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The dielectric properties of phase-change materials, which could switch between crystalline and amorphous states, determine the device performances in the electronic and optical applications. Here, we have systematically investigated the dielectric properties of amorphous (a-) Ge8Sb2Te11 thin films with temperature by two complementary methods, i.e., the AC electrical measurement (0.5–186 Hz) and the impedance spectroscopy (9 kHz–3 GHz). The characterization of dielectric behavior of a-Ge8Sb2Te11 at low frequencies approaching DC, as defined by the static dielectric constants ([Formula: see text]), is achieved by the measurement at very low temperature. The resulting [Formula: see text] of a-Ge8Sb2Te11 is much larger than its optical dielectric constant [Formula: see text] at room temperature, which is mainly ascribed to the contribution of infrared active phonons at around tens of meV. The [Formula: see text] increases linearly by 16.5% from 5 to 300 K, due to more optical phonons activated at elevated temperatures. We also notice that [Formula: see text] shows little dependence on the frequency, and the total conductivity of a-Ge8Sb2Te11 extracted from impedance spectroscopies is frequency independent up to 10 MHz. Hence, no obvious dielectric relaxations are observed in a-Ge8Sb2Te11 below 10 MHz close to room temperature. In addition, the bandgap calculated from temperature dependence of the electrical conductivity is larger than that measured from the optical absorption edge by 11.5%.

Chalcogenide phase-change materials (PCMs), in particular, the flagship Ge2Sb2Te5 (GST), are leading candidates for advanced memory applications. Yet, GST in conventional devices suffer from high power consumption, because the RESET operation requires melting of the crystalline GST phase. Recently, we have developed a conductive-bridge scheme for low-power phase-change application utilizing a self-decomposed Ge-Sb-O (GSO) alloy. In this work, we present thorough structural and electrical characterizations of GSO thin films by tailoring the concentration of oxygen in the phase-separating GSO system. We elucidate a two-step process in the as-deposited amorphous film upon the introduction of oxygen: with increasing oxygen doping level, germanium oxides form first, followed by antimony oxides. To enable the conductive-bridge switching mode for femtojoule-level RESET energy, the oxygen content should be sufficiently low to keep the antimony-rich domains easily crystallized under external electrical stimulus. Our work serves as a useful example to exploit alloy decomposition that develops heterogeneous PCMs, minimizing the active switching volume for low-power electronics.

Different from conductive filament (CF)-type counterparts, interface-type devices exhibit continuously gradual conductance changes, making them the potential for artificial synapses. In this paper, Mn-doped BiFeO3 (BFMO) devices with SrRuO3 and TiN bottom electrodes demonstrate the clear CF rather than the interface barrier type resistance-switching feature due to the high Schottky barrier. Considering the measured electron affinity of 3.52 eV and work function of 4.22 eV in the as-synthesized BFMO film (a weak n-type semiconductor, marked as n−), we fabricated a hetero-junction device with the Nb-doped SrTiO3 (NSTO) bottom electrode (a strong n-type semiconductor, marked as n+) exhibiting analog switch characteristics. The n−–n+ hetero-junction between BFMO and NSTO reverses the operation polarity and leads to a barrier transition-dominated conductive behavior in the BFMO-based memristor. The device shows a large ON/OFF ratio over 1200, favorable stability after 104 s, continual multi-value characteristics, symmetrical long-term potentiation and depression, and synaptic plasticity with about 80 ns time constant. The investigation of resistive switching features, band structure, and synapse performance in this work provides a reference for the application of BiFeO3 in the field of the memristor.

Phase change memory (PCM) is considered as a leading candidate for next generation data storage as well as emerging computing device, but the advancement has been hampered by high switching energy due to the melting process and amorphous relaxation induced large resistance drift. Polymorphic crystal-crystal transition without amorphization in metal dichalcogenides (TMDs) could be employed to solve these issues. Yet, the mechanism is still controversy. A melting-free PCM made of two dimensional (2D) MoTe2, which exhibits unipolar resistive switching (RS) and multi-level states with substantially reduced resistance drift via joule heating, is reported in this work. The device is first prepared based on the temperature dependence of Raman spectrum and electrical transport investigations on MoTe2 films. Significantly improved device performances on energy efficiency, switching speed, and memory window are further achieved by electrode size scaling down, indicating the key role of localized heating. Then, device scale transmission electron microscopy images reveal that the resistive switching stems from the transition between semiconducting 2H phase and metallic 1T′ phase. An entropy induced Te vacancies model is proposed to explain the reversible phase change mechanism in the MoTe2 based device. This study paves the way for further development of PCM based on atomically thin 2D TMDs, aiming for high density storage-class memory and high-precision neuromorphic computing.

