Most Cited
2025, 74 (1): 012801.
doi: 10.7498/aps.74.20241178
Abstract +
2025, 74 (9): 090304.
doi: 10.7498/aps.74.20241262
Abstract +
This paper reviews the physical principles, development history of related application research, current research status and prospects of the Josephson voltage standard (JVS) working at liquid helium temperatures. The JVS working at liquid helium temperature has advantages of high mobility and low-energy consumption, and has a broad application prospect. This paper describes the research status of Josephson voltage standards, focusing on the possibility of developing a JVS based on high-temperature superconductors, and the challenges in chip preparation. In addition, a newly developed preparation technology for Josephson junction, namely the focused helium ion beam, is introduced. It has advantages in the preparation of high consistent Josephson junction arrays in high consistency. Therefore, it is a possible technical route for exploring the realization of JVS working at liquid helium temperature in the future.
2025, 74 (2): 027702.
doi: 10.7498/aps.74.20240906
Abstract +
2025, 74 (2): 025201.
doi: 10.7498/aps.74.20241330
Abstract +
The free energy contained in electron drift, electron collision, and plasma density gradient, temperature, magnetic field gradient can trigger off the instabilities with different frequencies and wavelengths in hall thrusters. The instabilities will destroy the stable discharge of plasma, affecting the matching degree between the thruster and the power processing unit, and reducing the performance of the thruster. Based on this, the instabilities triggered off by electron collision, plasma density gradient, and magnetic field gradient in the hall thruster are studied by using dispersion relation derived from the fluid model. The results are shown below. 1) When in the model includes the effects of electron inertia, collision between electrons and neutral atoms, and electron drift, instability can be excited at any axial position from the near anode region to the plume region of the thruster. With the increase of azimuthal wavenumber ${k_y} = 2\pi /\lambda $, the lower-hybrid mode excited by electron collision transitions into the ion sound mode, where ${k_y} = 2{\text{π }}/\lambda $, $\lambda $being the wave length. The real frequency ${\omega _{\text{r}}}$ corresponding to the maximum growth rate ${\gamma _{\max }}$ slightly decreases with collision frequency increasing for ${k_y} = 10{\text{ }}{{\text{ m}}^{ - 1}}$. However, the maximum real frequency and real frequency ${\omega _{\text{r}}}$ corresponding to the maximum growth rate ${k_y} = 300{{\text{ m}}^{ - 1}}$ will not change with collision frequency for ${k_y} = 300{\text{ }}{{\text{ m}}^{ - 1}}$. Independent of the value of ${k_y}$, the growth rate of mode triggered off by electron collision increases with collision frequency increasing. 2) The plasma density gradient effect plays a dominant role in triggering off instabilities when the electron inertia, electron-neutral collisions and plasma density gradient are simultaneously included in the model. The dynamic behavior of the model does not change with the increase of ${k_y}$, but the eigenvalue of the model increases with the ${k_y}$ increasing. Since the sign of anti-drift frequency induced by the plasma density gradient is changed, the mode eigenvalues have the opposite change trend on both sides of point ${\kappa _{\text{N}}}$. When the sign of ${\omega _r}$ and ${\omega _r}$ are opposite, the density gradient effect has a stabilization effect on instability excitation (${\kappa _{\text{N}}} > 0$). When the sign of ${\omega _{\text{s}}}$ and ${\omega _{\text{r}}}$ are the same, the density gradient effect enhances the excitation of instability (${\kappa _{\text{N}}} < 0$). 3) If the plasma density gradient, magnetic field gradient, electron inertia and electron-neutral collisions are included in the dispersion, the mode eigenvalue relies on the electron drift frequency, and the diamagnetic drift frequency induced by the density gradient and magnetic field gradient. When the density gradient effect and the magnetic field gradient effect are considered, there is a stable window in the discharge channel. However, if the electron inertia and electron-neutral collisions are also included, the stable window will disappear.
