlooks of the memristive neurons toward high-performance neuromorphic hardware systems and provide an insightful perspective for the development of interactive neuromorphic electronic systems.

Research Progress on the Application of Topological Phase Transition Materials in the Field of Memristor and Neuromorphic Computing.

Topological phase transition materials have strong coupling between their charge, spin orbitals, and lattice structure, which makes them have good electrical and magnetic properties, leading to promising applications in the fields of memristive devices. The smaller Gibbs free energy difference between the topological phases, the stable oxygen vacancy ordered structure, and the reversible topological phase transition promote the memristive effect, which is more conducive to its application in information storage, information processing, information calculation, and other related fields. In particular, extracting the current resistance or conductance of the two-terminal memristor to convert to the weight of the synapse in the neural network can simulate the behavior of biological synapses in their structure and function. In addition, in order to improve the performance of memristors and better apply them to neuromorphic computing, methods such as ion doping, electrode selection, interface modulation, and preparation process control have been demonstrated in memristors based on topological phase transition materials. At present, it is considered an effective method to obtain a unique resistive switching behavior by improving the process of preparing functional layers, regulating the crystal phase of topological phase transition materials, and constructing interface barrier-dependent devices. In this review, we systematically expound the resistance switching mechanism, resistance switching performance regulation, and neuromorphic computing of topological phase transition memristors, and provide some suggestions for the challenges faced by the development of the next generation of non-volatile memory and brain-like neuromorphic devices based on topological phase transition materials.

Adaptive Synaptic Memory via Lithium Ion Modulation in RRAM Devices.

Biologically plausible computing systems require fine-grain tuning of analog synaptic characteristics. In this study, lithium-doped silicate resistive random access memory with a titanium nitride (TiN) electrode mimicking biological synapses is demonstrated. Biological plausibility of this RRAM device is thought to occur due to the low ionization energy of lithium ions, which enables controllable forming and filamentary retraction spontaneously or under an applied voltage. The TiN electrode can effectively store lithium ions, a principle widely adopted from battery construction, and allows state-dependent decay to be reliably achieved. As a result, this device offers multi-bit functionality and synaptic plasticity for simulating various strengths in neuronal connections. Both short-term memory and long-term memory are emulated across dynamical timescales. Spike-timing-dependent plasticity and paired-pulse facilitation are also demonstrated. These mechanisms are capable of self-pruning to generate efficient neural networks. Time-dependent resistance decay is observed for different conductance values, which mimics both biological and artificial memory pruning and conforms to the trend of the biological brain that prunes weak synaptic connections. By faithfully emulating learning rules that exist in human's higher cortical areas from STDP to synaptic pruning, the device has the capacity to drive forward the development of highly efficient neuromorphic computing systems.

Dynamically tunable transmissive color filters using ultra-thin phase change materials.

Structural color filters (i.e. plasmonics and nano-cavities) provide vivid and robust color filtering in applications such as CMOS image sensors but lack simplicity in fabrication and dynamic tuning. Here we report a dynamically tunable, transmissive color filter by incorporating an ultra-thin phase change layer inside a thin-film optical resonator. The transmitted color spectrum can be designed over the entire visible range and shifted by around 50 nm after phase transition. Angle dependence shows little color variation within a ±30° viewing angle. Crucially, only film deposition is required to fabricate our phase change color filter, showing great potential for large-scale and inexpensive production. The dynamically tunable color filter, described in this paper, could be a promising component in display, CMOS sensor, and solar cell technology.

Enhancing the efficiency of energy harvesting from salt gradient with ion-selective nanochannel.

The development of a nanofluidic energy harvesting system plays a fundamental role in harvesting osmotic power from Gibbs free energy within the salt concentration gradient, which is considered to be a clean and renewable energy source for the future. In this study, a silica-nanochannel based nanofluidic energy harvesting system was fabricated and the output power density reached 705 W m-2 under suitable KCl concentration bias which exceeded-by almost two orders of magnitude-the results obtained by previous work. The enhancement of energy harvesting was mainly ascribed to the appropriate length of nanochannel that provides a good balance between the desirable ion selectivity and the unfavorable large resistance from the nanochannel. This high-performance nanofluidic energy device could be used in a variety of applications, including to power tiny biomedical devices or for constructing future clean-energy recovery plants.

