144results about "CAD with nanotechnology" patented technology

The invention relates to a method and a system for manufacturing a nano modified polyethylene electronic wire in the field of intelligent manufacturing equipment industry. The method comprises the following steps of: extracting temperature fluctuation characteristics from temperature distribution in a plasticizing process, dynamically adjusting parameters of a cooling system by adopting a fuzzy control algorithm aiming at the fluctuation characteristics, and determining temperature consistency in a forming stage; temperature consistency data in the forming stage are obtained, a preset wire diameter tolerance threshold value is combined, if the temperature consistency is lower than the threshold value, the extrusion speed is adjusted through a servo motor, and the optimized wire diameter precision is obtained; melt rheological parameters are extracted from the wire diameter precision data, melt heat conduction uniformity is calculated by adopting a heat conduction model of a nano-modified polyethylene material, and the formation strength of an interface permeation network is determined; and rheological property data are obtained, a real-time cooperative control algorithm is adopted, coupling parameters of melt pressure and temperature are dynamically adjusted, and optimized extrusion molding process parameters are obtained.

The invention discloses an atomic scale MD-KMC parallel simulation unified modeling method and system. The method comprises the following steps: establishing an atomic scale unified architecture; carrying out molecular dynamics simulation based on the atomic scale unified architecture; and dynamics Monte Carlo simulation based on the atomic scale unified architecture. According to the method, decoupling between the MD software development module and the KMC software development module can be facilitated; the effect of optimizing one part and improving multiple parts is achieved, MD simulation and KMC simulation are supported, and meanwhile the large-scale simulation calculation level is achieved.

The invention relates to the technical field of nuclear materials, and discloses an anti-radiation metal material nanocrystallization design method based on a cellular model, and the method comprises the steps: building a defect reaction list and a rate coefficient, building a cluster dynamics model framework, determining model parameters, and carrying out simulation calculation. The dynamic influence of the grain boundary on defect evolution is comprehensively depicted by introducing the grain boundary type capture trap strength model considering the defect dynamic behavior, the limitation that an existing model is only based on the relation between simple absorption strength and grain size is solved, and the method is suitable for the unbalanced defect behavior in the long-time dynamic evolution process. And cross-scale defect behavior simulation is realized. Through the method, the influence of different grain sizes and grain boundary characteristics on the anti-radiation performance of the material can be deeply researched, an optimal design scheme is provided, the anti-radiation performance of the material is improved, the service life is prolonged, and powerful support is provided for the safety and economy of a nuclear reactor.

The embodiment of the invention provides a multi-physical field modeling method of a micro-nano interconnection structure, electronic equipment and a storage medium, and the method comprises the steps: constructing a space body model of the micro-nano interconnection structure, and determining a grid discrete representation parameter of the micro-nano interconnection structure in the space body model; constructing an electromagnetic field model according to the network discrete representation parameters, and determining target heat and electromagnetic force parameters according to a relational expression representing the electromagnetic field model and the network discrete representation parameters; constructing a thermal field model according to the target heat and the grid discrete representation parameter, and determining a target temperature distribution parameter and a thermal stress parameter according to a relational expression representing the thermal field model; constructing a stress field model according to the grid discrete representation parameter, the target temperature distribution parameter, the thermal stress parameter and the electromagnetic force parameter, and determining a target relational expression according to a relational expression representing the stress field model; and determining a target parameter of the micro-nano interconnection structure based on the target relational expression. According to the invention, the determination accuracy of the degradation parameter of the micro-nano interconnection structure is improved.

The invention discloses a porcelain piece stress reduction method and system based on elasticity modulus regulation and control, and belongs to the technical field of stress research and nondestructive testing, and the method comprises the following steps: generating a temperature-elasticity modulus characteristic curve, collecting geometrical morphology information of microdefects in a porcelain piece, and calculating a temperature-elasticity modulus characteristic curve; inputting a multi-physics field coupling finite element model taking a temperature change process of an extreme low-temperature working condition as a thermal load boundary condition, carrying out iterative optimization calculation to obtain an optimal elastic modulus combination which minimizes the maximum first principal stress in the porcelain part, and reversely solving an optimal manufacturing process parameter combination by utilizing a correlation model to obtain a final manufacturing process parameter combination. And calculating the predicted safe operation life cycle of the porcelain part through the fatigue crack propagation model. According to the method, by introducing dynamically changing material parameters and real microdefect data, the stress state of the porcelain piece under the extreme working condition is predicted and optimized, a corresponding process implementation path and service life evaluation are given, and the problems of inaccurate simulation and poor optimization effect caused by adoption of constant parameters in the prior art are solved.