2025, 74 (1): 012101.
doi: 10.7498/aps.74.20241201
Abstract +
The nuclear mass model has significant applications in nuclear physics, astrophysics, and nuclear engineering. The accurate prediction of binding energy is crucial for studying nuclear structure, reactions, and decay. However, traditional mass models exhibit significant errors in double magic number region and heavy nuclear region. These models are difficult to effectively describe shell effect and parity effect in the nuclear structure, and also fail to capture the subtle differences observed in experimental results. This study demonstrates the powerful modeling capabilities of MLP neural networks, which optimize the parameters of the nuclear mass model, and reduce prediction errors in key regions and globally. In the neural network, neutron number, proton number, and binding energy are used as training feature values, and the mass-model coefficient is regarded as training label value. The training set is composed of the multiple sets of calculated nuclear mass model coefficients. Through extensive experiments, the optimal parameters are determined to ensure the convergence speed and stability of the model. The Adam optimizer is used to adjust the weight and bias of the network to reduce the mean squared error loss during training. Based on the AME2020 dataset, the trained neural network model with the minimum loss is used to predict the optimal coefficients of the nuclear mass model. The optimized BW2 model significantly reduces root-mean-square errors in double magic number and heavy nuclear regions. Specifically, the optimized model reduces the root-mean-square error by about 28%, 12%, and 18% near Z = 50 and N = 50; Z(N) = 50 and N = 82; Z = 82 and N = 126, respectively. In the heavy nuclear region, the error is reduced by 48%. The BW3 model combines higher-order symmetry energy terms, and after parameter optimization using the neural network, reduces the global root-mean-square error from 1.86 MeV to 1.63 MeV. This work reveals that the model with newly optimized coefficients not only exhibit significant error reduction near double magic numbers, but also shows the improvements in binding energy predictions for both neutron-rich and neutron-deficient nuclei. Furthermore, the model shows good improvements in describing parity effects, accurately capturing the differences related to parity in isotopic chains with different proton numbers. This study demonstrates the tremendous potential of MLP neural networks in optimizing the parameters of nuclear mass model and provides a novel method for optimizing parameters in more complex nuclear mass models. In addition, the proposed method is applicable to the nuclear mass models with implicit or nonlinear relationships, providing a new perspective for further developing the nuclear mass models.
2025, 74 (2): 027501.
doi: 10.7498/aps.74.20241340
Abstract +
Rare-earth elements share similar ground-state electronic properties, and their unique lanthanide contraction effect can lower the mixing enthalpy of rare-earth elements in high-entropy materials, which is of great significance for fabricating low-cost and high-performance high-entropy rare-earth intermetallic compounds. In this work, the magnetization reversal mechanisms of rapidly quenched ribbons such as Nd11.76Fe82.36B5.88 (NdFeB) and the relevant high-entropy rare-earth permanent magnet alloy compounds (La0.2Pr0.2Nd0.2Gd0.2Dy0.2)11.76Fe82.36B5.88 and (La0.2Pr0.2Nd0.2Gd0.2Tb0.2)11.76Fe82.36B5.88 are studied by analyzing the magnetization and demagnetization curves, supplemented by Henkel curves and magnetic viscosity coefficient S. Compared with the pure NdFeB sample, the high-entropy rare-earth permanent magnet has the inter-grain exchange coupling significantly enhanced and the magnetic dipole interaction weakened, indicating that the element diffusion mechanism in heavy rare-earth containing high-entropy material homogenizes the sample, and significantly increases the coercivity. The mechanism of the coercivity is the nucleation of magnetization reversal domains in the grains of the hard magnetic phase. The magnetization mechanism is dominated by pinning at low magnetic fields and by nucleation at high magnetic fields, which is different from the magnetization mechanism of pure NdFeB and has some similarities with the self-pinning mechanism. The magnetic viscosity coefficient of (La0.2Pr0.2Nd0.2Gd0.2Dy0.2)11.76Fe82.36B5.88 is larger than that of pure NdFeB. Due to the asynchrony of hard magnetic phase reversal and intergranular magnetic coupling in (La0.2Pr0.2Nd0.2Gd0.2Tb0.2)11.76Fe82.36B5.88, the magnetic viscosity coefficient is small but the anisotropy field is large. This indicates that high-entropy sample reduces the magnetocrystalline anisotropy field barrier but increases the magnetocrystalline coupling length. This suggests that the magnetization reversal of high-entropy rare-earth permanent magnet material is significantly different from that of conventional rare earth permanent magnet material and it is worthy of further in-depth research.
