Emerging memristors and applications in reservoir computing

Recently, with the emergence of ChatGPT, the field of artificial intelligence has garnered widespread attention from various sectors of society. Reservoir Computing (RC) is a neuromorphic computing algorithm used to analyze time-series data. Unlike traditional artificial neural networks that require the weight values of all nodes in the trained network, RC only needs to train the readout layer. This makes the training process faster and more efficient, and it has been used in various applications, including speech recognition, image classification, and control systems. Its flexibility and efficiency make it a popular choice for processing large amounts of complex data. A recent research trend is to develop physical RC, which utilizes the nonlinear dynamic and short-term memory properties of physical systems (photonic modules, spintronic devices, memristors, etc.) to construct a fixed random neural network structure for processing input data to reduce computing time and energy. In this paper, we introduced the recent development of memristors and demonstrated the remarkable data processing capability of RC systems based on memristors. Not only do they possess excellent data processing ability comparable to digital RC systems, but they also have lower energy consumption and greater robustness. Finally, we discussed the development prospects and challenges faced by memristors-based RC systems.

STCF conceptual design report (Volume 1): Physics & detector

The superτ-charm facility(STCF)is an electron–positron collider proposed by the Chinese particle physics community.It is designed to operate in a center-of-mass energy range from 2 to 7 GeV with a peak luminosity of 0.5×10^(35) cm^(–2)·s^(–1) or higher.The STCF will produce a data sample about a factor of 100 larger than that of the presentτ-charm factory—the BEPCII,providing a unique platform for exploring the asymmetry of matter-antimatter(charge-parity violation),in-depth studies of the internal structure of hadrons and the nature of non-perturbative strong interactions,as well as searching for exotic hadrons and physics beyond the Standard Model.The STCF project in China is under development with an extensive R&D program.This document presents the physics opportunities at the STCF,describes conceptual designs of the STCF detector system,and discusses future plans for detector R&D and physics case studies.

Two-dimensional polarized MoSSe/MoTe2 van der Waals heterostructure: A polarization-tunable optoelectronic material

Two-dimensional (2D) heterostructures have shown great potential in advanced photovoltaics due to their restrained carrier recombination, prolonged exciton lifetime and improved light absorption. Herein, a 2D polarized heterostructure is constructed between Janus MoSSe and MoTe_(2) monolayers and is systematically investigated via first-principles calculations. Electronically, the valence band and conduction band of the MoSSe−MoTe_(2) (MoSeS−MoTe_(2)) are contributed by MoTe_(2) and MoSSe layers, respectively, and its bandgap is 0.71 (0.03) eV. A built-in electric field pointing from MoTe_(2) to MoSSe layers appears at the interface of heterostructures due to the interlayer carrier redistribution. Notably, the band alignment and built-in electric field make it a direct z-scheme heterostructure, benefiting the separation of photogenerated electron-hole pairs. Besides, the electronic structure and interlayer carrier reconstruction can be readily controlled by reversing the electric polarization of the MoSSe layer. Furthermore, the light absorption of the MoSSe/MoTe_(2) heterostructure is also improved in comparison with the separated monolayers. Consequently, in this work, a new z-scheme polarized heterostructure with polarization-controllable optoelectronic properties is designed for highly efficient optoelectronics.

Recent advances in memristors based on two-dimensional ferroelectric materials

In this big data era, the explosive growth of information puts ultra-high demands on the data storage/computing, such as high computing power, low energy consumption, and excellent stability. However, facing this challenge, the traditional von Neumann architecture-based computing system is out of its depth owing to the separated memory and data processing unit architecture. One of the most effective ways to solve this challenge is building brain inspired computing system with in-memory computing and parallel processing ability based on neuromorphic devices. Therefore, there is a research trend toward the memristors, that can be applied to build neuromorphic computing systems due to their large switching ratio, high storage density, low power consumption, and high stability. Two-dimensional (2D) ferroelectric materials, as novel types of functional materials, show great potential in the preparations of memristors because of the atomic scale thickness, high carrier mobility, mechanical flexibility, and thermal stability. 2D ferroelectric materials can realize resistive switching (RS) because of the presence of natural dipoles whose direction can be flipped with the change of the applied electric field thus producing different polarizations, therefore, making them powerful candidates for future data storage and computing. In this review article, we introduce the physical mechanisms, characterizations, and synthetic methods of 2D ferroelectric materials, and then summarize the applications of 2D ferroelectric materials in memristors for memory and synaptic devices. At last, we deliberate the advantages and future challenges of 2D ferroelectric materials in the application of memristors devices.