The endurance of ovonic threshold switching (OTS) selectors is a key element for memory application. However, multi-element system for OTS in recent studies will induce element or phase segregation and lead to device failure. Since pure Te based device characterizes relatively high off current, in this work, we studied a Te-rich Ge–Te based OTS selector. We first conducted a failure analysis on Ge–Te based OTS selector. Through first-principles calculations, we found that a relatively larger Ge concentration in the Ge–Te system may lead to a worse device endurance after continuous operation due to the migration of Ge atoms. Experiments further proved that device endurance can be improved more than two orders of magnitude through decreasing Ge concentration and the element segregation is greatly weakened by the composition close to elemental. Finally, a significantly improved endurance of 2 × 1010 was realized in Ge10Te90 based OTS selectors. In the meantime, the Ge10Te90 based OTS selectors show good switching performance and potential for use in memory applications.

The memristor study now generally exhibit threshold voltage characteristics. The memristance changes when the voltage across the memristor is greater than the threshold voltage. Otherwise, the memristance is almost constant. Based on this feature, we design a general-purpose memristor programmable circuit that is simple to operate. In the field of communication and signal processing, programmable analog filters are required, and memristors with multi-valued characteristic are suitable as programmable impedance elements for such circuits. Through simulation and physics experiments, we demonstrate a memristor-based programmable low-pass filter using the designed programmable circuit to realize the dynamic adjustment of circuit parameters.

The Kohn-Sham gaps of density functional theory (DFT) obtained in terms of local density approximation (LDA) or generalized gradient approximation (GGA) cannot be directly linked to the fundamental gaps of semiconductors, but in engineering there is a strong demand to match them through certain rectification methods. Shell DFT-1/2 (shDFT-1/2), as a variant of DFT-1/2, is a potential candidate to yield much improved band gaps for covalent semiconductors, but its accuracy depends on the LDA/GGA ground state, including optimized lattice parameters, basic Kohn-Sham gap before self-energy correction and the amount of self-energy correction that is specific to the exchange-correlation (XC) functional. In this work, we test the LDA/GGA as well as shDFT-1/2 results of six technically important covalent semiconductors Si, Ge, GaN, GaP, GaAs and GaSb, with an additional ionic insulator LiF for comparison. The impact of XC flavor (LDA, PBEsol, PBE and RPBE), either directly on the gap value, or indirectly through the optimized lattice constant, is examined comprehensively. Moreover, we test the impact of XC flavor on LDA/GGA and shDFT-1/2 gaps under the condition of fixed experimental lattice constants. In-depth analysis reveals the rule of reaching the best accuracy in calculating the electronic band structures of typical covalent semiconductors. Relevant parameters like lattice constant, self-consistency in shDFT-1/2 runs, as well as the exchange enhancement factor of GGA, are discussed in details., Comment: 23 pages, 10 figures

Memory and logic in the same device Future artificial intelligence applications and data-intensive computations require the development of neuromorphic systems beyond traditional heterogeneous device architectures. Physical separation between a peripheral signal-processing unit and a memory-operating unit is one of the main bottlenecks of heterogeneous architectures, blocking further improvements in efficient resistance matching, energy consumption, and integration compatibility. Tong et al . present a transistor-memory architecture based on a homogeneous tungsten selenide-on-lithium niobate device array (see the Perspective by Rao and Tao). Analog peripheral signal preprocessing and nonvolatile memory were possible within the same device structure, promising diverse neuromorphic functionalities and offering potential improvements in neuromorphic systems on-chip. —YS

Monolayer Ge2P4S2 and Ge2As4S2 are proposed as a new type of efficient photocatalyst for water splitting, based on first-principles calculations. Monolayer Ge2As4S2 exhibits a direct band gap of 1.89 eV (based on HSE06 calculation), while monolayer Ge2P4S2 is an indirect gap semiconductor that can turn into direct band gap by applying 3% compressive strain. Moreover, the band edge positions of monolayer Ge2P4S2 and Ge2As4S2 perfectly cover the redox potentials of water. Remarkably, the Ge2P4S2 and Ge2As4S2 monolayers possess rather high carrier mobilities (∼103–104 cm2 V−1 s−1), and have moderate optical absorption performance in the range of visible light. In addition, the adsorption and decomposition of water molecules on monolayer Ge2P4S2 and Ge2As4S2 are explored to illustrate the mechanism of photocatalytic hydrogen formation. These results demonstrate that the monolayer Ge2P4S2 and Ge2As4S2 hold great potential for photocatalytic water splitting.