2025, 74 (1): 014202.
doi: 10.7498/aps.74.20241126
Abstract +
The Casimir effect, a macroscopic manifestation of quantum phenomena, arises from zero-point energy and thermal fluctuations. When two objects are brought into close proximity, the Casimir effect manifests as a repulsive force, while at greater separations, it transforms into an attractive force. There exists a specific distance at which the Casimir force vanishes, which is referred to as the stable Casimir equilibrium. Stable Casimir equilibrium arises from the curve minimum value of the Casimir energy, which can create spatial trapping. The manipulation of stable Casimir equilibrium provides promising applications in fields such as tunable optical resonators and self-assembly. This work presents a scheme for achieving tunable Casimir equilibrium in a dual-liquid system. The system comprises a multilayered stratified structure with a gold substrate. Above the gold substrate, a stratified liquid system is formed due to the immiscibility between organic solutions and water. The lower-density solution is at the top, while the higher-density solution is at the bottom. Our results suggest that a stable Casimir equilibrium for a suspended gold nanoplate can be realized, when the suspended gold nanoplate is immersed in organic solution of toluene or benzene. Moreover, the height of the suspended gold nanoplate, determined by the stable Casimir equilibrium, can be precisely tuned by changing the thickness of the water layer. The effects of finite temperature and ionic concentration on the Casimir equilibria are also analyzed in this work. The results suggest that the separation height of Casimir equilibrium decreases with the increase of temperature. Interestingly, when the Debye shielding length is comparable to or smaller than the separation length, the ion concentration in water significantly affects the Casimir pressure allowing for extensive modulations of Casimir equilibrium. This work opens up a new avenue for adjusting Casimir equilibrium and has important applications in “quantum trapping” of micro-nano particles.
2025, 74 (5): 054301.
doi: 10.7498/aps.74.20241286
Abstract +
Acoustic skyrmion modes are topological texture structures of velocity field vectors generated on the surface of acoustic structures. This protected vector distribution provides new opportunities for processing sound information, transmission, and data storage. In this study, a combined structure of waveguides and spiral structures is designed by using directional acoustic sources to excite waveguide mode transmission, thereby achieving selective excitation of localized acoustic skyrmion modes. Through theoretical analysis and numerical simulations, the pressure field distribution and velocity field distribution excited by spin acoustic sources, Huygens acoustic sources, and Janus acoustic sources in this structure are investigated, demonstrating the directional transmission properties of acoustic surface waves and the selectively excited acoustic skyrmion modes in the combined structure. Numerical calculations reveal that when the spin acoustic source excites acoustic surface waves propagating along the waveguide, the acoustic skyrmion modes in the helical structure in the direction corresponding to the propagation are selectively excited. When the Huygens source excites acoustic surface waves propagating along the waveguide, the acoustic skyrmion modes in the right or left direction are selectively excited. However, when the Janus source excites acoustic surface waves propagating along the waveguide, the acoustic skyrmion modes in the upward or downward direction are selectively excited. This selective excitation of acoustic skyrmion modes by a directional acoustic source provides a new way to design advanced acoustic information processing functional devices.
2025, 74 (1): 017401.
doi: 10.7498/aps.74.20241534
Abstract +
2025, 74 (2): 024203.
doi: 10.7498/aps.74.20241352
Abstract +
Complementary metal oxide semiconductor (CMOS) image sensors have been increasingly widely used in the field of radiation environments due to their numerous advantages, and their radiation effects have also attracted much attention. Some experimental studies have shown that the saturation output of CMOS image sensors decreases after irradiation, while others have reported that it increases. In this work, the further in-depth research on the inconsistent results is conducted based on the proton irradiation experiments and TCAD simulations, and the degradation mechanism in full well capacity, conversion factor, and saturation output of the 4T pinned photodiode (PPD) CMOS image sensors due to proton cumulative radiation effects are also analyzed. In experiments, the sensors are irradiated by 12 MeV and 60 MeV protons with a fluence up to 2× 1012 cm–2. The sensors are unbiased during irradiation. The experimental results show that proton irradiation at 12 MeV and 60 MeV result in an increase of 8.2% and 7.3% in conversion factor, respectively, and a decrease of 7.3% and 3.8% in full well capacity, respectively. The saturation output shows no significant change trend under 12 MeV proton irradiation, but increases by 3% under 60 MeV proton irradiation. In the TCAD simulation, a three-dimensional 4T PPD pixel model is constructed. A simulation method that combines the trap and gamma radiation model in TCAD with the mathematical model of minority carrier lifetime is used to simulate global and local cumulative proton irradiation in order to analyze the degradation mechanism. It is proposed that the degradation of saturation output at the pixel level is determined by the full well capacity of PPD, the physical characteristics of the reset transistor and the capacitance of floating diffusion, but they have opposite effects. Proton irradiation leads to the accumulation of oxide-trapped positive charges in the shallow trench isolation on both sides of PPD, resulting in the formation of leakage current path in silicon, thereby reducing the full well capacity. A decrease in full well capacity leads to a decrease in saturation output. While, the radiation effect of the reset transistor causes the potential of floating diffusion (FD) to increase during the FD reset phase, further leading to an increase in saturation output. The irradiation causes the capacitance of the floating diffusion to decrease, resulting in an increase in conversion factor and consequently increasing the saturation output. The difference in radiation sensitivity among the three influence factors at the pixel level may result in a decrease or increase in saturation output with proton fluence increasing. The above work comprehensively reveals and analyzes the mechanisms of degradation in full well capacity, conversion factor and saturation output after irradiation, and the research results have certain guiding significance for analyzing the radiation damage to CMOS image sensors.