A high-speed true random number generator based on Ag/SiNx/n-Si memristor

The intrinsic variability of memristor switching behavior can be used as a natural source of randomness,this variability is valuable for safe applications in hardware,such as the true random number generator(TRNG).However,the speed of TRNG is still be further improved.Here,we propose a reliable Ag/SiNx/n-Si volatile memristor,which exhibits a typical threshold switching device with stable repeat ability and fast switching speed.This volatile-memristor-based TRNG is combined with nonlinear feedback shift register(NFSR)to form a new type of high-speed dual output TRNG.Interestingly,the bit generation rate reaches a high speed of 112 kb/s.In addition,this new TRNG passed all 15 National Institute of Standards and Technology(NIST)randomness tests without post-processing steps,proving its performance as a hardware security application.This work shows that the SiNx-based volatile memristor can realize TRNG and has great potential in hardware network security.

Advances of machine learning in materials science: Ideas and techniques

In this big data era, the use of large dataset in conjunction with machine learning (ML) has been increasingly popular in both industry and academia. In recent times, the field of materials science is also undergoing a big data revolution, with large database and repositories appearing everywhere. Traditionally, materials science is a trial-and-error field, in both the computational and experimental departments. With the advent of machine learning-based techniques, there has been a paradigm shift: materials can now be screened quickly using ML models and even generated based on materials with similar properties;ML has also quietly infiltrated many sub-disciplinary under materials science. However, ML remains relatively new to the field and is expanding its wing quickly. There are a plethora of readily-available big data architectures and abundance of ML models and software;The call to integrate all these elements in a comprehensive research procedure is becoming an important direction of material science research. In this review, we attempt to provide an introduction and reference of ML to materials scientists, covering as much as possible the commonly used methods and applications, and discussing the future possibilities.

Recent advances in laser self-injection locking to high-Q microresonators

The stabilization and manipulation of laser frequency by means of an external cavity are nearly ubiquitously used in fundamental research and laser applications. While most of the laser light transmits through the cavity, in the presence of some back-scattered light from the cavity to the laser, the self-injection locking effect can take place, which locks the laser emission frequency to the cavity mode of similar frequency. The self-injection locking leads to dramatic reduction of laser linewidth and noise. Using this approach, a common semiconductor laser locked to an ultrahigh-Q microresonator can obtain sub-Hertz linewidth, on par with state-of-the-art fiber lasers. Therefore it paves the way to manufacture high-performance semiconductor lasers with reduced footprint and cost. Moreover, with high laser power, the optical nonlinearity of the microresonator drastically changes the laser dynamics, offering routes for simultaneous pulse and frequency comb generation in the same microresonator. Particularly, integrated photonics technology, enabling components fabricated via semiconductor CMOS process, has brought increasing and extending interest to laser manufacturing using this method. In this article, we present a comprehensive tutorial on analytical and numerical methods of laser self-injection locking, as well a review of most recent theoretical and experimental achievements.

Recent developments in CVD growth and applications of 2D transition metal dichalcogenides

Two-dimensional(2D)transition metal dichalcogenides(TMDs)with fascinating electronic energy band structures,rich valley physical properties and strong spin–orbit coupling have attracted tremendous interest,and show great potential in electronic,optoelectronic,spintronic and valleytronic fields.Stacking 2D TMDs have provided unprecedented opportunities for constructing artificial functional structures.Due to the low cost,high yield and industrial compatibility,chemical vapor deposition(CVD)is regarded as one of the most promising growth strategies to obtain high-quality and large-area 2D TMDs and heterostructures.Here,state-of-the-art strategies for preparing TMDs details of growth control and related heterostructures construction via CVD method are reviewed and discussed,including wafer-scale synthesis,phase transition,doping,alloy and stacking engineering.Meanwhile,recent progress on the application of multi-functional devices is highlighted based on 2D TMDs.Finally,challenges and prospects are proposed for the practical device applications of 2D TMDs.

Moisture influence in emerging neuromorphic device

Conduction filament formation,redox reaction,and mobile ion migration in solid electrolytes underpin the memristive devices,all of which are partially influenced or fully dominated by the moisture.The moisture-based physical-chemistry mechanism provides an electric tunable method to create enough dissociate conductance states for neuromorphic computing,but overconcentration moisture will corrode electrode and then causes device invalidation.This perspective goal is that surveys the moisture-dependency of dynamic at interfaces or/and switching function layer,clarifies the bottlenecks that the memristive device facing in terms of water molecule-related reaction,and gives the possible solutions.

