CN113868879B - Simulation method and device of nano material device - Google Patents

Simulation method and device of nano material device Download PDF

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CN113868879B
CN113868879B CN202111164180.3A CN202111164180A CN113868879B CN 113868879 B CN113868879 B CN 113868879B CN 202111164180 A CN202111164180 A CN 202111164180A CN 113868879 B CN113868879 B CN 113868879B
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nanomaterial
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nano material
conductive path
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CN113868879A (en
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唐建石
李婷玉
李怡均
高滨
钱鹤
吴华强
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Tsinghua University
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Abstract

Embodiments of the present disclosure provide methods, apparatus, computer program products, and readable storage media for simulating nanomaterial devices. The method provided by the embodiment of the disclosure sets initialization parameters of a simulation model of a nanomaterial device according to parameters of the nanomaterial device and one-dimensional nanomaterial units, randomly generates a nanomaterial network with the initialization parameters on a two-dimensional rectangular plane, wherein the nanomaterial network comprises a plurality of one-dimensional nanomaterial units, and at least two of the one-dimensional nanomaterial units are intersected; and establishing a conductive network with the same topological structure as the nano material network, resolving the conductive path if the conductive path exists in the conductive network, and obtaining the electrical performance parameters of the simulation model of the nano material device, so as to accurately correspond to the relationship between each parameter of the nano material film and the electrical performance of the nano material device.

Description

Simulation method and device of nano material device
Technical Field
The present disclosure relates to the field of semiconductor technology, and more particularly, to methods, apparatus, computer program products, and readable storage media for simulating nanomaterial devices.
Background
With the continuous breakthrough of the physical limit of the semiconductor process, more Moore pushes the innovation and research in the aspects of device structure, channel material, manufacturing process and the like, the nano material becomes the alternative of integrated circuits to replace silicon material due to the excellent electrical property of the nano material, and the nano material device manufactured by the nano material has greater application prospect in the fields of flexible electronics, three-dimensional integration and the like.
The preparation of the nanometer film material has the key problems of difficult density improvement, difficult control of purity and uniformity and the like, and meanwhile, an accurate corresponding model is lacked between the film appearance and the electrical property of a nanometer material device, so that the design of a high-performance nanometer material device is greatly limited.
Therefore, an effective method for simulating a nanomaterial device is needed, so that the morphology of the nanomaterial film accurately corresponds to the electrical properties of the nanomaterial device, the system explores the relationship between each key parameter of the morphology of the nanomaterial film and the electrical properties of the nanomaterial device, and data support is provided for designing a high-performance nanomaterial device.
For carbon nanotube materials, in the prior art, on the basis of experiments, a numerical model is used to explain the performance of a Carbon Nanotube Transistor (CNT) and the influence of parameters such as channel length, CNT density and purity on the performance, however, the switching characteristics of the CNT are not studied, and a model that the characteristics of the CNT correspond to the electrical properties of a nanomaterial device is not established.
Disclosure of Invention
In order to solve the problems, the method converts the nanometer material film in the nanometer material device into the resistor network with the same topological structure by establishing a simulation model of the nanometer material device, and analyzes and settles the electrical parameters of the nanometer material device through kirchhoff's law and modified nodes, so as to accurately correspond the relationship between each parameter of the nanometer material film and the electrical performance of the nanometer material device and provide data support for the design of the high-performance nanometer material device.
The embodiment of the disclosure provides a simulation method, a simulation device, a computer program product and a readable storage medium of a nanometer material device.
The embodiment of the present disclosure provides a simulation method of a nanomaterial device, the nanomaterial device is prepared by a one-dimensional nanomaterial unit, and the simulation method includes: setting initialization parameters of a simulation model of the nanometer material device according to the parameters of the nanometer material device and the one-dimensional nanometer material unit; randomly generating a nanomaterial network with the initialization parameters on a two-dimensional rectangular plane according to the initialization parameters, wherein the nanomaterial network comprises a plurality of one-dimensional nanomaterial units, and at least two one-dimensional nanomaterial units in the plurality of one-dimensional nanomaterial units are intersected; establishing a conductive network with the same topological structure as the nano material network, and judging whether a conductive path exists in the conductive network; and under the condition that the conductive network has a conductive path, resolving the conductive path to obtain the electrical performance parameters of the simulation model of the nano material device.
According to an embodiment of the present disclosure, the initialization parameter includes at least one of: the size of the two-dimensional rectangular plane, the length of the one-dimensional nano material, the on-state resistance value and the off-state resistance value of the intersection point of the one-dimensional nano material, the density of the one-dimensional nano material and the purity of the one-dimensional nano material; the electrical performance parameter includes at least one of: the on-state current and the off-state current of the simulation model of the nano material device, the ratio of the on-state current to the off-state current of the simulation model of the nano material device, and the on-state current density and the off-state current density of the simulation model of the nano material device.
According to the embodiment of the disclosure, according to the initialization parameters, a nano material network with the initialization parameters is randomly generated on a two-dimensional rectangular plane for multiple times, and the electrical performance parameters of a simulation model of the nano material device are obtained for multiple times; the simulation method further comprises the following steps: and performing statistical analysis on the electrical performance parameters of the simulation model of the nano material device obtained for multiple times to determine the distribution characteristics of the electrical performance parameters of the simulation model of the nano material device.
According to an embodiment of the present disclosure, the establishing a conductive network having the same topology as the nanomaterial network comprises: determining the intersection condition of the plurality of one-dimensional nanomaterial units in the nanomaterial network; for each intersection point in the nano material network, setting the intersection point as one edge in the conductive network, and determining the intersection point resistance of the intersection point as the resistance on the edge; for the part of each one-dimensional nanomaterial cell located between two adjacent intersections, it is set as a node in the conductive network.
According to an embodiment of the present disclosure, determining the intersection of the plurality of one-dimensional nanomaterial units in the nanomaterial network comprises: optionally selecting one-dimensional nano material unit in the nano material network as a target one-dimensional nano material unit, setting a rectangular search area by taking the target one-dimensional nano material unit as a center, and searching whether other one-dimensional nano material units are included in the rectangular search area; under the condition that other one-dimensional nano-material units exist, judging whether the target one-dimensional nano-material unit is intersected with other one-dimensional nano-material units in the rectangular search area or not, and under the condition that the target one-dimensional nano-material unit is intersected with the other one-dimensional nano-material units in the rectangular search area, storing intersection information of the target one-dimensional nano-material unit and the one-dimensional nano-material units; traversing all the one-dimensional nano-material units in the nano-material network to obtain the intersection information of all the one-dimensional nano-material units.
According to an embodiment of the present disclosure, the nanomaterial network comprises a plurality of sub-nanomaterial networks, each sub-nanomaterial network comprises a plurality of the one-dimensional nanomaterial units, and there is an intersection between at least two of the one-dimensional nanomaterial units in the plurality of one-dimensional nanomaterial units. Wherein determining whether a conductive path exists in the conductive network comprises: applying a source voltage and a drain voltage to the nanomaterial network, determining that a conductive path exists in the conductive network if the nanomaterial network obtains the source voltage and the drain voltage; and extracting the topological structure of the nano material network, and generating a conductive network corresponding to the sub-nano material network as a conductive path according to the topological structure.