Image sensing devices including meta lenses and methods for manufacturing the image sensing devices are disclosed. In an embodiments, an image sensing device includes a plurality of microlenses including first to fourth microlenses arranged in a (2×2) matrix structure, a plurality of optical filters disposed under the first to fourth microlens, and configured to correspond to the first to fourth microlenses, one microlens per optical filter, respectively, and an optical element disposed at a center of the (2×2) matrix structure among the first to fourth microlenses and configured to separate incident light into light rays of in different wavelength ranges of different colors to guide each of the light rays to one of the plurality of optical filters of a corresponding color.

The invention provides a rock mineral micromechanical evolution characterization method based on a convolutional neural network. The rock mineral micromechanical evolution characterization method comprises the following steps: step 1, obtaining rock sample micromechanical properties through a nanoindentation experiment; 2, performing deconvolution statistical analysis by using a Gaussian mixture model, and obtaining mechanical and volume fractions of each phase through a nanoindentation experiment; step 3, utilizing a Mori-Tanaka scale upgrading method to understand a convolution result, and performing rock macromechanical property prediction in combination with a convolutional neural network; and step 4, performing precision checking calculation on an output result of the convolutional neural network model until the prediction precision meets a preset requirement. According to the method, the microscopic mechanical parameters of the rock sample are obtained through the grid nanoindentation, statistical analysis is carried out on the nanoindentation test result by utilizing a deconvolution method, the mechanical property of the macroscale of the rock is obtained in combination with machine learning and a scale upgrading model, and a bridge for connecting the macroscale and the microscale is established; the method can effectively solve the problem of inaccurate prediction of rock micromechanical parameters on the macroscale and the problem of mechanical property characterization caused by scale difference.

The invention relates to the technical field of nanofluid enhanced heat transfer simulation, in particular to a molecular dynamics-based mixed nanofluid heat transfer characteristic analysis method, which comprises the following steps of: constructing a molecular model comprising copper, graphene nanoparticles and an ionic liquid / water mixed base solution, and calculating the heat transfer characteristic of the mixed nanofluid through a distribution potential function and a periodic boundary condition; and performing energy minimization and molecular dynamic relaxation to achieve thermodynamic equilibrium. A steady-state temperature gradient is established based on the unbalanced molecular dynamics principle, and the thermal conductivity is calculated according to the Fourier law. Microcosmic parameters such as a radial distribution function, mean square displacement and the number of hydrogen bonds are synchronously analyzed, and a coupling mechanism of interaction and heat transfer performance of the nano-particle-base fluid interface is disclosed. According to the method, the heat transfer characteristic of the mixed nanofluid can be clarified on the molecular scale, and an efficient and reliable analysis means is provided for nanofluid screening and design in application of a solar heat collector, a heat management system and the like.

The patent presents a machine learning technique to determine the crystal phase distribution of polycrystalline thin films in nanodevices. It involves adjusting simulation parameters of transmission electron microscope software to generate a database of simulation images. A dedicated convolutional neural network is then built based on specific crystallographic parameters. This network is trained on the image dataset obtained. Once trained, it analyzes transmission electron microscope images of grains within actual thin films to swiftly and accurately identify crystal phase distribution. This innovation replaces manual methods, enabling automatic and reliable identification of crystal phase distribution in real polycrystalline thin films.