A multi-band atomic candle with microwave-dressed Rydberg atoms

Stabilizing important physical quantities to atom-based standards lies at the heart of modern atomic,molecular and optical physics,and is widely applied to the field of precision metrology.Of particular importance is the atom-based microwave field amplitude stabilizer,the so-called atomic candle.Previous atomic candles are realized with atoms in their ground state,and hence suffer from the lack of frequency band tunability and small stabilization bandwidth,severely limiting their development and potential applications.To tackle these limitations,we employ microwave-dressed Rydberg atoms to realize a novel atomic candle that features multi-band frequency tunability and large stabilization bandwidth.We demonstrate amplitude stabilization of microwave field from C-band to Ka-band,which could be extended to quasi-DC and terahertz fields by exploring abundant Rydberg levels.Our atomic candle achieves stabilization bandwidth of 100 Hz,outperforming previous ones by more than two orders of magnitude.Our simulation indicates the stabilization bandwidth can be further increased up to 100 kHz.Our work paves a route to develop novel electric field control and applications with a noise-resilient,miniaturized,sensitive and broadband atomic candle.

Reconfigurable memristor based on SrTiO_(3) thin-film for neuromorphic computing

Neuromorphic computing aims to achieve artificial intelligence by mimicking the mechanisms of biological neurons and synapses that make up the human brain.However,the possibility of using one reconfigurable memristor as both artificial neuron and synapse still requires intensive research in detail.In this work,Ag/SrTiO_(3)(STO)/Pt memristor with low operating voltage is manufactured and reconfigurable as both neuron and synapse for neuromorphic computing chip.By modulating the compliance current,two types of resistance switching,volatile and nonvolatile,can be obtained in amorphous STO thin film.This is attributed to the manipulation of the Ag conductive filament.Furthermore,through regulating electrical pulses and designing bionic circuits,the neuronal functions of leaky integrate and fire,as well as synaptic biomimicry with spike-timing-dependent plasticity and paired-pulse facilitation neural regulation,are successfully realized.This study shows that the reconfigurable devices based on STO thin film are promising for the application of neuromorphic computing systems.

Janus monolayer TaNF:A new ferrovalley material with large valley splitting and tunable magnetic properties

Materials with large intrinsic valley splitting and high Curie temperature are a huge advantage for studying valleytronics and practical applications.In this work,using first-principles calculations,a new Janus TaNF monolayer is predicted to exhibit excellent piezoelectric properties and intrinsic valley splitting,resulting from the spontaneous spin polarization,the spatial inversion symmetry breaking and strong spin-orbit coupling(SOC).TaNF is also a potential two-dimensional(2D)magnetic material due to its high Curie temperature and large magnetic anisotropy energy.The effective control of the band gap of TaNF can be achieved by biaxial strain,which can transform TaNF monolayer from semiconductor to semi-metal.The magnitude of valley splitting at the CBM can be effectively tuned by biaxial strain due to the changes of orbital composition at the valleys.The magnetic anisotropy energy(MAE)can be manipulated by changing the energy and occupation(unoccupation)states of d orbital compositions through biaxial strain.In addition,Curie temperature reaches 373 K under only−3%biaxial strain,indicating that Janus TaNF monolayer can be used at high temperatures for spintronic and valleytronic devices.

Bifunctional oxygen electrocatalysts for rechargeable zinc-air battery based on MXene and beyond

Oxygen electrocatalysts are of great importance for the air electrode in zinc–air batteries(ZABs).Owing to large surface area,high electrical conductivity and ease of modification,two-dimensional(2D)materials have been widely studied as oxygen electrocatalysts for the rechargable ZABs.The elaborately modified 2D materials-based electrocatalysts,usually exhibit excellent performance toward the oxygen reduction reaction(ORR)and oxygen evolution reaction(OER),which have attracted extensive interests of worldwide researchers.Given the rapid development of bifunctional electrocatalysts toward ORR and OER,the latest progress of non-noble electrocatalysts based on layered double hydroxides(LDHs),graphene,and MXenes are intensively reviewed.The discussion ranges from fundamental structure,synthesis,electrocatalytic performance of these catalysts,as well as their applications in the rechargeable ZABs.Finally,the challenges and outlook are provided for further advancing the commercialization of rechargeable ZABs.