Searching for novel two-dimensional (2D) materials is highly desired in the field of nanoelectronics. We here predict a new 2D crystal barium triarsenide (BaAs3) with a series of encouraging functionalities within density functional theory. Being kinetically and thermally stable, the monolayer and bilayer forms of BaAs3 possess narrow indirect band gaps of 0.74 eV and 0.34 eV, respectively, with high hole mobilities on the order of ~ 103 cm2 V−1 s−1. The electronic properties of 2D BaAs3 can be manipulated by controlling the layer thickness. The favorable cleavage energy reveals that layered BaAs3 can be produced as a freestanding 2D material. Furthermore, by introducing vacancy defects monolayer BaAs3 can be transformed from a semiconductor to a metal. Two-dimensional BaAs3 may find promising applications in nanoelectronic devices, such as memristors.

Atomically thin hexagonal boron nitride (h-BN) is often regarded as an elastic film that is impermeable to gases. The high stabilities in thermal and chemical properties allow h-BN to serve as a gas barrier under extreme conditions.In this work, we demonstrate the isolation of hydrogen in bubbles of h-BN via plasma treatment.Detailed characterizations reveal that the substrates do not show chemical change after treatment. The bubbles are found to withstand thermal treatment in air,even at 800 degree celsius. Scanning transmission electron microscopy investigation shows that the h-BN multilayer has a unique aligned porous stacking nature, which is essential for the character of being transparent to atomic hydrogen but impermeable to hydrogen molecules. We successfully demonstrated the extraction of hydrogen gases from gaseous compounds or mixtures containing hydrogen element. The successful production of hydrogen bubbles on h-BN flakes has potential for further application in nano/micro-electromechanical systems and hydrogen storage., 55 pages, 33figures

In unsupervised learning (UL)-oriented spiking neural network (SNN) designs, the initial conductance variation of the synapses is usually necessary, with which temporally differentiated responses of the output neurons can be achieved given the same input patterns, in order to implement the important lateral inhibition function. We take a lithium silicate memristor as an example and demonstrate through a motion-style recognition task that, without regulating the initial conductance variation of memristor synapses, several types of errors will occur: too large conductance variance leads to the deactivation of some output neurons and the incomplete learning of the motion routes, while too small variance causes duplicated learnings of the same pattern. By tuning the memristor conductance variance through the forming voltages, it is found that the UL capacity can be optimized, indicating a promising approach to enhance the UL performance of a memristor-based SNN.

By exploiting novel transport phenomena such as ion selectivity at the nanoscale, it has been shown that nanochannel systems can exhibit electrically controllable conductance, suggesting their potential use in neuromorphic devices. However, several critical features of biological synapses, particularly their conductance modulation, which is both memorable and gradual, have rarely been reported in these types of systems due to the fast flow property of typical inorganic electrolytes. In this work, we demonstrate that electrically manipulating the nanochannel conductance can result in nonvolatile conductance tuning capable of mimicking the analog behavior of synapses by introducing a room-temperature ionic liquid (IL) and a KCl solution into the two ends of a nanochannel system. The gradual conductance-tuning mechanism is identified through fluorescence measurements as the voltage-induced movement of the interface between the immiscible IL and KCl solution, while the successful memorization of the conductance tuning is ascribed to the large viscosity of the IL. We applied a nanochannel-based synapse to a handwritten digit-recognition task, reaching an accuracy of 94%. These promising results provide important guidance for the future design of nanochannel-based neuromorphic devices and the manipulation of nanochannel transport for computing.