The invention discloses a carbon nano tube Raman spectrum batch simulation method based on Gaussian light spot modeling and a related device, and relates to the technical field of high-flux carbon nano tube characterization, the method comprises the following steps: constructing a laser light spot area with light spot intensity conforming to Gaussian distribution based on a Gaussian model; randomly generating a plurality of simulation space coordinates in the region, and enabling the simulation space coordinates to correspond to Raman spectrum data of the carbon nanotubes with different chiral indexes; obtaining the effective chord length of the carbon nanotube, and calculating the signal intensity of the single tube in combination with the light spot intensity distribution; calculating the spatial weight of each carbon nanotube by taking the signal intensity of the central position of the light spot as a reference; and obtaining corresponding Raman spectrum data from a pre-established single-spectrum database in combination with the chiral index, and carrying out weight superposition to obtain total Raman spectrum data. According to the scheme, an actual detection scene can be accurately reproduced, and time consumed by single-time carbon nanotube Raman spectrum superposition is lt; and multi-chirality combination is supported, the simulation precision and efficiency are remarkably improved, and the characterization requirement of the high-flux carbon nano tube is met.

This invention belongs to the field of concrete repair and provides a method and system for targeted control of the transition zone at the repair interface of existing concrete substrates. The method for targeted control of the transition zone at the repair interface of existing concrete substrates includes calculating the average micromechanical properties and the average phase content of each segment at different distances from the repair interface; fitting the average micromechanical properties of each segment at different distances from the repair interface with the average content of each phase to determine the phase category that has the greatest impact on the micromechanical properties; based on the fitting relationship between the micromechanical properties and the phase that has the greatest impact, determining the content and distribution of the phase that has the greatest impact on the micromechanical properties corresponding to the set micromechanical property requirements, and thus obtaining a matching composition and proportion control scheme for the repair material. This improves the accuracy of targeted control of the repair interface of existing concrete substrates.

An apparatus includes one or more processors configured to train an artificial neural network for an analysis target including at least one of a first artificial neural network corresponding to a current-voltage model and a second artificial neural network corresponding to a capacitance-voltage model by using a geometric parameter and a process parameter as an input value; and extract a compact model by determining any one or any combination of any two or more of a current-voltage model corresponding to the trained first artificial neural network and a capacitance-voltage model corresponding to the trained second artificial neural network based on an input value of an input layer, a weight of a hidden layer, a bias of the hidden layer, an output value of the hidden layer, an output value of the output layer, and an activation function obtained from the trained first and second artificial neural networks.

One aspect of this description relates to a method for operating an integrated circuit (IC) manufacturing system. The method includes placing a first nano-sheet structure within a IC layout diagram. The first nano-sheet structure has a first width. The method includes abutting a second nano-sheet structure with the first nano-sheet structure. The second nano-sheet structure has a second width. The second width is less than the first width. The method includes generating and storing the IC layout diagram in a storage device.

The invention discloses a simulation modeling method and device for a nanoscale device, relates to the technical field of semiconductor modeling and simulation, and aims to solve the problem that efficiency and precision cannot be considered in nanoscale device modeling and simulation in the prior art. The method comprises the steps that key material parameters are extracted from electronic structure basic data of a target nanoscale device, a default material parameter library in TCAD software is corrected based on the key material parameters, and a corrected material parameter library is obtained; electronic structure basic data is obtained based on a density functional theory; based on the corrected material parameter library, optimizing adjustable parameters of a ballistic transport model preset in TCAD software in combination with a transport IV curve to obtain a ballistic transport model after parameter optimization; the transportation IV curve is generated by adopting a quantum transportation method based on an unbalanced Green function in combination with key material parameters; and integrating the key material parameters with the ballistic transport model after parameter optimization, and constructing a simulation parameter library of the target nanoscale device.

The present invention belongs to the field of NEMS micro-pressure sensitive sensor design, specifically a PZT-based graphene composite pressure-sensitive thin film structure based on the flexoelectric polarization-enhanced force-electric coupling mechanism, which includes a Si / SiO2 substrate. A back cavity is provided on the Si / SiO2 substrate. A metal layer of pt / Ti is sputtered on the front surface of the Si / SiO2 substrate. A PZT ferroelectric thin film is sputtered on the metal layer of pt / Ti. A graphene thin film is on the PZT ferroelectric thin film. Metal electrodes are sputtered on both sides of the graphene thin film. A packaging substrate is on the back surface of the Si / SiO2 substrate. The present invention regulates the graphene thin film through the polarization voltage generated by the bending of the PZT ferroelectric thin film, applies a bias voltage to the graphene thin film, indirectly characterizes the transport of graphene carriers through the output current of the circuit, and further characterizes the change in the energy band of the graphene thin film, completing the exploration of the mechanism of regulating the energy band structure and electrical transport of graphene by PZT flexoelectric polarization, and completing the design of a high-sensitivity force-electric coupling PZT-based graphene composite pressure-sensitive thin film structure.