2025, 74 (4): 044701.
doi: 10.7498/aps.74.20241453
Abstract +
The conjugate heat transfer at the particle-fluid interface and the collision between particles play a crucial role in the sedimentation process of particles. In this work, the recent volumetric lattice Boltzmann method for thermal particulate flows with conjugate heat transfer is adopted to investigate the drafting-kissing-tumbling movement in the sedimentation process of two particles in a closed channel. This volumetric lattice Boltzmann method is based on double distribution functions, with the density distribution function used for the velocity field and the internal energy distribution function used for the temperature field. It is a single-domain approach, and the nonslip velocity condition within the solid domain can be strictly ensured. The difference in thermophysical properties between the solid and fluid can be correctly handled, and the conjugate heat transfer condition can be automatically achieved without any additional treatments. Based on this particle-resolved simulation, the influences of the solid-to-fluid specific heat ratio, the Grashof number, and the particle’s initial temperature on the drafting-kissing-tumbling movement are discussed in detail. It is found that the fluid cooled by the particle and thus subjected to the downward buoyancy force can promote particle sedimentation. As the specific heat ratio increases, the particle’s temperature rises relatively slowly. In the sedimentation of two cold particles, the drafting duration and tumbling duration of the drafting-kissing-tumbling movement decrease when the heat capacity ratio increases. In contrast, the kissing duration increases as the heat capacity ratio increases. When the Grashof number increases, the heat transfer between the particle and fluid is enhanced, and the drafting duration significantly decreases while the kissing duration and tumbling duration remain almost unchanged in the sedimentation of two cold particles. The particle’s initial temperature greatly affects the occurrence moment of the drafting-kissing-tumbling movement. To be specific, the drafting-kissing-tumbling movement occurs at the earliest moment for the sedimentation of two cold particles, followed by the sedimentation of one cold and one hot particle, and the latest for the sedimentation of two hot particles. The promoting effect of the low particle’s initial temperature on the drafting-kissing-tumbling movement mainly takes place in the dragging stage and kissing stage. The particle’s initial temperature has almost no influence on the tumbling duration.
2025, 74 (6): 060701.
doi: 10.7498/aps.74.20240999
Abstract +
In order to solve the current lack of rigorous theoretical models in the machine learning process, in this paper the iterative motion process of machine learning is modeled by using quantum dynamic method based on the principles of first-principles thinking. This approach treats the iterative evolution of algorithms as a physical motion process, defines a generalized objective function in the parameter space of machine learning algorithms, and regards the iterative process of machine learning as the process of seeking the optimal value of this generalized objective function. In physical terms, this process corresponds to the system reaching its ground energy state. Since the dynamic equation of a quantum system is the Schrödinger equation, we can obtain the quantum dynamic equation that describes the iterative process of machine learning by treating the generalized objective function as the potential energy term in the Schrödinger equation. Therefore, machine learning is the process of seeking the ground energy state of the quantum system constrained by a generalized objective function. The quantum dynamic equation for machine learning transforms the iterative process into a time-dependent partial differential equation for precise mathematical representation, enabling the use of physical and mathematical theories to study the iterative process of machine learning. This provides theoretical support for implementing the iterative process of machine learning by using quantum computers. In order to further explain the iterative process of machine learning on classical computers by using quantum dynamic equation, the Wick rotation is used to transform the quantum dynamic equation into a thermodynamic equation, demonstrating the convergence of the time evolution process in machine learning. The system will be transformed into the ground energy state as time approaches infinity. Taylor expansion is used to approximate the generalized objective function, which has no analytical expression in the parameter space. Under the zero-order Taylor approximation of the generalized objective function, the quantum dynamic equation and thermodynamic equation for machine learning degrade into the free-particle equation and diffusion equation, respectively. This result indicates that the most basic dynamic processes during the iteration of machine learning on quantum computers and classical computers are wave packet dispersion and wave packet diffusion, respectively, thereby explaining, from a dynamic perspective, the basic principles of diffusion models that have been successfully utilized in the generative neural networks in recent years. Diffusion models indirectly realize the thermal diffusion process in the parameter space by adding Gaussian noise to and removing Gaussian noise from the image, thereby optimizing the generalized objective function in the parameter space. The diffusion process is the dynamic process in the zero-order approximation of the generalized objective function. Meanwhile, we also use the thermodynamic equation of machine learning to derive the Softmax function and Sigmoid function, which are commonly used in artificial intelligence. These results show that the quantum dynamic method is an effective theoretical approach to studying the iterative process of machine learning, which provides a rigorous mathematical and physical model for studying the iterative process of machine learning on both quantum computers and classical computers.