According to the embodiment of the disclosure, the conductive path includes n nodes, where n is a positive integer greater than 3, and the calculating the conductive path to obtain the electrical performance parameters of the simulation model of the nanomaterial device includes: applying a gate voltage, a drain voltage and a source voltage to the conductive path, the drain being set to node 0, the source being set to node 1 of the conductive path, the remaining nodes in the conductive path being set to node 2 to node n-1, respectively, wherein node 0 of the conductive path is set to a zero potential point; the potential and source current of each node of the conductive path are calculated using the following equations:
Figure BDA0003291143880000031
wherein, sigma g i ＝g 1i +g 2i +…+g ni
Wherein u is i Is the potential of a conductive path node I, I being an integer greater than 0, I 1 Is a source current, V ds Is the voltage difference between source and drain, sigma g i Is the total conductance, g, from conductive path node i to the remaining conductive path nodes ij The conductance of node i to node j, which is the conductive path.
According to an embodiment of the disclosure, calculating the conductive path to obtain the electrical performance parameters of the simulation model of the nanomaterial device includes: under the condition that the grid voltage is greater than the threshold voltage, the simulation model of the nanometer material device is conducted, the resistance value of the intersection point resistor in the nanometer material network is an on-state resistance value, and the obtained source-level current is an on-state current; under the condition that the grid voltage is smaller than the threshold voltage, the simulation model of the nanometer material device is cut off, the resistance value of the intersection point resistor in the nanometer material network is the off-state resistance value, and the obtained source-level current is the off-state current;
the on-state current density is calculated using the following equation:
J on ＝I on /W
wherein, J on Is an on-state current density, I on W is the width of a two-dimensional rectangular plane of a simulation model of the nano material device;
the off-state current density is calculated using the following equation:
J off ＝I off /W
wherein, J off Is an off-state current density, I off W is the width of a two-dimensional rectangular plane of a simulation model of the nano material device;
calculating the ratio K of the on-state current to the off-state current of the simulation model of the nano material device by using the following equation:
K＝J on /J off 。
an embodiment of the present disclosure also provides a simulation apparatus of a nanomaterial device, including: one or more processors; and one or more memories having stored therein a computer-executable program that, when executed by the processor, performs the method of any of the embodiments of the present disclosure.
Embodiments of the present disclosure also provide a computer program product comprising computer software code for implementing the method of any one of the embodiments of the present disclosure when executed by a processor.
Embodiments of the present disclosure also provide a computer-readable storage medium having stored thereon computer-executable instructions for implementing the method of any one of the embodiments of the present disclosure when executed by a processor.
Embodiments of the present disclosure provide methods, apparatus, computer program products, and readable storage media for simulating nanomaterial devices.
The method provided by the embodiment of the disclosure sets initialization parameters of a simulation model of a nanomaterial device according to parameters of the nanomaterial device and one-dimensional nanomaterial units, randomly generates a nanomaterial network with the initialization parameters on a two-dimensional rectangular plane, wherein the nanomaterial network comprises a plurality of one-dimensional nanomaterial units, and at least two one-dimensional nanomaterial units are intersected; and establishing a conductive network with the same topological structure as the nano material network, resolving the conductive path if the conductive path exists in the conductive network, and obtaining the electrical performance parameters of the simulation model of the nano material device, so as to accurately correspond to the relationship between each parameter of the nano material film and the electrical performance of the nano material device.
By the method, the relation between each parameter of the nano material film and the electrical property of the nano material device can be accurately corresponded, the numerical simulation of the nano material device layer is realized, the performance of the nano material device is predicted, and data support is provided for the design of the high-performance nano material device.
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In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings used in the description of the embodiments will be briefly introduced below. It is apparent that the drawings in the following description are only exemplary embodiments of the disclosure, and that other drawings may be derived from those drawings by a person of ordinary skill in the art without inventive effort.
FIG. 1 illustrates a flow diagram of a method 100 for creating a nanomaterial device simulation model in accordance with an embodiment of the present disclosure;
FIG. 2 shows a diagram of a nanomaterial network generating conductive pathways according to its topology;
FIG. 3 illustrates a flow diagram of a method 300 for establishing a conductive network having the same topology as the nanomaterial network, in accordance with an embodiment of the present disclosure;
FIG. 4a shows a first schematic diagram of a nanomaterial network, for example carbon nanotubes;
FIG. 4b shows a second schematic diagram of a nanomaterial network, using carbon nanotubes as an example;
FIG. 4c shows a schematic diagram of a pure resistive structure of a conductive network having the same topology as the nanomaterial network exemplified by carbon nanotubes;
FIG. 5 illustrates a flow diagram of a method 500 for determining an intersection of the plurality of one-dimensional nanomaterial units in the nanomaterial network in accordance with an embodiment of the present disclosure;
FIG. 6 shows a schematic of a KD-Tree rectangular search;
FIG. 7 is a diagram showing the distribution of nanomaterial potential and nanomaterial cross-point current in a nanomaterial network;
FIG. 8a is a first diagram illustrating a comparison of simulation results and experimental results for a thin film transistor device using carbon nanotubes as an example;
fig. 8b is a diagram illustrating a comparison between simulation results and experimental results of a thin film transistor device using a carbon nanotube as an example;
FIG. 9 shows a schematic diagram of an emulation apparatus 2000 of a nanomaterial device in accordance with an embodiment of the present disclosure;
FIG. 10 shows a schematic diagram of an architecture of an exemplary computing device, according to an embodiment of the present disclosure;
FIG. 11 shows a schematic diagram of a storage medium according to an embodiment of the present disclosure.
Detailed Description
In order to make the objects, technical solutions, and advantages of the present disclosure more apparent, example embodiments according to the present disclosure will be described in detail below with reference to the accompanying drawings. It should be understood that the described embodiments are only some of the embodiments of the present disclosure, and not all of the embodiments of the present disclosure, and it is to be understood that the present disclosure is not limited by the example embodiments described herein.
Further, in the present specification and the drawings, steps and elements having substantially the same or similar characteristics are denoted by the same or similar reference numerals, and repeated description of the steps and elements will be omitted.
Further, in the present specification and drawings, if flowcharts are used to illustrate steps of methods according to embodiments of the present disclosure, it should be understood that the preceding or following steps are not necessarily performed in the exact order. Rather, various steps may be processed in reverse order or concurrently, unless explicitly limited by the embodiments of the disclosure. Meanwhile, other operations may be added to or removed from these processes.
Furthermore, in the description and drawings, unless explicitly stated otherwise, the terms "connected" or "coupled" and the like are not restricted to physical or mechanical connections, but may include electrical connections, whether direct or indirect.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. The terminology used herein is for the purpose of describing embodiments of the invention only and is not intended to be limiting of the invention.
To facilitate the description of the present disclosure, concepts related to the present disclosure are introduced below.