Layered transition-metal trichalcogenides have become one of the research frontiers as two-dimensional magnets and candidate materials used for phase-change memory devices. Herein we report the high-pressure synchrotron X-ray diffraction and resistivity measurements on Cr2Ge2Te6 (CGT) single crystal by using diamond anvil cell techniques, which reveal a mixture of crystalline-to-crystalline and crystalline-to-amorphous transitions taking place concurrently at 18.3-29.2 GPa. The polymorphic transition could be interpreted by atomic layer reconstruction and the amorphization could be understood in connection with randomly flipping atoms into van der Waals gaps. The amorphous (AM) phase is quenchable to ambient conditions. The electrical resistance of CGT shows a bouncing point at ~ 18 GPa, consistent with the polymorphism phase transition. Interestingly, the high-pressure AM phase exhibits metallic resistance with the magnitude comparable to that of high-pressure crystalline phases, whereas the resistance of the AM phase at ambient pressure fails to exceed that of the crystalline phase, indicating that the AM phase of CGT appeared under high pressure is quite unique and similar behavior has never been observed in other phase-change materials. The results definitely would have significant implications for the design of new functional materials., 14 pages,7 figures, 2 tables, 1 supporting information. JPCC in press

The progress in the neuromorphic computing hinges on the development of nanoscale analog artificial synapses. Here, we report a LiSiOX (LSO)-based memristive synapse with 100-level conductance states under identical pulses, representing synaptic potentiation and depression behaviors. The superior analog behaviors originate from the dynamic evolution of an electro-thermal modulation region with the motion of lithium and oxygen ions. A three-layer perceptron was constructed in simulation with LSO synapses, and a 91.97% recognition accuracy was achieved for handwritten digits. Moreover, the influences of several critical parameters, including device variability and weight precision, on the accuracy have been investigated. This letter provides guidelines for the optimization of synaptic device in robust memristive neural network.

Ferroelectric materials with a perovskite structure have various shortcomings, including poor Si compatibility, large physical thickness, and small bandgap. HfO2-based ferroelectric materials provide a new solution for ferroelectric semiconductor devices. HfO2-ZrO2 solid solution (HZO) thin films have been widely studied due to their low crystallization temperature and wide stoichiometric range. Considering its low cost and flexible deposition conditions, the sputtering technique is a useful method for HZO deposition. However, sputtered HZO ferroelectric films have been rarely reported, and the composition effect on the ferroelectric properties of HfXZr1−XO2 thin films is still unclear. In this letter, sputtered HfXZr1−XO2 thin films were studied with different Zr contents (from 12.49 to 20.34 mol%) by varying the sputtering power ratio of ZrO2 to HfO2. We found that the dopant concentration with the best ferroelectric properties in the HfXZr1−XO2 system is ~15–16 mol%, which is much lower than that in the ALD-based HZO film. First-principle calculation shows that oxygen vacancies doped in the HZO film lead to a change in the Helmholtz free energy of different phases, which affected the optimal Zr concentration for the largest ferroelectric polarization.

Using first principle calculations we have investigated the structure stabilities, electronic and optical properties of single layer planar penta-X2N4 (X = Ni, Pd and Pt). According to the calculated phonon dispersion relation and elastic constants, as well as ab initio molecular dynamics simulation results, monolayers of planar penta-X2N4 are dynamically, mechanically, and thermally stable. In addition, these monolayers are direct-gap semiconductors with bandgaps ranging from 0.92 eV to 1.11 eV, which can be further tuned by external strains. Besides, the bonding characteristics and the optical properties in these monolayers are investigated based on HSE06 calculations, where strong in-plane optical absorption with wide spectral range has been revealed. Our results indicate that planar penta-X2N4 monolayers possess excellent electrical and optical properties, and may find potential applications in solar cells and nanoelectronics.

Searching for single-atom thin materials in the planar structure, like graphene and borophene, is one of the most attractive themes in two-dimensional materials. Using density functional theory calculations, we have proposed a series of single-layer planar penta-transition metal phosphides and arsenides, i.e., TM2X4 (TM = Ni, Pd and Pt; X = P, As). According to the calculated phonon dispersion relation and elastic constants, as well as ab initio molecular dynamics simulation results, monolayers of planar penta-TM2X4 are dynamically, mechanically and thermally stable. In addition, screened HSE06 hybrid functional calculations including spin–orbit coupling show that these monolayers are direct band gap semiconductors, with band gaps ranging from 0.14 to 0.77 eV. Ultrahigh carrier mobilities up to 104–105 cm2 V−1 s−1 both for electrons and holes have been confirmed, among the highest in 2D semiconductors. Our results indicate that planar penta-TM2X4 monolayers are interesting narrow-gap semiconductors with ultrahigh carrier mobility as well as excellent optical properties, and may find potential applications in nanoelectronics and photoelectronics.

