51results about "CAD with nanotechnology" patented technology

An integrated circuit design tool includes a cell library. An entry in the cell library comprises a specification of the cell including a first transistor and a second transistor. The first transistor can include a first set of nanowires or 2D material strips arranged in parallel to form a channel structure, and a gate conductor disposed across the first set of nanowires or 2D material strips. The second transistor can include a second set of nanowires or 2D material strips arranged in parallel to form a channel structure, and a gate conductor disposed across the first set of nanowires or 2D material strips. The number of nanowires or 2D material strips in the first set can be different from the number of nanowires or 2D material strips in the second set, so that the drive power of the individual transistors can be set with finer granularity.

A computer simulation analysis method suitable for describing the mechanical behavior of multiphase composites based on the real microstructure of materials relates to a multidisciplinary field such as computational material science, simulation and high throughput calculation. Through the first-principles calculation under nano scale, the molecular dynamics simulation under micro scale, and the thermodynamic calculation under mesoscopic scale, various physical parameters needed for the finite element simulation under macro scale can be obtained, including the elastic and plastic physical parameters of each phase in the composite at different temperature and different grain sizes. Focused ion beam experiment and image processing are adopted to obtain real material microstructure. Through the parameter coupling and parameter transfer among the calculated results of various scales, combining the microstructure of the material, stress-strain relationship, stress distribution and its evolution law, plastic deformation and other mechanical behaviors of the multiphase composites under complex stress and different temperature can be simulated.

The invention provides a nanostructure design method based on deep learning, and the method comprises the steps: building a training data set which comprises any kind of nanostructure information; preprocessing the training data set to transpose and normalize the data in the training data set; constructing a spectrum prediction network SPN and a geometrical shape prediction network GPN; integrating the spectrum prediction network SPN and the geometrical shape prediction network GPN into a neural network model, wherein the output of the spectrum prediction network SPN is connected with the input of the geometrical shape prediction network GPN; training the integrated neural network model by using the preprocessed training data set, and finishing the training of the neural network model whenthe loss function reaches a preset value; and inputting the frequency spectrums and the material properties in the X and Y polarization directions corresponding to the to-be-established nanostructureinto the trained neural network model to obtain eight nanostructure characterization points of the nanostructure, thereby obtaining the geometrical shape of the nanostructure.

The invention discloses a method and device for analyzing bending deformation of a functionally gradient multilayer magnetoelectric elastic nano-plate. The method comprises the following steps: establishing a three-dimensional coordinate system; establishing a coupling constitutive relation formula, an expansion strain-displacement relation formula and a balance equation; calculating an extended displacement vector of a functionally graded magnetic-electric-elastic nano-plate under a simply supported boundary condition, sequentially substituting the extended displacement vector into the expansion strain-displacement relation formula, the coupling constitutive relation formula and the balance equation to obtain a linear intrinsic equation set, and finally determining a general solution formula to calculate a general solution; determining a general solution expression, then applying a force load and an electric load to the top surface, then calculating an expansion displacement vector and a stress vector at any depth of each layer, and finally, calculating all quantities and corresponding in-plane components. The method is helpful for revealing small-scale mechanical behaviors, and provides a theoretical basis for the design and application of the functionally graded magnetoelectric elastic nano-multilayer board and the miniaturized design and manufacturing of an engineering intelligent structure.

The invention provides a general force field rodlike nanoparticle coarse graining model and an establishment method thereof, and belongs to the technical field of nanometer chemical calculation. The model is divided into a central bead cluster and outer-layer cylindrical shell-shaped beads, the outermost-layer beads arranged at the two ends are hemispherical spherical shells, and the outermost-layer beads arranged at the middle section are cylindrical shell-shaped beads; two types of beads are defined in a used force field, the molar mass and potential action parameters are assigned, and a central bead cluster and the outermost beads are connected through keys, so that it is guaranteed that deformation and collapse do not occur during molecular dynamics simulation, and the overall property is kept unchanged; the whole coarse graining model only has the outermost layer of beads and the central bead cluster, so that the number of the beads is greatly reduced, the accuracy of molecular dynamics calculation can be ensured, meanwhile, the calculation amount is greatly reduced, the calculation speed is increased, and the waste of calculation power is avoided.