The nano material device is an electronic device with nano scale and specific function, which is designed and prepared by utilizing nano processing and preparation technology, such as photoetching, epitaxy, micro processing, self-assembly growth, molecular synthesis technology and the like. Currently, many nanoelectronic devices have been developed by using nanoelectronic materials and nanolithography, such as an electron resonance tunneling device resonant diode, a three-pole resonance tunneling transistor, a single-electron transistor, a metal-based, a semiconductor, a nanoparticle, a single-electron electrometer, a single-electron memory, a single-electron logic circuit, a metal-based single-electron transistor memory, a semiconductor memory, a memory manufactured from silicon nanocrystals, a nano floating gate memory, a nano silicon microcrystalline thin film device, and a polymer electronic device.
The nano material device attracts people's interest in the aspects of basic theory and technical application, and is a bridge between molecular physics and solid physics, and the density of the device which can be achieved in the future is much higher than that of the device which can be achieved by the traditional semiconductor technology.
The nano material device disclosed by the invention is a one-dimensional nano material thin film device, which is prepared from a one-dimensional nano material unit, wherein the one-dimensional nano material is a material with one of three dimensions of which the size is not between 0.1 and 100nm, for example, silicon carbide (SiC) nanowires and Carbon Nano Tubes (CNT) are all one-dimensional nano materials. The electrical performance of the one-dimensional nanomaterial thin film device is influenced by the seepage conduction of the thin film network and essentially depends on the properties and distribution of the one-dimensional nanomaterial and the size parameters of the one-dimensional nanomaterial thin film device, for example, the resistivity of the thin film formed by the CNT depends on the size of the thin film, the density of the thin film, the length of the CNT, the purity of the CNT and the distribution uniformity of the CNT. In order to study the physical mechanism of the nano material device more systematically and to explore the relationship between each key parameter of the nano material film appearance and the electrical property of the nano material device, a simulation technology is required.
The embodiment of the disclosure provides a simulation method of a nano material device, wherein a nano material device model is established, the performance distribution characteristics of the one-dimensional nano material device can be obtained according to the relation between each parameter of a nano material film and the electrical performance of the nano material device, the performance simulation and performance prediction are carried out on the one-dimensional nano material device, and data support is provided for designing a high-performance nano material device.
Embodiments of the present disclosure will be further described with reference to the accompanying drawings.
FIG. 1 shows a flow diagram of a method 100 for building a nanomaterial device simulation model in accordance with an embodiment of the present disclosure.
According to an embodiment of the present disclosure, the nanomaterial device is fabricated from a one-dimensional nanomaterial cell.
As shown in fig. 1, in step S101, initialization parameters of a simulation model of the nanomaterial device are set according to parameters of the nanomaterial device and the one-dimensional nanomaterial unit.
Alternatively, the nanomaterial device may be a CNT thin film transistor and the one-dimensional nanomaterial cell may be a CNT. Optionally, the parameters of the nanomaterial device and the one-dimensional nanomaterial unit may be parameters extracted by characterizing a macro morphology of the nanomaterial device by an Atomic Force Microscope (AFM) or a Scanning Electron Microscope (SEM).
Optionally, the extracted parameters of the nanomaterial device and the one-dimensional nanomaterial unit may include a size of the nanomaterial device, a length of the one-dimensional nanomaterial, an on-state resistance and an off-state resistance of an intersection of the one-dimensional nanomaterial, a density of the one-dimensional nanomaterial, a purity of the one-dimensional nanomaterial, a threshold voltage of the nanomaterial device, a relationship between a resistance and a length of the one-dimensional nanomaterial, and a distribution of the one-dimensional nanomaterial in the nanomaterial device, where the size of the nanomaterial device is configured to a size of the two-dimensional rectangular plane of a simulation model of the nanomaterial device.
In step S102, a nanomaterial network having the initialization parameter is randomly generated on a two-dimensional rectangular plane according to the initialization parameter, wherein the nanomaterial network includes a plurality of one-dimensional nanomaterial units, and an intersection exists between at least two one-dimensional nanomaterial units in the plurality of one-dimensional nanomaterial units.
Optionally, a Python or other simulation tools are used to establish the two-dimensional rectangular plane, simulate a planar structure of the nanomaterial device, generate a plurality of one-dimensional nanomaterial units with the initialization parameter on the two-dimensional rectangular plane in a random distribution, determine an intersection condition of the plurality of one-dimensional nanomaterial units in the nanomaterial network, and generate the nanomaterial network by connecting the plurality of intersected one-dimensional nanomaterial units with the initialization parameter.
It should be understood that, when the simulation model of the nanomaterial device is established, the nanomaterial network with the initialization parameter generated on the two-dimensional rectangular plane is randomly generated, and in the actual nanomaterial device manufacturing process, the one-dimensional nanomaterials in the nanomaterial device are also randomly distributed, so that the nanomaterial network with the initialization parameter randomly generated on the two-dimensional rectangular plane can simulate the internal structure of the nanomaterial device. The electrical performance parameters of the simulation model of the nano material device are obtained for multiple times by randomly generating the nano material network with the initialization parameters for multiple times, and the performance of the nano material device can be accurately known by carrying out statistical analysis on the electrical performance parameters obtained for multiple times.
In step S103, a conductive network having the same topology as the nanomaterial network is established, and it is determined whether a conductive path exists in the conductive network.
Selecting the target one-dimensional nano-material unit in the nano-material network, searching the one-dimensional nano-material units included in a certain rectangular range with the target one-dimensional nano-material unit as the center by utilizing rectangular search, judging whether the one-dimensional nano-material units included in the rectangular range are intersected and storing, traversing the nano-material network, and obtaining the intersection information of all the one-dimensional nano-material units.
For example, there may be multiple sub-nanomaterial networks in the nanomaterial network, each sub-nanomaterial network being independent of each other, and there being no conductive path between any two sub-nanomaterial networks. And judging whether each sub one-dimensional nano-material unit obtains a source voltage and a drain voltage or not by applying the source voltage and the drain voltage to the nano-material network. For a sub-nanomaterial network capable of obtaining a source voltage and a drain voltage, it may be determined that a conductive path exists in the sub-nanomaterial network and a topology of the sub-nanomaterial network is determined, and a conductive network having the same topology as the sub-nanomaterial network is used as the conductive path. For sub-nanomaterial networks where only a source voltage can be obtained, only a drain voltage can be obtained, or source and drain voltages cannot be obtained, it can be determined that the sub-nanomaterial networks do not have a conductive path.
Fig. 2 shows a schematic diagram of a carbon nanotube network generating conductive paths according to its topology.
As shown in fig. 2, (a) shows a schematic diagram of a carbon nanotube network on a two-dimensional rectangular plane with dimensions of 3 μm × 3 μm, with different colors being used to distinguish different daughter carbon nanotube networks. The carbon nanotubes with the same color are connected to form a sub-carbon nanotube network, and the carbon nanotubes in the same sub-carbon nanotube network are mutually conducted, namely the carbon nanotubes with the same color are mutually conducted. The milky white carbon nanotube represents a carbon nanotube participating in electric conduction, the colored carbon nanotube represents a carbon nanotube not participating in electric conduction, a sub-carbon nanotube network consisting of the milky white carbon nanotube is a conductive path, and the sub-carbon nanotube network consisting of the colored carbon nanotube does not have the conductive path.