2025, 74 (2): 025202.
doi: 10.7498/aps.74.20241241
Abstract +
The streamer propagation and electric field distribution in a two-dimensional fluid model of a packed bed reactor (PBR) filled with carbon dioxide are comprehensively studied by utilizing the PASSKEy simulation platform in this work. The spatiotemporal evolution of electron density, electric fields and key plasma species in the discharge process are studied in depth. The PBR with layered dielectric spheres is simulated by using the model, indicating that the inner sides of the first layer and the second layer of dielectric spheres are not the main regions for reactions such as CO2 dissociation; instead, the main regions are along the streamer propagation path and the outer side of the first layer of dielectric sphere. In this work, the propagation of streamers in an electric field is investigated, highlighting the influence of anode voltage rise and dielectric polarization on local electric field enhancement. This enhancement leads the electron density and temperature to increase, which facilitats streamer propagation and the formation of filamentary microdischarges and surface ionization waves. This work provides a detailed analysis of the local electric field evolution at specific points within the PBR, and a further investigation of the spatiotemporal dynamics of spatial and surface charges, revealing that negative charges concentrate in the streamer and on the dielectric surface, with density being significantly higher than that of positive charges. The positive charge distribution is closely related to the streamer path, and with time going by, the charge distribution becomes dominated in the discharge space. This work also explores the surface charge deposition on the dielectric spheres, and discusses the evolution trend of the distribution. Additionally, this work discusses the temporal and spatial evolution of key plasma species, including ions and radicals, and their contributions to the overall discharge characteristics. The production mechanisms of carbon monoxide particles, carbon dioxide ions, and oxygen ions are analyzed, with a focus on their spatial distribution and correlation with electron density. Finally, the energy deposition within the PBR is examined by integrating the spatial energy deposition of electrons and major positive ions. The results indicate a total energy deposition value of approximately 1.428 mJ/m, with carbon dioxide ions accounting for 8.8% of this value.
2025, 74 (3): 036201.
doi: 10.7498/aps.74.20241030
Abstract +
In nanosystems, the metallic nanowires are subjected to significant and cyclic bending deformation upon being integrated into stretchable and flexible nanoelectronic devices. The reliability and service life of these nanodevices depend fundamentally on the bending mechanical properties of the metallic nanowires that serve as the critical components. An in-depth understanding of the deformation behavior of the metallic nanowires under bending is not only essential but also imperative for designing and manufacturing high-performance nanodevices. To explore the mechanism of the bending plasticity of the metallic nanowire, the bending deformations of B2-FeAl alloy nanowires with various crystallographic orientations, sizes and cross-sectional shapes are investigated by using molecular dynamics simulation. The results show that the bending behavior of the B2-FeAl alloy nanowires is dependent on neither their size nor cross-sectional shape of the nanowire, but it is highly sensitive to its axial orientation. Specifically, both $\left\langle {111} \right\rangle $- and $\left\langle {110} \right\rangle $-oriented nanowires are generated through dislocation nucleation during bending, with the $\left\langle {111} \right\rangle $-oriented nanowires failling shortly after yielding due to brittle fracture, while the $\left\langle {110} \right\rangle $-oriented nanowires exhibit good ductility due to uniform plastic flow caused by continuous nucleation and stable motion of dislocations. Unlike the aforementioned two nanowires, the bending plasticity of the $\left\langle {001} \right\rangle $-oriented nanowire is mediated by the stress-induced transition from B2 phase to L10 phase, which leads to excellent ductility and higher fracture strain. The orientation dependence of bending deformation can be understood by considering the Schmid factor. Moreover, the plastically bent nanowires with $\left\langle {110} \right\rangle $ and $\left\langle {001} \right\rangle $ orientation are able to recover to their original shape upon unloading, particularly, the plastic deformation in the $\left\langle {001} \right\rangle $-oriented nanowire is recoverable completely via reverse transformation from L10 to B2 structures, exhibiting superelasticity. This work elucidates the deformation mechanism of the B2-FeAl alloy nanowires subjected to bending loads, which provides a crucial insight for designing and optimizing flexible and stretchable nanodevices based on metallic nanowires.