The invention provides an inverse Monte Carlo method based on an upper bound confidence interval (UCB), and the inverse Monte Carlo method is used for structural optimization in spectrogram inversion. According to the method, an upper bound confidence interval strategy in reinforcement learning is combined, an optimal atom movement strategy is intelligently selected in the optimization process, and efficient structure inversion modeling is achieved. A user can specify an experiment spectrogram according to needs, and a program can efficiently conduct reverse speculation on structure information under the condition that an initial structure is not used.

The invention provides a method for analyzing the wetting behavior of a liquid drop on a rough solid surface under the action of an electric field, and the method comprises the following steps: S1, building an initial simulation system and a simulation model based on a research and information collection result; s2, configuring operation parameters of the simulation model, and executing simulation calculation; s3, in the molecular dynamics simulation process, applying electric fields with different intensities, and monitoring the state change of the simulation system until the simulation system reaches dynamic balance; and S4, exporting the calculation data in the dynamic balance state, and carrying out visualization processing and data analysis on the calculation data to summarize the internal law of the calculation data. Influences of electric field intensity and roughness factors on complex electrowetting are described through a contact angle and some interface characteristics, and an electrowetting expansion mechanism of micro-nano liquid drops on a rough wall surface is explained from a microscopic perspective.

The present invention discloses a method and system for optimizing the configuration of nanostructure scattering measurements based on condition numbers, belonging to the field of photolithography technology. The method includes: step S1, determining the morphological parameters to be measured of the nanostructure and the fixed parameters in the parameter extraction process of the scattering measurement; step S2, constructing an evaluation function for measuring the fitting error between the measured signal and the theoretical signal in the scattering measurement based on the morphological parameters to be measured and the fixed parameters; step S3, obtaining the error transfer matrix of the morphological parameters to be measured based on the evaluation function; step S4, calculating the condition number of the error transfer matrix of the morphological parameters to be measured under different measurement conditions, and the measurement condition with the smallest condition number is the optimal configuration. The present invention also provides a system for optimizing the configuration of nanostructure scattering measurements based on condition numbers. The present invention provides a new measurement configuration optimization scheme for achieving high measurement precision, accuracy, and good robustness in nanostructure scattering measurements.

The invention discloses a full-polarization light-splitting type super-structure surface covering a mid-infrared broadband and a method of the full-polarization light-splitting type super-structure surface. The super-structure surface is formed by periodically arranging super-structure units, each super-structure unit comprises three groups of nano-structures used for regulating and controlling different cross-polarization, each group of nano-structures respectively control a pair of cross-polarization, each group of nano-structures comprises three anisotropic nano-columns, and the nano-columns of the three groups of nano-structures are arranged in a staggered manner. According to the invention, on the basis of the structural design of space polarization multiplexing, the independent regulation and control capability of the metasurface on the phase, amplitude and polarization state of a light field is fully utilized, the metasurface with polarization selective response is constructed, and the full Stokes polarization decomposition and focusing function of the mid-infrared broadband (3-5 [mu] m) is realized at the sub-wavelength scale; incident light in different polarization states can be respectively focused to different spatial positions, so that the polarization beam splitting efficiency and the imaging quality are remarkably improved, and the application of polarization information in complex light field detection and infrared imaging is promoted.