Stabilizing amorphous Sb by adding alien seeds for durable memory materials.

The thermal stability of the amorphous phase is a key property of phase-change memory, which limits the data retention time and device reliability. The high thermal stability of memory devices enables their applications in harsh environments and under extreme conditions. Here, we discovered that the alloying of C, Si and Ge significantly improves the stability of amorphous Sb by adding "alien" tetrahedral seeds to the octahedral matrix. This doping strategy impedes the crystallization at elevated temperatures so that the crystallization temperature of Sb is increased by 170-220 °C. The mechanism is systematically investigated by ab initio molecular dynamics simulations and classical crystal growth theory. We confirm that the alien tetrahedral bonds increase the activation energy of atomic migration upon crystallization. Our results demonstrate an effective alloying strategy to improve the thermal stability of phase change memory, paving the way for the design of durable memory devices.

Impact of ionization equilibrium on electrokinetic flow of weak electrolytes in nanochannels.

Weak electrolyte transport in nanochannels or nanopores has been actively explored in recent experiments. In this paper, we establish a new electrokinetic model where the ionization balance effect of weak electrolytes is outlined, and performed numerical calculations for H3PO4 concentration-biased nanochannel systems. By considering the roles of local chemical equilibrium in phosphorous acid ionization, the simulation results show quantitative agreement with experimental observations. Based on the model, we predict that enhanced energy harvesting capacity could be accomplished by utilizing weak electrolytes compared to the conventional strong electrolyte approaches in a concentration gradient-based power-generating system.

Reconfigurable logic in nanosecond Cu/GeTe/TiN filamentary memristors for energy-efficient in-memory computing.

Owing to the capability of integrating the information storage and computing in the same physical location, in-memory computing with memristors has become a research hotspot as a promising route for non von Neumann architecture. However, it is still a challenge to develop high performance devices as well as optimized logic methodologies to realize energy-efficient computing. Herein, filamentary Cu/GeTe/TiN memristor is reported to show satisfactory properties with nanosecond switching speed (<60 ns), low voltage operation (<2 V), high endurance (>104 cycles) and good retention (>104 s @85 °C). It is revealed that the charge carrier conduction mechanisms in high resistance and low resistance states are Schottky emission and hopping transport between the adjacent Cu clusters, respectively, based on the analysis of current-voltage behaviors and resistance-temperature characteristics. An intuitive picture is given to describe the dynamic processes of resistive switching. Moreover, based on the basic material implication (IMP) logic circuit, we proposed a reconfigurable logic method and experimentally implemented IMP, NOT, OR, and COPY logic functions. Design of a one-bit full adder with reduction in computational sequences and its validation in simulation further demonstrate the potential practical application. The results provide important progress towards understanding of resistive switching mechanism and realization of energy-efficient in-memory computing architecture.

Pr-based metallic glass films used as resist for phase-change lithography.

Metallic glass film of Pr60Al10Ni10Cu20 is proposed to be used as a resist of phase-change lithography (PCL). PCL is a mask-less lithography technology by using laser-direct-writing to create the intended nanopatterns. Thermal distribution in the PrAlNiCu film after exposure is calculated by finite element method (FEM). Thin films are exposed by continuous-wave laser and selective etched by nitric-acid solution, and the patterns are discerned by optical and atomic force microscope. The etching rate of as-deposited PrAlNiCu is thus nearly five times of the crystalline film. These results indicate that PrAlNiCu metallic glass film is a promising resist for phase-change lithography.

Mimicking the brain functions of learning, forgetting and explicit/implicit memories with SrTiO3-based memristive devices.