Finite element prediction and device performance of piezoelectric fiber composite based smart sensor
2025, 74 (5): 057701.
doi: 10.7498/aps.74.20241379
Abstract +
Macro fiber composite (MFC) is extensively utilized in aviation, aerospace, civilian, and military domains due to its high piezoelectricity, flexibility, and minimal loss. Nevertheless, existing research on MFC sensors has focused on material applications, with a conspicuous lack of systematic investigation into the simulation and modeling of MFC sensor devices. In this study, three models, namely, a representative volume element (RVE) model, a direct model, and a Hybrid model are established to analyze the finite element models of MFC, covering the scales from micro to macro. On the one hand, the equivalent RVE model contributes to an understanding of the internal electric field distribution in MFC, thereby establishing a theoretical foundation for force-electric coupling. On the other hand, the application of the direct model and hybrid model accords with the boundary conditions in MFC applications, which lays a theoretical foundation for the stress sensing and resonance sensing mechanisms of MFC. These models constitute effective tools for predicting the sensing performance of MFC smart element sensors. The simulation outcomes indicate that resonant sensors exhibit significantly superior performance compared with patch sensors. Under the conditions where the excitation acceleration is 5 m/s² and the cantilever substrate length is 80 mm, the simulated resonant frequency of the MFC resonant sensor is 67 Hz, with an output voltage of 4.17 V. Experimental results confirm these findings. It is reported that the resonant frequency is 74 Hz and the output voltage is 3.59 V for the MFC sensor. The remarkable consistency between the simulation results and experimental data of the MFC sensor deserves to be emphasized. In addition, the MFC sensor shows excellent sensing sensitivity at low frequencies, with a sensitivity of 7.35 V/g. Obviously, MFC shows remarkable sensing characteristics at low-frequency resonance. The three finite element models established in this work can well predict the sensing performance of MFC sensors, thus ensuring reliable prediction of the performance of such sensors.
2025, 74 (1): 014203.
doi: 10.7498/aps.74.20241458
Abstract +
Fractional-order vortex beams possess fractional orbital angular momentum (FOAM) modes, which theoretically have the potential to increase transmission capacity infinitely. Therefore, they have significant application prospects in the fields of measurement, optical communication and microparticle manipulation. However, when fractional-order vortex beams propagate in free space, the discontinuity of the helical phase makes them susceptible to diffraction in practical applications, thereby affecting the accuracy of OAM mode recognition and severely limiting the use of FOAM-based optical communication. Achieving machine learning recognition of fractional-order vortex beams under diffraction conditions is currently an urgent and unreported issue. Based on ResNetA, a deep learning (DL) method of accurately recognizing the propagation distance and topological charge of fractional-order vortex beam diffraction process is proposed in this work. Utilizing both experimentally measured and numerically simulated intensity distributions, a dataset of vortex beam diffraction intensity patterns in atmospheric turbulence environments is created. An improved 101-layer ResNet structure based on transfer learning is employed to achieve accurate and efficient recognition of the FOAM model at different propagation distances. Experimental results show that the proposed method can accurately recognize FOAM modes with a propagation distance of 100 cm, a spacing of 5 cm, and a mode spacing of 0.1 under turbulent conditions, with an accuracy of 99.69%. This method considers the effect of atmospheric turbulence during spatial transmission, allowing the recognition scheme to achieve high accuracy even in special environments. It has the ability to distinguish ultra-fine FOAM modes and propagation distances, which cannot be achieved by traditional methods. This technology can be applied to multidimensional encoding and sensing measurements based on FOAM beam.
2025, 74 (8): 086101.
doi: 10.7498/aps.74.20250097
Abstract +
- 1
- 2