Herein, the tape-casting technique was used to fabricate an Er-doped (K0.5Na0.5)NbO3 (KNN:0.02Er) laminar ceramic, which possesses a pseudo-cubic phase structure with regular cube-shaped grains. Considering the substitution behavior of the Er dopant in the KNN lattice, X-ray diffraction analysis and density functional theory simulations both verify that Er can replace K and Na sites. And Er-induced enlargement of the energy band gap of KNN:0.02Er has been obtained in the reflectance spectra and simulations. Furthermore, the ceramic exhibits dual-mode down-conversion and up-conversion photoluminescence (PL). As a lead-free luminescent ferroelectric, the PL intensity of KNN:0.02Er can be enhanced by polarization induced by electric poling. And the dual-mode PL can be obviously modulated under an electric field in an in situ, reversible, real-time and dynamical way. Our results offer an effective theoretical way to bridge the relationship between the crystal structure/chemical bonding environment and the performance of the KNN system, and also provide an opportunity to realize electrically controlled tuning of the PL response in KNN-based luminescent ferroelectrics and the corresponding optoelectronic devices.

By simply doping with a certain amount of Sm3+, opaque lead-free (K0.5Na0.5)NbO3 piezoelectric ceramics were fabricated into transparent/translucent ceramics. The photochromic (PC) behavior and associated reversible transmittance/luminescence modulation were realized via physical means. Upon illumination under a xenon lamp, the maximal decreased values of optical transmittance and luminescence intensity are 36.1% and 49%, respectively. Meanwhile, the colors of all the ceramics turn darker after illumination, and subsequently return to their initial states by thermal stimulus (200 °C for 5 minutes), exhibiting a typical PC phenomenon. Combining experimental and calculation results, it can be found that the PC mechanism is closely related to the vacancy defects induced by both high-temperature sintering and Sm3+-doping. Additionally, the Sm3+ effects on phase structures, microstructures, optical transmittance and photoluminescence properties of the ceramics were systematically studied. The substitution behavior of Sm3+ and vacancy defect concentrations on the energy band gap of the material were unraveled by the first principles calculations to further clarify the corresponding mechanism. The Sm3+-doped (K0.5Na0.5)NbO3 transparent ceramics have potential for application in modulation and conversion of photoenergy, such as in optical memory and photo-switching devices. And the reversible tuning of transmittance in our material is especially attractive for exploring novel transparent PC materials.

Phase change memory is an emerging nonvolatile memory technology, recently becoming the center of attention to bridge the speed gap between fast dynamic random access memory and slow flash-based solid-state drives. Lately, CrGeTe3 has been investigated as a special phase change material with an inverse resistance and density change. This material has excellent properties such as good thermal stability, ultralow-energy glass formation process and almost zero mass-density change upon crystallization. Here, we analyzed the amorphous structure of this abnormal material in detail through ab initio simulations and discovered that the metallic-like tight atomic packing is the origin of the high carrier concentration and high mass density in the amorphous phase. Furthermore, the bonding analysis confirms that it is the short Cr–Cr bonds that lead to high packing efficiency in the amorphous local order. Our results discovered the material gene of the amorphous CrGeTe3, paving the way for the design of high-performance memory devices based on this material.

Analog resistive switching behavior in emerging nonvolatile memories has facilitated a wide range of potential applications in neuromorphic computing and cognitive tasks. However, the intrinsic nonlinearity (NL) of conductance update has been proven to be highly unfavorable for the implementation of analog synapse-based hardware neural network (HNN). In this brief, we show that the conductance update characteristics of our Pt/TaO x /Ta memristor will be significantly improved by carefully designing the potentiation and depression pulse shapes. Furthermore, measured conductance update characteristics with different degrees ofNL are applied in the simulation of a face classification task. The results show that both the recognition accuracy and the learning speed of this supervised learning scenario are substantially improved by the proposed optimization approach.