To implement the complex brain functions of learning, forgetting and memory in a single electronic device is very advantageous for realizing artificial intelligence. As a proof of concept, memristive devices with a simple structure of Ni/Nb-SrTiO3/Ti were investigated in this work. The functions of learning, forgetting and memory were successfully mimicked using the memristive devices, and the "time-saving" effect of implicit memory was also demonstrated. The physics behind the brain functions is simply the modulation of the Schottky barrier at the Ni/SrTiO3 interface. The realization of various psychological functions in a single device simplifies the construction of the artificial neural network and facilitates the advent of artificial intelligence.

Magnetic-ferroelectric synergic control of multilevel conducting states in van der Waals multiferroic tunnel junctions towards in-memory computing.

van der Waals (vdW) multiferroic tunnel junctions (MFTJs) based on two-dimensional materials have gained significant interest due to their potential applications in next-generation data storage and in-memory computing devices. In this study, we construct vdW MFTJs by employing monolayer Mn2Se3 as the spin-filter tunnel barrier, TiTe2 as the electrodes and In2S3 as the tunnel barrier to investigate the spin transport properties based on first-principles quantum transport calculations. It is highlighted that apparent tunneling magnetoresistance (TMR) and tunneling electroresistance (TER) effects with a maximum TMR ratio of 6237% and TER ratio of 1771% can be realized by using bilayer In2S3 as the tunnel barrier under finite bias. Furthermore, the physical origin of the distinguished TMR and TER effects is unraveled from the k||-resolved transmission spectra and spin-dependent projected local density of states analysis. Interestingly, four distinguishable conductance states reveal the implementation of four-state nonvolatile data storage using one MFTJ unit. More importantly, in-memory logic computing and multilevel data storage can be achieved at the same time by magnetic switching and electrical control, respectively. These results shed light on vdW MFTJs in the applications of in-memory computing as well as multilevel data storage devices.

Ovonic threshold switching-based artificial afferent neurons for thermal in-sensor computing.

Artificial afferent neurons in the sensory nervous system inspired by biology have enormous potential for efficiently perceiving and processing environmental information. However, the previously reported artificial afferent neurons suffer from two prominent challenges: considerable power consumption and limited scalability efficiency. Herein, addressing these challenges, a bioinspired artificial thermal afferent neuron based on a N-doped SiTe ovonic threshold switching (OTS) device is presented for the first time. The engineered OTS device shows remarkable uniformity and robust endurance, ensuring the reliability and efficacy of the artificial afferent neurons. A substantially decreased leakage current of the SiTe OTS device by nitrogen doping results in ultra-low power consumption less than 0.3 nJ per spike for artificial afferent neurons. The inherent temperature response exhibited by N-doped SiTe OTS materials allows us to construct a highly compact artificial thermal afferent neuron over a wide temperature range. An edge detection task is performed to further verify its thermal perceptual computing function. Our work provides an insight into OTS-based artificial afferent neurons for electronic skin and sensory neurorobotics.

Resistive Memory Devices at the Thinnest Limit: Progress and Challenges.

The Si-based integrated circuits industry has been developing for more than half a century, by focusing on the scaling-down of transistor. However, the miniaturization of transistors will soon reach its physical limits, thereby requiring novel material and device technologies. Resistive memory is a promising candidate for in-memory computing and energy-efficient synaptic devices that can satisfy the computational demands of the future applications. However, poor cycle-to-cycle and device-to-device uniformities hinder its mass production. 2D materials, as a new type of semiconductor, is successfully employed in various micro/nanoelectronic devices and have the potential to drive future innovation in resistive memory technology. This review evaluates the potential of using the thinnest advanced materials, that is, monolayer 2D materials, for memristor or memtransistor applications, including resistive switching behavior and atomic mechanism, high-frequency device performances, and in-memory computing/neuromorphic computing applications. The scaling-down advantages of promising monolayer 2D materials including graphene, transition metal dichalcogenides, and hexagonal boron nitride are presented. Finally, the technical challenges of these atomic devices for practical applications are elaborately discussed. The study of monolayer-2D-material-based resistive memory is expected to play a positive role in the exploration of beyond-Si electronic technologies.