(b) Showing the conducting path with the same structure as the sub-carbon nanotube network formed by the milky white carbon nanotubes in (a), and eliminating the non-conducting sub-carbon nanotube network in the carbon nanotube network. The electrical properties of the carbon nanotube network are determined by the carbon nanotubes in the conductive paths, and model calculations are also performed for the carbon nanotube conductive network. In step S104, under the condition that a conductive path exists in the conductive network, the conductive path is calculated to obtain the electrical performance parameters of the simulation model of the nanomaterial device.
Based on the above, the present disclosure provides a simulation method of a nanomaterial device, which includes establishing a simulation model of the nanomaterial device, converting a nanomaterial network in the nanomaterial device into a pure resistance conductive network with the same topological structure, and resolving a conductive path in the conductive network to obtain electrical performance parameters of the simulation model of the nanomaterial device, thereby corresponding to the relationship between each parameter of a nanomaterial film and the electrical performance of the nanomaterial device, realizing numerical simulation at the nanomaterial device level, predicting the nanomaterial device performance, and providing data support for the design of a high-performance nanomaterial device.
According to an embodiment of the present disclosure, the initialization parameters of the simulation model of the nanomaterial device include at least one of the following parameters of the nanomaterial device and the one-dimensional nanomaterial unit: the size of the two-dimensional rectangular plane, the length of the one-dimensional nano material, the on-state resistance value and the off-state resistance value of the intersection point of the one-dimensional nano material, the density of the one-dimensional nano material and the purity of the one-dimensional nano material.
The size of the two-dimensional rectangular plane determines the substrate size of the simulation model of the nanomaterial device, the width W of the two-dimensional rectangular plane is set as the channel width of the nanomaterial device, the length L of the two-dimensional rectangular plane is set as the channel length of the nanomaterial device, the length of the one-dimensional nanomaterial influences the self resistance value of the one-dimensional nanomaterial, and the longer the length of the one-dimensional nanomaterial is, the larger the self resistance value of the one-dimensional nanomaterial is, the larger the resistance of the simulation model of the nanomaterial device is; the density of the one-dimensional nano material influences the on-state current and the off-state current of the nano material device, and the larger the density of the one-dimensional nano material is, the denser the film form of a simulation model of the nano material device is, and the larger the on-state current and the off-state current are; the purity of the one-dimensional nano material influences the ratio of the on-state current to the off-state current of the simulation model of the nano material device, and the higher the purity of the semiconductor one-dimensional nano material is, the larger the ratio of the on-state current to the off-state current of the simulation model of the nano material device is.
The electrical performance parameter includes at least one of: the on-state current and the off-state current of the simulation model of the nano material device, the ratio of the on-state current to the off-state current of the simulation model of the nano material device, and the on-state current density and the off-state current density of the simulation model of the nano material device.
According to the embodiment of the disclosure, according to the initialization parameters, a nano material network with the initialization parameters is randomly generated on a two-dimensional rectangular plane for multiple times, and the electrical performance parameters of a simulation model of the nano material device are obtained for multiple times; the simulation method further comprises the following steps: and carrying out statistical analysis on the electrical performance parameters of the simulation model of the nano material device obtained for multiple times so as to determine the distribution characteristics of the electrical performance parameters.
Based on the above, the present disclosure provides a simulation model electrical property parameter analysis result of a large number of nanomaterial devices obtained by performing electrical property parameter analysis on the simulation model of the nanomaterial device for multiple times, wherein the simulation model electrical property parameter analysis result of the large number of nanomaterial devices obeys normal distribution, a normal distribution curve has centrality, and a peak of the normal distribution curve is located at the midpoint, i.e., a position where a mean is located, so that a mean value of the simulation model electrical property parameter analysis result of the nanomaterial device can be obtained through the normal distribution curve, a property distribution characteristic of the nanomaterial device is determined, and data support is provided for designing a high-performance nanomaterial device.
Fig. 3 shows a flow diagram of a method 300 for establishing a conductive network having the same topology as the nanomaterial network, according to an embodiment of the disclosure.
As shown in fig. 3, in step S301, the intersection of the plurality of one-dimensional nanomaterial units in the nanomaterial network is determined.
Optionally, every two one-dimensional nanomaterial units in the nanomaterial network intersect to generate an intersection, and when the plurality of one-dimensional nanomaterial units intersect, each one-dimensional nanomaterial unit may simultaneously include a plurality of intersections.
For each intersection point in the one-dimensional nano material network, corresponding the intersection point to an edge (edge) in the conductive network, and determining the resistance of the intersection point as the weight of the edge (edge); for each one-dimensional nanomaterial unit, it is set as a node in the conductive network.
The graph theory takes a graph as a research object, wherein the graph in the graph theory is a graph formed by a plurality of given points and lines connecting the two points, the graph is generally used for describing a certain specific relationship between certain objects, the points represent the objects, and the lines connecting the two points represent the relationship between the corresponding two objects.
According to the intersection point condition in the nano material network and the information of the one-dimensional nano material units among the intersection points, the one-dimensional nano material units in the nano material network are represented by points in a graph theory through the graph theory, the intersection point relation of the one-dimensional nano material units is represented by edges, namely the intersection points in the nano material network are set as the edges in the graph theory, and the intersection point resistance of the intersection points is determined as the weight of the edges.
For example, as shown in fig. 4a, taking carbon nanotubes as an example, a carbon nanotube network includes 3 carbon nanotubes, and a source voltage and a drain voltage are applied to the carbon nanotube network to generate 5 intersections, wherein 3 intersections are generated by the carbon nanotubes and intersecting two by two, 1 intersection is generated by the carbon nanotube and the source, 1 intersection is generated by the carbon nanotube and the drain, and 5 intersections respectively have an intersection resistance R 02 、R 12 、R 23 、R 24 、R 34 Then these 5 intersections are set as 5 edges of the conductive network.
As shown in FIG. 4b, 5 intersections in the carbon nanotube network are used as 5 edges of the conductive network, and the resistances are respectively intersection resistances, i.e. R 02 、R 12 、R 23 、R 24 、R 34 。
Optionally, the intersection point resistance of the intersection point includes a junction resistance generated by intersection of the one-dimensional nanomaterial unit and the one-dimensional nanomaterial unit.
The one-dimensional nano-material units in the nano-material network are provided with self-resistors, the one-dimensional nano-material units are intersected to generate junction resistors, the resistance values of the self-resistors of the one-dimensional nano-material units and the resistance values of the junction resistors of the one-dimensional nano-material units are mainly determined by the types of the one-dimensional nano-material units, the resistance values of the junction resistors of the one-dimensional nano-material units are generally more than two orders of magnitude of the resistance values of the self-resistors of the one-dimensional nano-material units, and therefore the intersection point resistors are mainly determined by the sizes of the junction resistors generated by the intersection of the one-dimensional nano-material units.
For example, if two kinds of carbon nanotubes, namely, metallic carbon nanotubes (m-CNTs) and semiconducting carbon nanotubes (s-CNTs), are present in the carbon nanotube network, and the metallic carbon nanotubes (m-CNTs) and the semiconducting carbon nanotubes (s-CNTs) intersect with each other, the following five resistances are present in the carbon nanotube network: the resistance of the intersection point resistance can also be changed when the type of the carbon nano tube is changed.