The thermal conductivities of GeTe/Bi2Te3 superlattice-like materials are calculated based on density functional perturbation theory (DFPT) and measured using a 3ω method. The calculated results show that the lattice thermal conductivity or thermal diffusivity of GeTe/Bi2Te3 superlattice-like materials significantly decrease due to the effects of interfaces and Bi atoms in Bi2Te3. Our measured results are in line with the theoretical calculations, and reach an extremely low thermal conductivity at 0.162 W mK−1 compared with published work on Ge–Sb(Bi)–Te, indicating the effectiveness of modulating the thermal properties of phase change materials by using Bi-based GeTe/Bi2Te3 superlattice-like materials. Our findings give a calculation method to modify the thermal characteristics of superlattice-like materials and confirm Bi-based GeTe/Bi2Te3 superlattice-like materials as promising candidates for phase change materials with lower thermal conductivity.

High-quality inorganic red-emitting phosphors are widely used in white light-emitting diodes to realize excellent white light emission. Tungstate is one of the most popular materials for phosphors due to their great chemical stability and unique luminescent properties. However, the quantum efficiency of tungstate-based phosphor is still not high enough for commercial application. In this study, we synthesized a series of α-La2W2O9: Eu3+ phosphors by a modified high-temperature solid-state reaction method. The prepared phosphors crystallized well in triclinic structure. Eu3+ ion mainly substituted La3+ ion in La (1) site of low local symmetry as C1, thus the phosphors showed a strong red emission at 618 nm by 5D0→7F2 transition. When Eu3+ doping concentration is 3%, α-La2W2O9: 3%Eu3+ presents maximum luminescent intensity, longest lifetime (0.651 ms) and highest quantum yield (77%). Furthermore, the mechanism of concentration quenching is discussed and indicates that electric dipole-dipole interaction is the dominant mechanism. Our study demonstrates a high-quality red-emitting phosphor based on α-La2W2O9: 3%Eu3+, which is potential for commercial white light-emitting diodes applications.

We studied the pseudo-homeothermic synaptic behaviors by integrating complimentary metal–oxide–semiconductor-compatible materials (hafnium oxide, aluminum oxide, and silicon substrate). A wide range of temperatures, from 25 °C up to 145 °C, in neuronal dynamics was achieved owing to the homeothermic properties and the possibility of spike-induced synaptic behaviors was demonstrated, both presenting critical milestones for the use of emerging memristor-type neuromorphic computing systems in the near future. Biological synaptic behaviors, such as long-term potentiation, long-term depression, and spike-timing-dependent plasticity, are developed systematically, and comprehensive neural network analysis is used for temperature changes and to conform spike-induced neuronal dynamics, providing a new research regime of neurocomputing for potentially harsh environments to overcome the self-heating issue in neuromorphic chips.

It is known that the Kohn–Sham eigenvalues do not characterize experimental excitation energies directly, and the band gap of a semiconductor is typically underestimated by local density approximation (LDA) of density functional theory (DFT). An embarrassing situation is that one usually uses LDA+U for strongly correlated materials with rectified band gaps, but for non-strongly-correlated semiconductors one has to resort to expensive methods like hybrid functionals or GW. In spite of the state-of-the-art meta-generalized gradient approximation functionals like TB-mBJ and SCAN, methods with LDA-level complexity to rectify the semiconductor band gaps are in high demand. DFT-1/2 stands as a feasible approach and has been more widely used in recent years. In this work we give a detailed derivation of the Slater half occupation technique, and review the assumptions made by DFT-1/2 in semiconductor band structure calculations. In particular, the self-energy potential approach is verified through mathematical derivations. The aims, features and principles of shell DFT-1/2 for covalent semiconductors are also accounted for in great detail. Other developments of DFT-1/2 including conduction band correction, DFT+A-1/2, empirical formula for the self-energy potential cutoff radius, etc, are further reviewed. The relations of DFT-1/2 to hybrid functional, sX-LDA, GW, self-interaction correction, scissor’s operator as well as DFT+U are explained. Applications, issues and limitations of DFT-1/2 are comprehensively included in this review.

