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Memristor, with the ability of analog computing, is widely investigated for improving the computing efficiency of deep neural networks (DNNs) deployment. However, how to fully take advantage of the analog computing ability of memristive computing system (MCS) for DNN deployment is still an open question. Here, a new neural network models deployment scheme, that is, an information dimension matching (IDM) scheme, is proposed to fully take advantage of the analog computing ability of MCS. Furthermore, the spatial and temporal DNN, that is convolutional neural network (CNN) and recurrent neural network (RNN) is used to verify the proposed deployment scheme, respectively. The experimental results indicate that, compared to the traditional deployment schemes, the proposed deployment scheme shows obvious inference accuracy and energy efficiency improvement (>4 × in four‐layer DNNs deployment), and the energy efficiency improvement increases dramatically with the layers increment of DNNs. This work paves the path for developing high computing efficiency analog MCS. [ABSTRACT FROM AUTHOR]

Two-dimensional (2D) sliding ferroelectric materials possess intriguing physical and electronic properties, thereby greatly expanding the family of 2D ferroelectrics (FEs). In this work, using first-principles calculations, we demonstrate a reversible antiferroelectricity–ferroelectricity–antiferroelectricity (AFE–FE–AFE) transition in bilayer δ-SiX (X = S/Se) along the in-plane direction during interlayer sliding. This transition primarily stems from the mechanical sliding of the top layer. Notably, spontaneous polarization (Ps) can reach up to approximately 80 pC m−1 and 70 pC m−1 for bilayers SiS and SiSe, respectively. Furthermore, the mechanism underlying this phase transition involves the interlayer between interaction energy (Einter) and strain energy (Eε). In the case of bilayer SiS, along the ABAFE–AAFE–ABAFE path, critical points arise from the cooperation of strain energy and mechanical sliding force. During the ACAFE–ADFE sliding process, phase transition relies on the combined effect of strain energy and mechanical sliding force. At the ADFE–ACAFE point, the transition is primarily driven by the combined action of interlayer interaction energy and mechanical sliding force. This theoretical work not only establishes a feasible approach for achieving a reversible AFE–FE–AFE phase transition, but also offers valuable insights for the design of novel volatile devices. [ABSTRACT FROM AUTHOR]

In two-dimensional valleytronics, the controllability of anomalous valley Hall effect is the key to its practical application. However, most of the previously proposed control methods are volatile or irreversible. Here, using first principles calculations, we demonstrate that the GdF2/Sc2CO2 multiferroic heterojunction can exhibit nonvolatile switching of its electronic band structures. Interestingly, the polarization switching of the ferroelectric monolayer induces movement in the valley polarization band of GdF2, enabling manipulation of the valley Hall effect. This allows the memory state encoded in the ferroelectric monolayer to be read out via the anomalous valley Hall signal of the heterostructures. The switchable anomalous valley Hall effect can also be achieved by applying biaxial strain in the GdF2/Sc2CO2 multiferroic heterojunction. By harnessing this intriguing anomalous valley Hall effect switching characteristic, we develop a nonvolatile valleytronics memory device. This work provides a way to achieve nonvolatile control in valleytronics and promotes the design of memory devices. [ABSTRACT FROM AUTHOR]

In this article, we analyzed the experimental data based on the TaOx memristor and found that the threshold switching (TS) characteristics are related to temperature, and its logarithmic I–V curve is in good agreement with the space charge limiting current conduction mechanism. We use this mechanism to establish a TS physical model and then use the physical model to build an LTspice model. The model data are fitted with the experimental data, which is basically consistent. Next, using the TS memristor to simulate a leaky integrate-and-fire neuron circuit, the basic dynamics are realized. By changing the external temperature of the memristor, the output frequency of the neuron will be more intense as the temperature increases. Finally, an artificial spiking neural network (SNN) was built based on this neuron circuit for MNIST recognition task. In this SNN, the input signals fused both voltage amplitude and temperature to achieve neuromorphic multimodal preprocessing and enhance the recognition accuracy. These results demonstrated the reliability of the model, which enhanced the flexibility for exploring the application of TaOx-based TS memristors. [ABSTRACT FROM AUTHOR]