The invention provides a system and a method for testing and analyzing the dust holding capacity of a nano-filtration material, and relates to the technical field of nano-filtration material testing, three-dimensional modeling is performed on the nano-filtration material to obtain a three-dimensional digital model of the nano-filtration material; performing dust holding process simulation of multi-particle-size dust load on the three-dimensional digital model to obtain a response curve of the filtering resistance of the three-dimensional digital model in a dust holding simulation period; performing response matching analysis on the dust holding process simulation of the three-dimensional digital model to obtain a simulation matching degree of the dust holding process simulation of the three-dimensional digital model; further determining simulation constraint conditions of dust holding process simulation of the three-dimensional digital model, and adjusting dust holding simulation parameters of the three-dimensional digital model based on the simulation constraint conditions; and generating a simulation test result of the dust holding capacity of the nano-filtration material through the adjusted dust holding simulation parameters. According to the method, the authenticity of simulation calculation of the dust holding capacity of the nano-filtration material can be improved, so that the test cost of the nano-filtration material is reduced.

Within the layout of the first surface in the flip-chip configuration, a bump restriction region is mapped according to a set of bump placement constraints, wherein the first bump placement constraint specifies a range of allowable distances between bumps in the layout of the first surface and qubit chip components, and wherein the second bump placement constraint specifies a range of allowable distances between bumps in the layout of the second surface in the flip-chip configuration and qubit chip components. Conductive material is deposited outside the bump restriction region to form bumps, wherein the bumps include conductive structures that electrically couple signals between the first surface and the second surface and are positioned according to the set of bump placement constraints.

A circuit layout processing method and apparatus, a device, a storage medium and a program product, which relate to the technical field of micro-nano processing. The circuit layout processing method comprises: acquiring an initial circuit layout of a chip (21), the initial circuit layout comprising initial position information of a line in the chip; acquiring a frame correction value of the line (22), the frame correction value being a correction value which is set on the basis of an etching error during a processing process of the chip; and correcting the boundary of the line in the initial circuit layout on the basis of the initial position information and the frame correction value to obtain a corrected circuit layout (23). According to the present solution, the influence caused by the etching error may be reduced during an etching process in chip manufacturing while ensuring the design efficiency of the circuit layout of the chip.

The invention provides a three-stage translocation model for revealing Aerolysin nanopore fingerprint characteristics, a mathematical framework related to the nanopore fingerprint characteristics (charge-to-mass ratio, resistance-to-mass ratio and other parameters) is established by decomposing a charged single-molecule translocation total process into three stages of pore entry, perforation and pore exit, and a single-molecule-level fingerprint characteristic extraction technology is established. And explaining the translocation attribute of the single molecule by using fingerprint characteristics. Theoretical and experimental analysis proves that the molecular charge-to-mass ratio is positively correlated with the metathesis speed, the molecular metathesis speed is dominated by the charge-to-mass ratio during hole entering, and the acceleration phenomenon of molecules in the hole exiting stage is predicted. The invention provides a framework for quantitative characterization, modeling and prediction of nanopore fingerprints in biosensing applications.

The invention discloses a preparation method of a near-infrared achromatic super lens based on a nanoimprint technology, and relates to the technical field of micro-nano manufacturing. The preparation method comprises the following steps: 1, designing a target super lens by using finite element simulation software based on a time domain finite difference method, a particle swarm algorithm and a scalar diffraction algorithm; 2, preparing a silicon female die corresponding to the super lens according to the designed target super lens parameters by using electron beam lithography and deep reactive ion etching; 3, carrying out surface fluorination treatment on the silicon female die; and 4, preparing a corresponding sub-mold according to the silicon female mold, carrying out imprinting by using a nano imprinting technology and the sub-mold, and finally, etching the a-Si layer on the surface of the substrate through deep reactive ion etching by taking imprinting glue as an etching mask to obtain the super lens. According to the invention, the nanoimprint technology can be used for manufacturing the near-infrared achromatic superlens, so that the near-infrared achromatic superlens gets rid of dependence on a high-precision photoetching machine.

The invention discloses an optical response electromagnetic simulation method and system for dynamic evolution of a nano-array structure, and the method comprises the following steps: carrying out the geometric modeling of the nano-array structure, dispersing the periodic units in the nano-array structure into dipole arrays at equal intervals, and distributing a corresponding polarizability to each dipole; initializing a simulation condition, and setting an incident light condition, a medium environment and a periodic boundary condition; establishing a dipole interaction matrix meeting a periodic boundary condition, calculating an inverse matrix, and further calculating a polarization vector of a dipole; updating the inverse matrix based on a rank-one or low-rank inverse updating method; and calculating an optical response parameter of the nano array based on the updated polarization vector. On the premise of ensuring the calculation accuracy, the optical response change of the nano array structure in the dynamic evolution process can be efficiently tracked, and the method is suitable for electromagnetic simulation and optical optimization calculation scenes involving a large number of intermediate states.