The invention discloses a characterization method and application of fluctuation introduced by single-particle irradiation. Threshold voltage distribution of a plurality of devices with different sizes before and after single-particle irradiation is tested and extracted to acquire threshold voltage fluctuation caused by single-particle irradiation. Then a process fluctuation model is corrected, and the circuit design margin requirement of work in the radiation environment is corrected. A calculation method is simple, an application range is wide, the application requirements of different technical generations and different radiation environments can be met, the circuit design margin requirement of working in the radiation environment is corrected, and the working reliability of the nanoscale integrated circuit in the radiation environment is improved.

The invention discloses a nano CMOS (Complementary Metal Oxide Semiconductor) circuit fault-tolerant mapping method capable of optimizing time delay, and aims to solve the problems of poor time delayperformance, poor solving speed, poor quality and the like in a nano CMOS circuit for realizing a correct logic function by adopting an existing fault-tolerant mapping method. Under the mapping constraints of defective nano CMOS circuit, the invention provides a nano CMOS circuit fault-tolerant mapping method capable of optimizing time delay. According to the fault-tolerant mapping method, the mapping process of a traditional nano CMOS circuit is optimized, the dividing technology of a logic level to a logic circuit to be mapped and the physical-level pre-planning technology with original input as an object are newly added, and the logic circuit is mapped by taking a path tree as a unit. Two mapping modes are adopted to be mapped into a pre-planned area in the nano CMOS circuit to optimizeeach path delay, the mapping success rate is improved by searching available defect units, and the delay performance of the delay mapping circuit is optimized on the basis of quickly eliminating theinfluence of defects on the logic function of the nano CMOS circuit.

The invention relates to a modeling scheme of a profile radiator, in particular to a parameterization rapid modeling method for a profile radiator with taper corrugated fins. Aiming at modeling of air-cooled fin radiators in medium and high power converter application occasions, particularly simulation modeling of tapered corrugated fin section bar radiators, a parameterized mathematical modeling method is adopted, and corrugated fin units with different radiuses and different sizes, fins with different tapers, different fin numbers and different substrate thicknesses are realized; according to the rapid modeling function of edge rib radiators with different fin offset distances and different sizes and shapes, a three-dimensional model can be automatically generated by software only by inputting key technical parameters, and the three-dimensional modeling and simulation efficiency is improved. The heat dissipation performance comparative analysis of the fin units in different shapes is facilitated, and the optimal structure scheme is determined.

The invention relates to an automatic positioning method for a critical heat production value of magnetic nanoparticles based on a simplex algorithm. After a simplex algorithm and a magnetic nanoparticle critical heat production technology are introduced, a finite element method is applied to solve the biological heat transfer mathematical model based on magnetic nanoparticle heat production, andthe magnetic nanoparticle critical heat production value is automatically positioned through algorithm optimization; on the basis, the application of the improved magnetic nanoparticle heat productiontheory effectively improves the prediction precision of the temperature distribution of the target geometric model. According to the invention, the optimal particle attribute of the magnetic nanoparticles during the magnetic hyperthermia period can be finally optimized so as to be applied to the magnetic nanometer hyperthermia instrument, and the magnetic hyperthermia effect of the magnetic nanometer hyperthermia instrument is improved.

The invention discloses a nanostructure extinction characteristic simulation method based on a moment method in combination with an adaptive cross approximation fast algorithm. According to the method, a moment method in an integral equation method is adopted for solving, and compared with a traditional numerical method such as a finite element method based on a differential equation method, dispersion errors do not exist, and the accuracy is high. According to the method, the extinction characteristic of the large-scale nanostructure can be solved by combining the adaptive cross approximation fast algorithm on the basis of the moment method, transplantation can be conveniently carried out under different background media, and the application range is wide.