When a grid voltage is applied to the carbon nano tube network, the resistance values of the five resistors are changed in a switching state along with the change of the grid voltage. When the grid voltage is increased to be higher than the threshold voltage, the resistance values of the five resistors are suddenly changed from large to small, the resistance values of the five resistors are all in an on-state, and the conductivity of the carbon nano tube network is improved; when the gate voltage is reduced to be lower than the threshold voltage, the resistance values of the five resistors are suddenly increased from small to large, and all the five resistors present off-state resistance values, so that the conductivity of the carbon nanotube network is reduced. The theoretical values of the resistances of the carbon nanotube networks are shown in the following table.
Figure BDA0003291143880000121
Optionally, the on-state resistance value and the off-state resistance value of the intersection point are set when setting the initialization parameter of the simulation model of the nanomaterial device.
The five types of resistors can be greatly changed along with different deposition experimental conditions of the nano material device, for example, whether the carbon nano tube is wrapped by organic matters or not can affect the resistance values of the on-state resistor and the off-state resistor, so that the resistance values of the on-state resistor and the off-state resistor cannot be set completely according to theoretical values. The theoretical values need to be corrected by using a regression algorithm according to the experimental results, and finally, various resistance parameters in the simulation model of the nano material device are determined as shown in the table below.
Figure BDA0003291143880000122
In step S303, for the portion of each one-dimensional nanomaterial cell located between two adjacent intersections, it is set as one node in the conductive network.
According to the intersection point condition in the nano material network and the information of the one-dimensional nano material units among the intersection points, the one-dimensional nano material units in the nano material network are represented by points in a graph theory through the graph theory, the intersection point relation of the one-dimensional nano material units is represented by edges, namely the intersection points in the nano material network are set as the edges in the graph theory, and the intersection point resistance of the intersection points is determined as the weight of the edges.
For example, as shown in FIG. 4b, 5 intersections in the carbon nanotube network are 5 sides of the conductive network, and the resistances are the intersection resistances, i.e., R 02 、R 12 、R 23 、R 24 、R 34 With the carbon nanotubes between two adjacent intersections set as nodes in the conductive network, as shown in FIG. 4c, the intersection resistance R 02 Both ends are nodes N 0 And node N 2 Resistance R at the intersection 12 Both ends are nodes N 1 And node N 2 Resistance R at the intersection 23 Both ends are nodes N 2 And node N 3 Node resistance R 24 Both ends are node N 2 And node N 4 Node resistance R 34 Both ends are nodes N 3 And node N 4 。
Optionally, the nodes in the conductive network are considered to be in a superconducting state.
Based on the above, in the present disclosure, by establishing the conductive network having the same topological structure as the nanomaterial network, the nanomaterial network is converted into a pure resistive network structure, so that the electric potential at two ends of each one-dimensional nanomaterial in the nanomaterial network and the magnitude of the current passing through each intersection in the nanomaterial network can be calculated conveniently.
Fig. 4a and 4b show schematic diagrams of two nanomaterial networks.
Fig. 4c shows a pure resistive structure of the conductive network with the same topology as the nanomaterial network.
As shown in fig. 4a-4c, the nanomaterial network is a carbon nanotube network, the carbon nanotube network comprises 3 carbon nanotubes intersected with each other to generate 3 intersections, a source voltage and a drain voltage are applied to the carbon nanotube network to generate 2 intersections, and a total of 5 intersections are generated, wherein each of the 5 intersections has an intersection resistance R 02 、R 12 、R 23 、R 24 、R 34 . When the nanomaterial network is converted into a conductive network, 5 intersections are set as 5 edges of the pure resistive network, and the intersection resistance is determined as the resistance on the edge. The carbon nano tube positioned between two adjacent intersection points is set as a corresponding node N 0 -N 4 . If there are a greater number of carbon nanotubes in the carbon nanotube network, each carbon nanotube can intersect any number of carbon nanotubes, with multiple intersections.
Fig. 5 illustrates a flow diagram of a method 500 for determining an intersection of the plurality of one-dimensional nanomaterial units in the nanomaterial network in accordance with an embodiment of the present disclosure.
As shown in fig. 5, in step S501, one of the one-dimensional nanomaterial units is selected as a target one-dimensional nanomaterial unit, a rectangular search area is set with the target one-dimensional nanomaterial unit as a center, and whether another one-dimensional nanomaterial unit exists in the rectangular search area is searched.
Optionally, a KD-Tree algorithm is used to search whether the rectangular search area has the other one-dimensional nanomaterial unit, and the other one-dimensional nanomaterial unit may intersect with the target one-dimensional nanomaterial unit or may not intersect with the target one-dimensional nanomaterial unit.
KD-Tree is an abbreviation for K-Dimensional Tree, a Tree-like data structure that stores instance points in a K-Dimensional space for fast retrieval. The method is mainly applied to searching multidimensional space key data, such as: range search and nearest neighbor search.
The rectangular search schematic diagram of the KD-Tree is shown in fig. 6, wherein a rectangular area in the rectangular area represents a search area, 5 nearby carbon nanotubes except for a target carbon nanotube in the search area can be efficiently obtained through KD-Tree search, and the nearby carbon nanotubes are located in a closer range of the same layer or an upper layer and a lower layer in a data structure of the KD-Tree. And (4) finding the nearby carbon nano tube by using a KD-Tree rectangular search method, then performing intersection judgment, and storing the contacted carbon nano tube information.
In step S502, in the case that there are other one-dimensional nanomaterial units, it is determined whether there is an intersection between the target one-dimensional nanomaterial unit and the other one-dimensional nanomaterial units, and in the case that there is an intersection, information of the one-dimensional nanomaterial unit that intersects the target one-dimensional nanomaterial unit is stored.
In step S503, all the one-dimensional nanomaterial units in the nanomaterial network are traversed to obtain intersection information of all the one-dimensional nanomaterial units.
Optionally, traversing all the one-dimensional nanomaterial units in the nanomaterial network by using a spatial function in a Python open source library.
Based on the above, the method and the device provided by the disclosure utilize KD-Tree rectangular search to determine the intersection condition of the multiple one-dimensional nanomaterial units in the nanomaterial network, ensure that the intersection condition of all the one-dimensional nanomaterial units in the nanomaterial network is judged, and avoid missing the possible intersection points of the one-dimensional nanomaterial intersection.
According to the embodiment of the present disclosure, the nanomaterial network may include a plurality of sub nanomaterial networks, each sub nanomaterial network includes a plurality of the one-dimensional nanomaterial units, and there is an intersection between at least two of the one-dimensional nanomaterial units in the plurality of one-dimensional nanomaterial units; determining whether a conductive path exists in the conductive network comprises: applying a source voltage and a drain voltage to the nanomaterial network, determining that a conductive path exists in the conductive network if the sub-nanomaterial network is connectable to a source drain; and extracting the topological structure of the sub-nano material network, and generating a conductive network corresponding to the sub-nano material network as a conductive path according to the topological structure.