The invention belongs to the technical field of environmental pollution monitoring, and particularly relates to an underground water pollution tracing method combining two detection means of surface enhanced Raman scattering (SERS) and nanopore sequencing and two intelligent algorithms of a twinNet and a long-short term memory (LSTM). Specifically, high-sensitivity detection is achieved through an SERS substrate, TwinNet improves the anti-interference capacity through two-channel input, nanopore sequencing and an improved LSTM algorithm are combined, a DNA library of microorganisms in underground water is obtained, time sequence data are processed through an LSTM model, the time change trend of microbial communities is predicted, and reverse prediction of micropollutants is achieved. The device and the method have the beneficial effects that the device and the method can be used for monitoring the influence of micropollutants in underground water on microbial communities, and can also give out early warning on the micro-pollution of the underground water through the change of microbial populations.

The invention discloses a signal amplifier design method based on a spin bit-nano mechanical oscillator composite system. The signal amplifier design method comprises the following steps: 1) constructing the spin bit-nano mechanical oscillator composite system; 2) using a strong pumping field, a weak detection field and a magnetic field to jointly act on the spin bit-nano mechanical oscillator composite system in the step 1) to realize coupling of spin bits and nano mechanical oscillators; and 3) adjusting the intensity of the strong pumping field in the step 2) according to the gain factor alpha of the expected detection wave. According to the signal amplifier design method based on the spin bit-nano mechanical oscillator composite system, the design of the signal amplifier based on the spin bit-nano mechanical oscillator composite system can be guided; according to the invention, the amplification factor of the signal amplifier can be adjusted by changing the intensity of the strong pumping field.

The present invention relates to a computer-implemented method of simulating a physical parameter at an interior of a pipe, wherein the pipe is arranged passing through an enclosure containing an ignition source, using computational fluid dynamics, the method comprising: providing a model of the enclosure, the pipe, the ignition source, a mesh of a fluid within the enclosure, and the fire suppression system; activating the ignition source; simulating the physical parameter at the interior of the pipe by modelling a fire propagating from the ignition source, radiation within the enclosure, soot within the enclosure, chemical species mixing within the enclosure, and turbulence of the fluid inside the enclosure, and by modelling heat transfer from the fluid in the enclosure through the pipe using a conjugate heat transfer model; activating the fire suppression system; and simulating the physical parameter at the interior of the pipe over time by additionally modelling water spray and water mist suppression within the enclosure, evaporation of water droplets within the enclosure, and water-droplets on an exterior surface of the pipe.

This disclosure provides an apparatus comprising at least one non-temporary computer-readable storage medium storing instructions, and a processing circuit coupled to the at least one non-temporary computer-readable storage medium. The processing circuit is configured to, upon executing the instructions, provide randomized data to a neural network, receive a metagrating design from the neural network, generate diffraction performance values ​​based on the metagrating design for each of a plurality of non-zero-order

The invention belongs to the field of nanometer optical spectrums, and particularly relates to a dielectric characteristic inversion method of a nanometer material. According to the method, a physical model and a neural network are deeply fused to construct a hybrid optimization framework; the used s-SNOM response model accords with a theoretical physical mechanism and is fused with a theoretical dielectric model and a finite dipole model, and a simulation spectrum of the s-SNOM response model is verified and matched with an actually measured spectrum; latin hypercube sampling is used for carrying out initial estimation on dielectric key parameters, a loss optimal initial value which is initially obtained in a physical constraint range is obtained, and then a neural network is used for carrying out joint optimization on the initial value and extracted spectral features. Loss calculation in the whole process is obtained by means of mean square error calculation of a measured value and a simulation value, calculation of the simulation value is achieved through an s-SNOM response model, and physical accuracy of a fitting result is ensured. According to the method, the technical blank between a traditional physical model and a pure data driving method is filled up, and the method is of great significance to further development and application of a nanometer optical characterization technology.