In this paper, we look at how artificial neural networks (ANNs) may be used to improve compact model extraction of statistical variability in 5 nm nanosheet transistors (NSTs) and how it can be applied to 6NST-static random access memory (SRAM) simulations. To begin, both the TCAD simulation platform and compact model of 3D n-type and p-type NST have been rigorously validated against the experimental data. The transfer characteristics curves of 1104 NST samples generated by metal gate granularity, random discrete dopants and line edge roughness are used to extract the important figures of merit (FoM) including ON-current (I ON), OFF-current (I OFF), threshold voltage (V TH) and subthreshold slope. Meanwhile, we can collect the main compact model parameters of these NST samples using our automatic extraction technique. Furthermore, a multi-layer ANN engine is trained to anticipate the important compact model parameters by entering FoMs, which significantly speeds up the automatic extraction. When we compare the prediction results to the genuine values, we discover that their correlation coefficients are all larger than 0.99. Finally, we simulated the 6NST-SRAM circuit and obtained its stability variation, with the help of extracted NST variability by the aforementioned speedup techniques.

Ion transport and hydrodynamic flow through nanometer-sized channels (nanopores) have been increasingly studied owing to not only the fundamental interest in the abundance of novel phenomena that has been observed but also their promising application in innovative nanodevices, including next-generation sequencers, nanopower generators, and memristive synapses. We first review various kinds of materials and the associated state-of-the-art processes developed for fabricating nanoscale pores, including the emerging structures of DNA origami and 2-dimensional nanopores. Then, the unique transport phenomena are examined wherein the surface properties of wall materials play predominant roles in inducing intriguing characteristics, such as ion selectivity and reverse electrodialysis. Finally, we highlight recent progress in the potential application of nanopores, ranging from their use in biosensors to nanopore-based artificial synapses. The state of the art in nanopore sensors and devices is reviewed focusing the progress toward their applications beyond the single-molecule sequencing.

Subject to the strong quantum confinement, the impact of channel geometry structure variation on the electrostatic performance parameters of sub-5nm nanosheet transistors (NSTs) is comprehensively studied by a 2D Poisson-Schrodinger (PS) quantum simulation platform, which is cautiously calibrated against the experiment data. New physical effects in NSTs including quantum confinement variation, round corner and thin channel are studied and analyzed. It is found that the electrostatic driven performance drops sharply for thin NSTs boosted by quantum confinement variation.

Hamming Distance (HD) plays an important role in many fields such as information theory, coding and cryptography. In this work, an efficient in-memory HD calculation method in a one-transistor-two-memristor (1 T2M) array has been designed. HD calculation is based on the implementation of XOR logic, which only costs three steps and the results could be stored in-situ. HD value is measured by a Read current, which is obtained by applying the Read voltage on top electrodes of all memristors. The influence of LRS device-to-device variation on the calculation results is also discussed.

Linear matrix equations have been one of the main topics in matrix theory and its applications. In this work, a self-adaptive matrix equation solving method in analog non-volatile memory (NVM) arrays has been demonstrated. The solving method utilizes the gradient descent algorithm of the machine learning and can be illustrated by the linear neural network. Relying on the multiplication and accumulation operation of the NVM array, the self-adaptive method provides an efficient and reliable solution of the matrix equation. The influence of the device conductance states and write noise on the solution accuracy is also discussed.

Rather than stiffly giving out spikes upon reaching a fixed threshold voltage, the biological neurons show quite dynamic excitation behaviors namely stochastic firing which is regarded to play a critical role in probabilistic computing. In this work we realize electronic stochastic neuron by developing and examining through three major kinds of threshold switching memristors (TSMs). The novel CuS/GeSe conducting-bridge threshold switching (CBTS) memristor is identified as the best candidate owing to the most desired stochastic switching behavior as well as the similarity with biological ion channels.

Various types of memristor-based network circuits have been reported to imitate multifunctional associative learning, while at the price of complex control circuits. In this work, we construct a compact memristive network for associative learning, in which memristors serve as interconnected synapses to extract relevance and timing information. A spice-compatible dynamic model of memristor is established and all the learning functions of Pavlov's dog are successfully demonstrated through circuit simulation. By incorporating the actual characteristics of the memristor into the network, multiple functions such as associative learning, acquisition, backward conditioning, and extinction are completely implemented. Moreover, the spike timing coding scheme and learning ability versus signal interval (Δt) are in line with biological behavior, making this design a promising solution for future brain-inspired associative learning systems.

In this paper, the microscopic mechanisms of the transition metal oxides/RuO 2 memristor showing great performance in the recent advances were presented using ab initio theoretical methods. The calculations of oxygen vacancy formation, the Schottky barrier height, and the migration barriers for the HfO 2 /RuO 2 interface model suggest that there is mediated oxygen balance at the interface which contributes to the improvement of the device.