Optionally, the dixotera algorithm is adopted to divide the nanomaterial network into a plurality of sub-nanomaterial networks, the plurality of sub-nanomaterial networks are randomly distributed in the nanomaterial network, and at least one sub-nanomaterial network can generate the conductive path corresponding to the sub-nanomaterial network according to the topology structure of the sub-nanomaterial network.
For example, if the nanomaterial network includes 5 sub-nanomaterial networks, and a source voltage and a drain voltage are applied to the nanomaterial network, where 1 sub-nanomaterial network can connect the source and the drain, and 4 sub-nanomaterial networks cannot connect the source and the drain, then the 1 sub-nanomaterial network that can connect the source and the drain is a conductive path.
Based on the above, the present disclosure determines whether a conductive path exists in the conductive network, and generates the conductive path, where the electrical performance parameter of the simulation model of the nanomaterial device depends on the calculation of the conductive path, thereby eliminating the influence of paths in the conductive network that cannot conduct electricity.
According to a more detailed embodiment of the simulation method of the present disclosure, the conductive path includes n nodes, n being a positive integer greater than 3. Applying a gate voltage V to the conductive path g Drain voltage V s And a source voltage V d The drain electrode is set as a node 0 of the conductive path, the source electrode is set as a node 1 of the conductive path, and the rest nodes in the conductive path are respectively set as a node 2 to a node n-1, wherein the node 0 of the conductive path is set as a zero potential point, and the rest nodes of the conductive path have a potential i relative to the zero potential point i 。
According to kirchhoff's current law, saidAt any node in the conductive path, at any one time, the sum of the currents flowing into the node is equal to the sum of the currents flowing out of the node, the direction of the current flowing out of any node is set to be positive, and the current I passing through the node I can be calculated by using the following equation i ：
I i ＝u 1 ·g 1i +u 2 ·g 2i +…+u n ·g ni -u i ·(g 1i +g 2i +…+g ni )
Wherein, I i Is the current flowing through node i of the conductive path, g ij Is the conductance, u, of node i to node j of said conductive path i Is the potential of node i of the conductive path.
The larger the conductance from the node i to the node j of the conductive path is, the smaller the resistance from the node i to the node j is, and the larger the current from the node i to the node j is.
Applying kirchhoff's law to the nodes except node 0 in the conductive path, the solving equations of the node voltage and the source current except node 0 in the conductive path can be listed as follows:
Figure BDA0003291143880000151
wherein, sigma g i ＝g 1i +g 2i +…+g ni
Wherein u is i Is the potential of a node I of said conductive path, I being an integer greater than 0, I 1 Is a source current, V ds For the voltage difference between the source and the drain, Σ g i Is the total conductance, g, from conductive path node i to the remaining conductive path nodes ij The conductance of node i to node j, which is the conductive path.
And under the condition that the simulation model of the nano material device is switched on, the source level current is the on-state current, and under the condition that the simulation model of the nano material device is switched off, the source level current is the off-state current.
Optionally, after the electric potential of each node in the conductive path is obtained, according to ohm's law, the current between nodes of the conductive path may be calculated by using the following equation:
u i -u j ＝I ij ·R ij
wherein u is i And u j Potentials of node I and node j of the conductive path, I ij And R ij Respectively the current and the resistance between the node i and the node j of the conductive path.
Based on the above, the method provided by the disclosure is based on kirchhoff's law, the conductive path is solved to obtain the potential of each node in the conductive path, the current between each node and the source current, the distribution of the potentials of all the nodes in the conductive path and the current between the nodes can be determined, and the electrical performance condition inside the simulation model of the nanomaterial device can be further simulated. FIG. 7 is a diagram showing the distribution of the nanomaterial potential and nanomaterial cross-point current in the nanomaterial network.
As shown in fig. 7, (a) shows the nanomaterial network, (b) shows the potential distribution of each nanomaterial in the nanomaterial network, and (c) shows the distribution of current flowing through each nanomaterial intersection in the nanomaterial network, in (b) and (c), the darker the color indicates the potential of each nanomaterial in the nanomaterial network and the magnitude of current flowing through each nanomaterial intersection in the nanomaterial network, and the darker the color indicates the larger the corresponding potential or current, and the lighter the color indicates the smaller the corresponding potential or current. The electrical characteristics of the nano material network can be visually observed through the potential distribution diagram of each nano material in the nano material network and the distribution diagram of the current flowing through the intersection point of each nano material in the nano material network.
And after the potential of each node and the current among the nodes of the nano material network are obtained through calculation, the potential of each node and the current among the nodes in the nano material network are represented through the height of the color.
According to a more detailed embodiment of the simulation method of the present disclosure, at the gate voltage V g Greater than the threshold voltage V th The simulation model of the nano material device is conducted, and the intersection point in the nano material network is electrically connectedThe resistance value is an on-state resistance value to obtain a source current I d Is an on-state current I on (ii) a At gate voltage V g Less than threshold voltage V th Under the condition of (1), the simulation model of the nano material device is cut off, the resistance value of the intersection point resistor in the nano material network is the off-state resistance value, and the source-level current I is obtained d Is an off-state current I off 。
The on-state current density is calculated using the following equation:
J on ＝I on /W
wherein, J on Is an on-state current density, I on And W is the width of a two-dimensional rectangular plane of the simulation model of the nano material device.
The width W of the two-dimensional rectangular plane of the simulation model of the nano material device is fixed to obtain the on-state current I on The larger the on-state current density J on The larger the size, the better the simulation model performance of the nano material device, and the better the performance of the corresponding nano material device.
The off-state current density is calculated using the following equation:
J off ＝I off /S
wherein, J off Is an off-state current density, I off W is the width of the two-dimensional rectangular plane of the simulation model of the nanomaterial device for off-state current.
The width of the two-dimensional rectangular plane of the simulation model of the nano material device is fixed to obtain off-state current I off The smaller, the off-state current density J off The smaller the size, the better the simulation model performance of the nanomaterial device, and the better the corresponding nanomaterial device performance.
Calculating the ratio K of the on-state current to the off-state current of the simulation model of the nano material device by using the following equation:
K＝J on /J off
based on the above, the present disclosure calculates the ratio K of the on-state current to the off-state current of the simulation model of the nanomaterial device, and can study the switching characteristics of the simulation model of the nanomaterial device according to the ratio K of the on-state current to the off-state current. Obtaining key parameters of a simulation model of the nano material device, comparing and verifying a simulation result and an experimental result, analyzing the same points and different points of the experimental result and a simulation result, and further understanding the reasons of device performance expression.
The larger the value of the ratio K of the on-state current to the off-state current is, the higher the conductivity of the corresponding nano material device is.
With carbon nanotubes as a one-dimensional material, fig. 8a shows a first schematic diagram of a comparison between simulation results and experimental results of a carbon nanotube thin film transistor device; and fig. 8b shows a second comparison diagram of simulation results and experimental results of the carbon nanotube thin film transistor device.
As shown in fig. 8a and 8b, the length l of the carbon nanotube CNT Carbon nanotube density ρ and carbon nanotube purity P m In two conditions of different values, the simulation result of the nano material device is highly consistent with the experimental result, and an excellent simulation effect is obtained.