Nowadays, two-dimensional (2D) materials with Janus structures evoke much attention due to their unique mechanical and electronic properties. In this work, Janus Pma2-Si2XY (X/Y = S/Se/Te, X ≠ Y) ferroelectric monolayers are firstly proposed and systematically investigated by first-principles calculations. These monolayers exhibit remarkable mechanical properties, including small Young's modulus values, negative Poisson's ratios (NPRs) and large critical strains, reflecting their exceptional flexibility and stretchability. More strikingly, the novel structures of Si2STe and Si2SeTe also endow them with in-plane spontaneous polarization (Ps) and low energy barrier for phase transition, with Ps and energy barrier values being 1.632 × 10−10 C m−1 and 159 meV for Si2STe and 1.149 × 10−10 C m−1 and 196.6 meV for Si2SeTe. The ab initio molecular dynamics (AIMD) simulations reveal high Curie temperatures (Tc) for Si2STe and Si2SeTe, ranging between 1300 K and 1400 K. Additionally, Si2XY monolayers exhibit high anisotropic carrier mobility (∼103 cm2 V−1 s−1) and an extraordinary light absorption coefficient (∼105 cm−1). Our research not only broadens the family of 2D Janus ferroelectric materials, but also demonstrates their potential applications in nanomechanical, nanoelectronic and optoelectronic devices. [ABSTRACT FROM AUTHOR]

Oxide-based memristors by incorporating thermally enhanced layer (TEL) have showed great potential in electronic devices for high-efficient and high-density neuromorphic computing owing to the improvement of multilevel resistive switching. However, research on the mechanism of resistive switching regulation is still lacking. In this work, based on the method of finite element numerical simulation analysis, a bilayer oxide-based memristor Pt/HfO2 (5 nm)/Ta2O5 (5 nm)/Pt with the Ta2O5 TEL was proposed. The oxygen vacancy concentrates distribution shows that the fracture of conductive filaments (CF) is at the interface where the local temperature is the highest during the reset process. The multilevel resistive switching properties were also obtained by applying different stop voltages. The fracture gap of CF can be enlarged with the increase of the stopping voltage, which is attributed to the heat-gathering ability of the TEL. Moreover, it was found that the fracture position of oxygen CF is dependent on the thickness of TEL, which exhibits a modulation of device RS performance. These results provide a theoretical guidance on the suitability of memristor devices for use in high-density memory and brain-actuated computer systems. [ABSTRACT FROM AUTHOR]

Two-dimensional (2D) van der Waals (vdW) heterostructures with spontaneous intrinsic ferroelectrics play an essential role in ferroelectric memories. Also, the reversal of polarized directions induces band alignment transitions among different types to provide a new path for multifunctional devices. In this work, the structural and electronic properties of 2D VS2/Ga2O3 vdW heterostructures under different polarizations were investigated using first-principles calculations with the vdW correction of the DFT-D2 method. The results reveal that the polarized direction of a 2D Ga2O3 monolayer can cause a distinct band structure reversion from a metal to a semiconductor due to the shift of band alignment induced by the interlayer charge transfer. Moreover, the VS2/P↑ Ga2O3 heterostructures retain type-I and type-II band alignments in the majority and minority channel, respectively, under an external electric field. Interestingly, applying the external electric field for VS2/P↓ Ga2O3 heterostructures can lead to a transition from type-II to type-I in the majority channel, and from type-II to type-III in the minority channel. Our work provides a feasible way to realize 2D VS2/Ga2O3 vdW heterostructures for potential applications in ferroelectric memories and electrostatic gating dependent multiple band alignment devices. [ABSTRACT FROM AUTHOR]

A memristor‐based reinforcement learning (RL) system has shown outstanding performance in achieving efficient autonomous decision‐making and edge computing. Sarsa (λ) is a classical multistep RL algorithm that records state with λ decay and guides policy updates, significantly improving the algorithm convergence speed. However, λ decay implementation of traditional computing hardware is confined by the extensive computation of power exponential decay. Herein, the value update equation for Sarsa (λ) is implemented by using the topological structure of the memristor array, without complex circuits. Where, most importantly, the critical λ decay function is realized by a TiOx‐based memristor with conductance decay property. The energy required for floating‐point operations can be significantly reduced while accelerating the convergence speed. Then, a path planning task is demonstrated based on intrinsic conductance decay property and shows outstanding performance. Finally, the information of rounds used for the task is obtained, which is based on the intrinsic decay property of the TiOx‐based memristor, maps into a 32 × 32 memristor array in parallel to calculate the value of each round. The results indicate that the experimental data have similar results to the simulations. Herein, thus, it provides a hardware‐enabled scheme for the memristor‐based RL algorithm implementation. [ABSTRACT FROM AUTHOR]

In this work, a compact model of the diffusive memristor is proposed from the perspective of the transition of electronic transmission mechanisms induced by the dynamics of the filament. First, a new physical model is established based on tunneling mechanisms that are used to fit the experimental data, and the results indicate that it is versatile enough for various diffusive memristors. In addition, the threshold voltage (V th) of the diffusive memristor negatively correlates with the ratio of ionic migration and the diffusion coefficient (u i /Ds), and the hold voltage (V h) positively correlates with the ratio of ionic diffusion and the migration coefficient (Ds/u i), which is useful for the selection of materials to achieve target electrical properties. Furthermore, the different parameters that influence the simulated switching curve are explored. The results indicate that the desired electrical characteristics can be obtained by adjusting these parameters. A compact electrical module model is then built and tested in LTspice to carry out bio-neuron and bio-synaptic performances completely. These simulations demonstrate that the model is reliable for exploring diffusive memristor applications. [ABSTRACT FROM AUTHOR]