Flexible and ultrathin dopamine modified MXene and cellulose nanofiber composite films with alternating multilayer structure for superior electromagnetic interference shielding performance

With the development of modern electronics,especially the next generation of wearable electromagnetic interference(EMI)shielding materials requires flexibility,ultrathin,lightweight and robustness to protect electronic devices from radiation pollution.In this work,the flexible and ultrathin dopamine modified MXene@cellulose nanofiber(DM@CNF)composite films with alternate multilayer structure have been developed by a facile vacuum filtration induced self-assembly approach.The multilayered DM@CNF composite films exhibit improved mechanical properties compared with the homogeneous DM/CNF film.By adjusting the layer number,the multilayered DM3@CNF2 composite film exhibits a tensile strength of 48.14 MPa and a toughness of 5.28 MJ·m^(–3) with a thickness about 19μm.Interestingly that,the DM@CNF film with annealing treatment achieves significant improvement in conductivity(up to 17264 S·m^(–1))and EMI properties(SE of 41.90 dB and SSE/t of 10169 dB·cm^(2)·g–1),which still maintains relatively high mechanical properties.It is highlighted that the ultrathin multilayered DM@CNF film exhibits superior EMI shielding performance compared with most of the metal-based,carbon-based and MXene-based shielding materials reported in the literature.These results will offer an appealing strategy to develop the ultrathin and flexible MXene-based materials with excellent EMI shielding performance for the next generation intelligent protection devices.

Machine learning transforms the inference of the nuclear equation of state

Our knowledge of the properties of dense nuclear matter is usually obtained indirectly via nuclear experiments,astrophysical observations,and nuclear theory calculations.Advancing our understanding of the nuclear equation of state(EOS,which is one of the most important properties and of central interest in nuclear physics)has relied on various data produced from experiments and calculations.We review how machine learning is revolutionizing the way we extract EOS from these data,and summarize the challenges and opportunities that come with the use of machine learning.

Ultrasensitive solar-blind ultraviolet detection and optoelectronic neuromorphic computing using α-In_(2)Se_(3)phototransistors

Detection of solar-blind ultraviolet(SB-UV)light is important in applications like confidential communication,flame detection,and missile warning system.However,the existing SB-UV photodetectors still show low sensitivities.In this work,we demonstrate the extraordinary SB-UV detection performance of α-In_(2)Se_(3 )phototransistors.Benefiting from the coupled semiconductor and ferroelectricity property,the phototransistor has an ultraweak detectable power of 17.85 fW,an ultrahigh gain of 1.2×10^(6),a responsivity of 2.6×10^(5) A/W,a detectivity of 1.3×10^(16) Jones and an ultralow noise-equivalent-power of 4.2×10^(–20 )W/Hz1/2 for 275 nm light.Its performance exceeds most other UV detectors,even including commercial photomultiplier tubes and avalanche photodiodes.It can be also implemented as an optoelectronic synapse for neuromorphic computing.A 784×300×10 artificial neural network(ANN)based on this optoelectronic synapse is constructed and demonstrated with a high recognition accuracy and good noise-tolerance for the Fashion-MNIST dataset.These extraordinary features endow this phototransistor with the potential for constructing advanced SB-UV detectors and intelligent hardware.

Criticality-based quantum metrology in the presence of decoherence

Because quantum critical systems are very sensitive to the variation of parameters around the quantum phase transition(QPT),quantum criticality has been presented as an efficient resource for metrology.In this paper,we address the issue whether the divergent feature of the inverted variance is realizable in the presence of noise when approaching the QPT.Taking the quantum Rabi model(QRM)as an example,we obtain the analytical result for the inverted variance with single-photon relaxation.We show that the inverted variance may be convergent in time due to the noise.Since the precision of the metrology is very sensitive to the noise,as a remedy,we propose squeezing the initial state to improve the precision under decoherence.In addition,we also investigate the criticality-based metrology under the influence of the two-photon relaxation.Strikingly,although the maximum inverted variance still manifests a power-law dependence on the energy gap,the exponent is positive and depends on the dimensionless coupling strength.This observation implies that the criticality may not enhance but weaken the precision in the presence of two-photon relaxation,due to the non-linearity introduced by the twophoton relaxation.

Magnetic anisotropy,exchange coupling and Dzyaloshinskii–Moriya interaction of two-dimensional magnets

The two-dimensional(2D)magnets provide novel opportunities for understanding magnetism and investigating spin related phenomena in several atomic thickness.Multiple features of 2D magnets,such as critical temperatures,magnetoelectric/magneto-optic responses,and spin configurations,depend on the basic magnetic terms that describe various spins interactions and cooperatively determine the spin Hamiltonian of studied systems.In this review,we present a comprehensive survey of three types of basic terms,including magnetic anisotropy that is intimately related with longrange magnetic order,exchange coupling that normally dominates the spin interactions,and Dzyaloshinskii–Moriya interaction(DMI)that favors the noncollinear spin configurations,from the theoretical aspect.We introduce not only the physical features and origin of these crucial terms in 2D magnets but also many correlated phenomena,which may lead to the advance of 2D spintronics.