In the first set of simulations, the purity of carbon nanotubes, P m Set to 1% carbon nanotube length l CNT Setting the density rho of the carbon nano tube to be 1.0 mu m, setting the density rho of the carbon nano tube to be 27.5 tubes/mu m, setting the voltage of the drain electrode to be-1V, and carrying out analog simulation under the parameters to obtain a simulated linear fitting result. The linear fitting results are presented in a coordinate system, wherein the abscissa of the coordinate system is the reciprocal of the channel length of the carbon nanotube thin film transistor, namely the reciprocal of the length of the two-dimensional rectangular plane, and the ordinate is the logarithm value of the ratio of the on-state current to the off-state current. Four scenes with the channel lengths of the carbon nano tube thin film transistors of 10 mu m, 5 mu m, 3 mu m and 2 mu m are selected for respectively carrying out experiments to obtain the ratios of on-state current and off-state current under the four experimental scenes. The simulated linear fitting result is compared with the result obtained by the experiment, the simulated linear fitting result is highly consistent with the experiment result, and an excellent simulation effect is obtained.
In the second set of simulation, the purity Pm of the carbon nanotube is set to 0.1%, the length lCNT of the carbon nanotube is set to 1.5 μm, the density ρ of the carbon nanotube is set to 28tubes/μm, and the drain voltage is set to-1V, and the simulation is performed under the parameters to obtain the linear fitting result of the simulation. The linear fitting results are presented in a coordinate system, wherein the abscissa of the coordinate system is the reciprocal of the channel length of the carbon nanotube thin film transistor, namely the reciprocal of the length of the two-dimensional rectangular plane, and the ordinate is the logarithm value of the ratio of the on-state current to the off-state current. Four scenes with the channel lengths of the carbon nano tube thin film transistors of 10 mu m, 5 mu m, 3 mu m and 2 mu m are selected for respectively carrying out experiments to obtain the ratios of on-state current and off-state current under the four experimental scenes. The simulated linear fitting result is compared with the result obtained by the experiment, the simulated linear fitting result is highly consistent with the experiment result, and an excellent simulation effect is obtained.
The simulation method is suitable for one-dimensional material devices with similar characteristics.
According to another aspect of the present disclosure, there is also provided a simulation apparatus of a nanomaterial device. Fig. 9 shows a schematic diagram of an emulation device 2000 of a nanomaterial device in accordance with an embodiment of the present disclosure.
As shown in fig. 9, the simulation apparatus 2000 of the nanomaterial device may include one or more processors 2001, and one or more memories 2002. Wherein the memory 2002 has stored therein computer readable code, which when executed by the one or more processors 2001, may perform a method as described above.
The processor in the embodiments of the present disclosure may be an integrated circuit chip having signal processing capabilities. The processor may be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, either of the X86 architecture or the ARM architecture.
In general, the various example embodiments of this disclosure may be implemented in hardware or special purpose circuits, software, firmware, logic or any combination thereof. Certain aspects may be implemented in hardware, while other aspects may be implemented in firmware or software which may be executed by a controller, microprocessor or other computing device. While aspects of embodiments of the disclosure have been illustrated or described as block diagrams, flow charts, or using some other pictorial representation, it is well understood that the blocks, apparatus, systems, techniques or methods described herein may be implemented in, as non-limiting examples, hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controller or other computing devices, or some combination thereof.
For example, a method or apparatus in accordance with embodiments of the present disclosure may also be implemented by way of the architecture of computing device 3000 shown in fig. 10. As shown in fig. 10, computing device 3000 may include a bus 3010, one or more CPUs 3020, a Read Only Memory (ROM) 3030, a Random Access Memory (RAM) 3040, a communication port 3050 to connect to a network, input/output components 3060, a hard disk 3070, and the like. A storage device in the computing device 3000, such as the ROM 3030 or the hard disk 3070, may store various data or files used in the processing and/or communication of the methods provided by the present disclosure, as well as program instructions executed by the CPU. Computing device 3000 can also include user interface 3080. Of course, the architecture shown in FIG. 9 is merely exemplary, and one or more components of the computing device shown in FIG. 10 may be omitted as needed in implementing different devices.
According to yet another aspect of the present disclosure, there is also provided a computer-readable storage medium. Fig. 11 shows a schematic diagram 5000 of a storage medium according to the present disclosure.
As shown in fig. 11, the computer storage media 4020 has stored thereon computer readable instructions 4010. The computer readable instructions 4010, when executed by a processor, may perform methods according to embodiments of the present disclosure described with reference to the above figures. The computer readable storage medium in embodiments of the present disclosure may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be Random Access Memory (RAM), which acts as external cache memory. By way of example and not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), synchronous Dynamic Random Access Memory (SDRAM), double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), enhanced Synchronous Dynamic Random Access Memory (ESDRAM), synchronous Link Dynamic Random Access Memory (SLDRAM), and direct memory bus random access memory (DR RAM). It should be noted that the memories of the methods described herein are intended to comprise, without being limited to, these and any other suitable types of memory. It should be noted that the memories of the methods described herein are intended to comprise, without being limited to, these and any other suitable types of memory.
Embodiments of the present disclosure also provide a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, causing the computer device to perform a method according to an embodiment of the present disclosure.
Embodiments of the present disclosure provide methods, apparatus, computer program products, and readable storage media for simulating nanomaterial devices.
The method provided by the embodiment of the disclosure sets initialization parameters of a simulation model of a nanomaterial device according to parameters of the nanomaterial device and one-dimensional nanomaterial units, randomly generates a nanomaterial network with the initialization parameters on a two-dimensional rectangular plane, wherein the nanomaterial network comprises a plurality of one-dimensional nanomaterial units, and at least two one-dimensional nanomaterials are intersected; and establishing a conductive network with the same topological structure as the nano material network, resolving the conductive path if the conductive path exists in the conductive network, and obtaining the electrical performance parameters of the simulation model of the nano material device, so as to accurately correspond to the relationship between each parameter of the nano material film and the electrical performance of the nano material device.
By the method, a user can obtain the performance distribution characteristics of the one-dimensional nanometer material device by establishing the model of the one-dimensional nanometer material device and corresponding to the relationship between each parameter of the nanometer material film and the electrical performance of the nanometer material device, so that the performance of the one-dimensional nanometer material device can be simulated and predicted, and data support can be provided for designing a high-performance nanometer material device.
It is to be noted that the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises at least one executable instruction for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In general, the various example embodiments of this disclosure may be implemented in hardware or special purpose circuits, software, firmware, logic or any combination thereof. Certain aspects may be implemented in hardware, while other aspects may be implemented in firmware or software which may be executed by a controller, microprocessor or other computing device. While aspects of embodiments of the disclosure have been illustrated or described as block diagrams, flow charts, or using some other pictorial representation, it is well understood that the blocks, apparatus, systems, techniques or methods described herein may be implemented in, as non-limiting examples, hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controller or other computing devices, or some combination thereof.
The exemplary embodiments of the present disclosure described in detail above are merely illustrative, and not restrictive. It will be appreciated by those skilled in the art that various modifications and combinations of these embodiments or the features thereof are possible without departing from the spirit and scope of the disclosure, and that such modifications are intended to be within the scope of the disclosure.