We report on the investigation of linear and nonlinear spin-wave dynamics of a microstructured Co2FeAl Heusler waveguide using the microfocus Brillouin light scattering technique. A significantly increased decay length of 19.55 μm owing to decreased Gilbert damping has been observed for waves propagating in the linear regime. Furthermore, the localized edge mode caused by the demagnetizing field leads to the nonlinear generation of high-order harmonics at double and triple excitation frequencies at high powers. The obtained results provide valuable insights into the linear and nonlinear spin wave dynamics of the Heusler waveguide and could potentially be applied in the implementation of spin wave frequency multipliers for magnonic applications. [ABSTRACT FROM AUTHOR]

Memristive computing system (MCS), with the feature of in-memory computing capability, for artificial neural networks (ANNs) deployment showing low power and massive parallelism, is a promising alternative for traditional Von-Neumann architecture computing system. However, because of the various non-idealities of both peripheral circuits and memristor array, the performance of the practical MCS tends to be significantly reduced. In this work, a linear compensation method (LCM) is proposed for the performance improvement of MCS under the effect of non-idealities. By considering the effects of various non-ideal states in the MCS as a whole, the output error of the MCS under different conditions is investigated. Then, a mathematic model for the output error is established based on the experimental data. Furthermore, the MCS is researched at the physical circuit level as well, in order to analyze the specific way in which the non-idealities affect the output current. Finally, based on the established mathematical model, the LCM output current is compensated in real time to improve the system performance. The effectiveness of LCM is verified and showing outstanding performance in the residual neural network-34 network architecture, which is easily affected by the non-idealities in hardware. The proposed LCM can be naturally integrated into the operation processes of MCS, paving the way for optimizing the deployment on generic ANN hardware based on the memristor., (© 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Nowadays, two-dimensional (2D) materials with Janus structures evoke much attention due to their unique mechanical and electronic properties. In this work, Janus Pma2-Si 2 XY (X/Y = S/Se/Te, X ≠ Y) ferroelectric monolayers are firstly proposed and systematically investigated by first-principles calculations. These monolayers exhibit remarkable mechanical properties, including small Young's modulus values, negative Poisson's ratios (NPRs) and large critical strains, reflecting their exceptional flexibility and stretchability. More strikingly, the novel structures of Si 2 STe and Si 2 SeTe also endow them with in-plane spontaneous polarization ( P s ) and low energy barrier for phase transition, with P s and energy barrier values being 1.632 × 10 -10 C m -1 and 159 meV for Si 2 STe and 1.149 × 10 -10 C m -1 and 196.6 meV for Si 2 SeTe. The ab initio molecular dynamics (AIMD) simulations reveal high Curie temperatures ( T c ) for Si 2 STe and Si 2 SeTe, ranging between 1300 K and 1400 K. Additionally, Si 2 XY monolayers exhibit high anisotropic carrier mobility (∼10 3 cm 2 V -1 s -1 ) and an extraordinary light absorption coefficient (∼10 5 cm -1 ). Our research not only broadens the family of 2D Janus ferroelectric materials, but also demonstrates their potential applications in nanomechanical, nanoelectronic and optoelectronic devices.

Two-dimensional (2D) van der Waals (vdW) heterostructures with spontaneous intrinsic ferroelectrics play an essential role in ferroelectric memories. Also, the reversal of polarized directions induces band alignment transitions among different types to provide a new path for multifunctional devices. In this work, the structural and electronic properties of 2D VS 2 /Ga 2 O 3 vdW heterostructures under different polarizations were investigated using first-principles calculations with the vdW correction of the DFT-D2 method. The results reveal that the polarized direction of a 2D Ga 2 O 3 monolayer can cause a distinct band structure reversion from a metal to a semiconductor due to the shift of band alignment induced by the interlayer charge transfer. Moreover, the VS 2 /P↑ Ga 2 O 3 heterostructures retain type-I and type-II band alignments in the majority and minority channel, respectively, under an external electric field. Interestingly, applying the external electric field for VS 2 /P↓ Ga 2 O 3 heterostructures can lead to a transition from type-II to type-I in the majority channel, and from type-II to type-III in the minority channel. Our work provides a feasible way to realize 2D VS 2 /Ga 2 O 3 vdW heterostructures for potential applications in ferroelectric memories and electrostatic gating dependent multiple band alignment devices.