The invention discloses a method for accurately regulating and controlling wettability of a double-layer two-dimensional material based on interlayer spacing, and belongs to the technical field of calculation of two-dimensional nano materials. Firstly, a numerical model of double-layer two-dimensional material wetting is established; secondly, establishing a wettability characterization theory based on the numerical model; the adhesion energy Wa between solid and liquid in a wetting system is calculated and substituted into a wetting theoretical formula, so that an expression of a contact angle of a wetting theoretical numerical model is established. And finally, through the steps, establishing an explicit relational expression of the contact angle theta and the interlayer spacing d for representing wettability. According to the method, the wettability can be accurately regulated and controlled based on the interlayer spacing of the two-dimensional material, the established theoretical model can accurately, quickly and effectively calculate the contact angle of the liquid on the surface of the corresponding material, the efficiency is greatly improved, a new strategy is provided for constructing the wettability-controllable material surface, and the method is suitable for popularization and application. And the problems of high cost, difficulty in operation, poor applicability and the like caused by experiments are avoided.

Within a layout of a first surface in a flip chip configuration, a bump restriction area is mapped according to a set of bump placement restrictions, wherein a first bump placement restriction specifies an allowed distance range between a bump and a qubit chip element in a layout of the first surface, and wherein a second bump placement restriction specifies an allowed distance range between the bump and a qubit chip element in a layout of a second surface in the flip chip configuration. An electrically conductive material is deposited outside the bump restriction area, to form the bump, wherein the bump comprises an electrically conductive structure that electrically couples a signal between the first surface and the second surface and is positioned according to the set of bump placement restrictions.

The invention provides a wafer detection data processing method and a computer readable storage medium, and relates to the technical field of wafer manufacturing. The wafer detection data processing method comprises the following steps: determining a new failure bit generated in a completed wafer detection process; obtaining a repair record of the new invalid bit and a repair record of an adjacent bit of the new invalid bit; the repair record is analyzed to determine attribute information of the new failure bit and the adjacent bit, and the attribute information comprises at least one of position information, standby circuit information, a unit graph of the new failure bit and a wafer detection process; performing classified learning according to the attribute information to obtain a failure bit prediction model; and predicting a failure bit to be subjected to wafer detection through the failure bit prediction model. According to the technical scheme, the reliability of failure bit prediction is improved, and the yield of wafer products can be improved.

The invention relates to a method and device for obtaining grating structure data and a readable storage medium, and the method comprises the steps: obtaining the first diffraction efficiency data of a to-be-detected grating, and enabling the first diffraction efficiency data to be obtained through detection; obtaining first grating structure data of the grating to be measured according to a target model and the first diffraction efficiency data; wherein the target model is a model obtained by adjusting model parameters of a set model according to second grating structure data and second diffraction efficiency data of a set grating, and the grating type of the to-be-measured grating is the same as that of the set grating. The second grating structure data and the second diffraction efficiency data are obtained through detection, and the diffraction efficiency data obtained according to the second grating structure data and the target model and the second diffraction efficiency data meet set matching requirements.

The invention relates to a recursive algorithm for the Mie coefficients of multilayer shell spherical nanoparticles based on the MCT, and belongs to the field of microwave photonics. By using the recursive algorithm of the Mie coefficients of the multilayer shell spherical nanoparticles based on the MCT, the Mie coefficients of the spherical nanoparticles with different shell layer numbers can beanalyzed. Firstly, by sorting, simplifying and popularizing the MCT, relative size parameters and corresponding Bezier equations required for calculating Mie coefficients of spherical nanoparticles with different shell layer numbers can be accurately found out through the recursive algorithm; meanwhile, in the calculation process, the recursive algorithm is also needed for calculating a key coefficient so as to obtain the Mie coefficient. Through the method, omission and error can be avoided, and the experimental result is accurate.