Sb-Based phase-change materials have exhibited tremendous advantages in both data storage and reconfigurable photonic devices. Despite the intensive studies on their structures and properties in the crystalline state, the widely used amorphous phase remains elusive. Here, we investigate amorphous Sb2Te3, Sb2Se3, and Sb2S3 through ab initio calculations to link their unique properties to the local structure and bonding nature. We discover that Sb forms shorter and stronger bonds with Se and S than Te, and the average bonding angles of Se (92.0°) and S (94.1°) show larger distortion than that of Te (91.5°). This leads to larger Peierls-like distortion in Sb2Se3 and Sb2S3. On the other hand, more charge transfer and void fraction are presented, opening band gaps and leading to different electronic and optical properties. In contrast, Sb2Te3, due to its semiconducting behavior and low thermal stability, enables its application in phase-change memory. Our results reveal the physics of vastly different electronic and optical properties induced by S, Se, and Te alloying, providing an effective strategy for materials design.

Memristor-based in-memory computing could address the inherent von Neumann bottleneck constraints in traditional computing architecture. Apart from brain-inspired neuromorphic computing, memristive logic is another promising road to realize the true fusion of memory and computing, beyond CMOS transistor-based logic. Fundamental Boolean logic functions have been demonstrated by utilizing the resistive switching behaviors in binary memristors or memristive devices. The logic results can be stored in situ as the resistance states of the device; in other words, the nonvolatility of logic computing is a natural feature of this novel logic method, which may help rebuild the computing paradigm from the bottom-up. In this chapter, we will first introduce the ideology evolution from near-memory computing to in-memory computing and explain why the memristor is the component chosen to serve as the building block. Then we will review the basic operation principles of nonvolatile memristive R-R and V-R logic methodologies, including IMP-based stateful logic, sequential logic, MAGIC logic, neural network logic, etc. Their pros and cons are summarized to clarify their existing limitations. Finally, the opportunities and challenges that will have to be met will also be discussed, both at the device and system levels.

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.

Memristive convolutional neural network accelerator has attracted intensive interest in reducing time and energy consumption. In this article, we analysis the viability of the quantization method using LeNet-5 model on MNIST dataset. Low bit-precision quantization is achieved with slight accuracy loss. The fabricated bilayer AlO x memristor with 3-bit states is used to emulate the synapse, and an accuracy of 97.93% is accomplished within the device nonideality. Furthermore, the device requirements of variation and reliability on inference application are evaluated and proposed.

In this paper, the cause of inverse drain induced barrier lowering effect in negative capacitance field effect transistors (NCFETs) is discussed, and a capacitance model is given. Based on inverse DIBL effect, negative differential resistance phenomenon could occur, and these two effects are hard to be removed. Since higher on-off ratio and lower NDR cannot be achieved at the same condition, their relationship with device structure is also studied.

By exploiting the unique transport properties in nanoscale, the nanochannel-based memristor exhibits analog and stable resistive switching. To have a complete understanding of the physical picture behind the nanochannel-based memristor, we carry out an electrokinetic analysis of the frequency and other memristive characteristics extracted from the measured data in our device. Our theoretical analysis opens up an avenue for broad adoption of more nanochannel-based structures and novel functionalities in neuromorphic computation.

Ge-Sb-Te (GST)-based phase-change memory (PCM) excels in the switching performance but remains insufficient of the operating speed to replace cache memory (the fastest memory in a computer). In this work, a novel approach using Sb

Chalcogenide phase change materials are currently under intensive academic and industrial investigations, since it shows nonvolatile reversible switching between amorphous and crystalline states under electrical or optical pulses. In this chapter, we will briefly review the history and basic properties of the chalcogenides. The underlying atomic models that allow precise controlling of the conductance or transmittance states of phase change materials will be described. Then we will introduce the applications covering mature optical disk technology and emerging all-photonic nonvolatile memory. At last, the utilization of chalcogenides in neuromorphic computing, including electronic artificial neural network and on-chip photonic neural network, will be illustrated, highlighting the energy-efficient nature of in-memory computing. These results open the way for disruptive applications in scalable non-von Neumann computing paradigm.

Supplemental Document 1