Claims (11)

1. A simulation method of a nanomaterial device, the nanomaterial device being fabricated from a one-dimensional nanomaterial cell, the simulation method comprising:
setting initialization parameters of a simulation model of the nanometer material device according to the parameters of the nanometer material device and the one-dimensional nanometer material unit;
randomly generating a nanomaterial network with the initialization parameters on a two-dimensional rectangular plane according to the initialization parameters, wherein the nanomaterial network comprises a plurality of one-dimensional nanomaterial units, and an intersection exists between at least two one-dimensional nanomaterial units in the plurality of nanomaterial units;
establishing a conductive network with the same topological structure as the nano material network, and judging whether a conductive path exists in the conductive network;
and under the condition that the conductive network has a conductive path, resolving the conductive path to obtain the electrical performance parameters of the simulation model of the nano material device.
2. The simulation method of claim 1,
the initialization parameters include at least one of: the size of the two-dimensional rectangular plane, the length of the one-dimensional nano material, the on-state resistance and the off-state resistance of the intersection point of the one-dimensional nano material, the density of the one-dimensional nano material and the purity of the one-dimensional nano material;
the electrical performance parameter comprises at least one of: the on-state current and the off-state current of the simulation model of the nano material device, the ratio of the on-state current to the off-state current of the simulation model of the nano material device, and the on-state current density and the off-state current density of the simulation model of the nano material device.
3. The simulation method according to claim 1, wherein according to the initialization parameters, the nanomaterial network with the initialization parameters is randomly generated on a two-dimensional rectangular plane for a plurality of times, and the electrical performance parameters of the simulation model of the nanomaterial device are obtained for a plurality of times; the simulation method further comprises the following steps:
and carrying out statistical analysis on the electrical performance parameters of the simulation model of the nano material device obtained for multiple times so as to determine the distribution characteristics of the electrical performance parameters.
4. The simulation method of claim 2, wherein the establishing a conductive network having the same topology as the nanomaterial network comprises:
determining the intersection condition of the plurality of one-dimensional nanomaterial units in the nanomaterial network;
for each intersection point in the nano material network, setting the intersection point as one edge in the conductive network, and determining the intersection point resistance of the intersection point as the resistance on the edge;
for the part of each one-dimensional nanomaterial cell located between two adjacent intersections, it is set as a node in the conductive network.
5. The simulation method of claim 4, wherein determining the intersection of the plurality of one-dimensional nanomaterial units in the nanomaterial network comprises:
optionally selecting one-dimensional nano material unit in the nano material network as a target one-dimensional nano material unit, setting a rectangular search area by taking the target one-dimensional nano material unit as a center, and searching whether other one-dimensional nano material units are included in the rectangular search area;
under the condition that other one-dimensional nano-material units exist, judging whether the target one-dimensional nano-material unit is intersected with other one-dimensional nano-material units in the rectangular search area or not, and under the condition that the target one-dimensional nano-material unit is intersected with the other one-dimensional nano-material units in the rectangular search area, storing intersection information of the target one-dimensional nano-material unit and the one-dimensional nano-material units;
traversing all the one-dimensional nano-material units in the nano-material network to obtain the intersection information of all the one-dimensional nano-material units.
6. The simulation method of claim 1, wherein the nanomaterial network comprises a plurality of sub-nanomaterial networks, each sub-nanomaterial network comprising a plurality of the one-dimensional nanomaterial units, and wherein there is an intersection between at least two of the one-dimensional nanomaterial units in the plurality of one-dimensional nanomaterial units;
wherein determining whether a conductive path exists in the conductive network comprises:
applying a source voltage and a drain voltage to the nanomaterial network, determining that a conductive path exists in the conductive network if the source voltage and the drain voltage are obtained by the sub-nanomaterial network;
and extracting the topological structure of the sub-nano material network, and generating a conductive network corresponding to the sub-nano material network as a conductive path according to the topological structure.
7. The simulation method of claim 1, wherein the conductive path comprises n nodes, n being a positive integer greater than 3, wherein resolving the conductive path to obtain electrical performance parameters of the simulation model of the nanomaterial device comprises:
applying a gate voltage, a drain voltage and a source voltage to the conductive path, the drain being set to node 0, the source being set to node 1 of the conductive path, the remaining nodes in the conductive path being set to node 2 to node n-1, respectively, wherein node 0 of the conductive path is set to a zero potential point;
the potential and source current of each node of the conductive path are calculated using the following equations:
Figure FDA0003291143870000031
wherein, sigma g i ＝g 1i +g 2i +…+g ni
Wherein u is i Is the potential of a conductive path node I, I being an integer greater than 0, I 1 Is a source current, V ds Is the voltage difference between source and drain, Σ g i Is the total conductance, g, of conductive path node i to the remaining conductive path nodes ij The conductance of node i to node j, which is the conductive path.
8. The simulation method of claim 7, wherein the calculating of the conductive path to obtain electrical performance parameters of the simulation model of the nanomaterial device comprises:
under the condition that the grid voltage is greater than the threshold voltage, the simulation model of the nanometer material device is conducted, the resistance value of the intersection point resistor in the nanometer material network is an on-state resistance value, and the obtained source-level current is an on-state current; under the condition that the grid voltage is smaller than the threshold voltage, the simulation model of the nanometer material device is cut off, the resistance value of the intersection point resistor in the nanometer material network is the off-state resistance value, and the obtained source-level current is the off-state current;
the on-state current density is calculated using the following equation:
J on ＝I on /W
wherein, J on Is an on-state current density, I on W is the width of a two-dimensional rectangular plane of a simulation model of the nano material device;
the off-state current density is calculated using the following equation:
J off ＝I off /W
wherein, J off Is an off-state current density, I off Is an off-state current, W isThe width of a two-dimensional rectangular plane of a simulation model of the nanomaterial device;
calculating the ratio K of the on-state current to the off-state current of the simulation model of the nano material device by using the following equation:
K＝J on /J off 。
9. an apparatus for simulating a nanomaterial device, comprising:
one or more processors; and one or more memories in which computer-executable programs are stored which, when executed by the processor, perform the method of any of claims 1-8.
10. A computer program product comprising computer software code for implementing a method according to any one of claims 1-8 when executed by a processor.
11. A computer-readable storage medium having stored thereon computer-executable instructions for implementing the method of any one of claims 1-8 when executed by a processor.
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CN111597767A (en) * 2020-04-29 2020-08-28 东南大学 Random nanowire network topology analysis and electrical property simulation method
CN112528428A (en) * 2020-11-20 2021-03-19 中国科学院武汉岩土力学研究所 Method and device for displaying physical parameters of engineering structure and computer equipment
CN113096238A (en) * 2021-04-02 2021-07-09 杭州柳叶刀机器人有限公司 X-ray diagram simulation method and device, electronic equipment and storage medium

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Publication number Priority date Publication date Assignee Title
CN111563325A (en) * 2020-04-29 2020-08-21 东南大学 Linear and nonlinear electrical property simulation method of random silver nanowire network
CN111597767A (en) * 2020-04-29 2020-08-28 东南大学 Random nanowire network topology analysis and electrical property simulation method
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