The invention discloses a rapid thermal model construction method based on a neural network. The method comprises the following steps: S1, establishing a thermal link model; s2, obtaining and preprocessing thermal model modeling sample data; s3, establishing and training a neural network kernel model; s4, performing rapid thermal model packaging and the like. According to the invention, the common problems that in the thermal evaluation process of a complex system, the number of grids is large, convergence is difficult, and consumed time is long are solved, and the thermal simulation efficiency can be greatly improved on the premise that the simulation progress is guaranteed.

The invention discloses a finite element model modeling method for an amplitude modulation decomposition structure, and belongs to the technical field of material science crystallography, and the method comprises the following steps: 1, simulating a diffusion process according to a diffusion Cashn-Hilliard equation, and constructing a phase field model; 2, reading and arranging data of a phase field model result; 3, creating a hexahedron finite element model and dividing grids; and 4, dividing units of the finite element model into two unit sets, namely a solid unit set and a hole unit set, through a result of the phase field model. According to the method provided by the invention, the finite element model subjected to the binary alloy amplitude modulation decomposition process can be created. The method can be used for researching finite element simulation of nano-porous materials such as nano-silver, nano-gold and nano-copper. And the damage and fracture micromechanisms under various working conditions are explored. Therefore, the nano-porous material can be better used.

The invention discloses a geometric modeling method for a microstructure of a carbon nanotube reinforced composite material. The method mainly comprises the following steps of: in a representative volume element, generating a plurality of random points in a series of local cylindrical coordinate systems, constructing a spline curve by using the points, calculating the nearest distance between the spline curve and the existing spline curve, and judging whether interference exists or not according to the diameter d of a carbon nanotube model and the thickness t of an interface phase; performing geometric cutting on the spline curve with interference, and reserving a non-interference part; and respectively constructing a curved cylinder with the diameter of d and a thin-walled cylinder geometric model with the difference between the outer diameter D and the inner diameter d of t by taking a spline curve as a sweeping path for representing the carbon nano tube and the interface phase thereof. Based on UG secondary development, the geometric modeling method which is simple, visual and easy to implement is provided, the curvature change of the carbon nanotube geometric model can be controlled by setting the variable range of the local cylindrical coordinate system, and a foundation is laid for follow-up research on the mechanical property of a carbon nanotube reinforced composite material.

The invention discloses a three-dimensional TSV based on carbon nanotubes and a parameter extraction method thereof. The three-dimensional TSV comprises a TSV structure arranged in a substrate and three signal pads arranged at the top of the TSV structure, and the TSV structure comprises a through hole formed in the substrate and multiple beams of carbon nanotubes uniformly filled in the through hole; and the three signal pads are laid at different positions of the top of the TSV structure at intervals and are respectively communicated with the multi-walled carbon nanotubes below the three signal pads so as to form three signal transmission channels on one TSV structure. According to the three-position TSV, one TSV structure can transmit three independent signals at the same time through the three signal pads, the number of I/O pins between an upper chip and a lower chip which are connected through the three-position TSV is doubled, the integration degree of the chips is improved, the process cost is reduced, and the three-position TSV has a better transmission characteristic.

The invention discloses a double-loss value network deep reinforcement learning KVFD model mechanical parameter global optimization method and system, and the method comprises the following steps: S1, inputting a pre-obtained nanoindentation measurement curve into a trained prediction value obtaining network, and obtaining a parameter prediction value of the nanoindentation measurement curve; and s2, performing iteration by taking the parameter prediction value as an iteration initial value of a deep reinforcement learning algorithm to obtain approximation of a global parameter solution of a pre-obtained nanoindentation measurement curve; and when the approximation of the global parameter solution reaches a preset convergence condition, outputting the approximation of the global parameter solution as a mechanical parameter of the KVFD model. According to the method, the parameter prediction value is introduced in iteration for parameter guidance, and the global optimal solution can be better approached.