JP5958308B2 - Electric conduction characteristic prediction method and program - Google Patents

Description

  The present invention relates to a technique for predicting electric conduction characteristics of electronic materials.

Graphene is, for example, an atomic layer made of carbon atoms with sp ² bonds constituting a hexagonal lattice of carbon in a graphite crystal. However, if the effect of scattering can be suppressed, 200000 cm ² V ⁻¹ s ⁻¹ can be obtained even at room temperature. Since it is possible to achieve a very high electron mobility exceeding that of the above, research on making ultrafast electronic devices using graphene has been conducted.

  Graphene nanoribbons are particularly attracting attention as graphene nanostructures used in electronic device applications. This is because graphene itself is a zero-gap metalloid material, but a graphene nanoribbon obtained by processing graphene into a ribbon having a width of several nanometers becomes a semiconductor with a band gap and can be used as a channel of an electronic device. Knowing the electrical conductivity characteristics of such graphene nanoribbons is very important for application to electronic devices.

JP 2012-121751 A

  Quantum conduction calculation based on the nonequilibrium Green's function method is used as a method for examining the electrical conduction characteristics of electronic materials at the atomic level. The simulation is performed using a graphene sheet or an electrode structure model showing characteristics of a metal (Ni, Pd, etc.) used as an electrode in an actual electronic device. However, when these are used, at least several to about 10 k points are required to correctly describe the electronic state of the graphene sheet or metal, and high cost is required to obtain the transmission coefficient in the conduction direction at each k point. Therefore, it is impossible to check the electric conduction characteristics at high speed with high accuracy.

  In one aspect, an object of the present invention is to accurately predict electrical conduction characteristics at low calculation cost.

  According to an embodiment of the present invention, there is provided a method for predicting electrical conduction characteristics based on a nonequilibrium Green's function method executed by a computer, wherein an armchair graphene nanoribbon having a hydrogen-terminated edge is subjected to tensile strain in the armchair direction. A structure model is created by sandwiching a channel region as a system to be analyzed by using the electrode structure data stored in the storage unit. create.

  Further, as means for solving the above problems, an electric conduction characteristic prediction program for causing a computer to execute the above processing, a recording medium on which the program is recorded, and an electric conduction characteristic prediction apparatus can be provided.

  According to one aspect of this embodiment, since the electrode structure data in which the armchair graphene nanoribbon is subjected to tensile strain in the armchair direction is used, the number of k points can be greatly reduced, and the electric power can be accurately calculated at low calculation cost. Conductivity characteristics can be predicted.

It is a figure which shows the example of the structural model used for electrical conduction calculation. It is a figure which shows the example of the structural model in this Embodiment. It is a figure which shows the structural example of the armchair type graphene nanoribbon of ribbon width period N = 8. It is a figure for demonstrating the relationship between distortion and an energy gap. It is a figure which shows the relational expression between (epsilon) ₀ and m for making a band gap zero. It is a figure which shows the hardware constitutions of an electrical conduction characteristic prediction apparatus. It is a figure showing an example of functional composition of an electric conduction characteristic prediction device. It is a flowchart for demonstrating the process from a procedure (2) to a procedure (3). It is a flowchart for demonstrating the process in a procedure (4). It is a figure which shows the structural model of the electrical conduction calculation in a 1st Example. It is a figure which shows the comparative example of the calculation result of the current-voltage characteristic in a 1st Example. It is a figure which shows the structural model of the electric conduction calculation in a 2nd Example. It is a figure which shows the comparative example of the calculation result of the current-voltage characteristic in a 2nd Example.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings. First, a method for examining the electrical conduction characteristics of an electronic material at the atomic level will be described. FIG. 1 is a diagram illustrating an example of a structural model used for electrical conduction calculation.

  (1) First, a structural model 10 representing a structure in which a channel region 12 serving as a target system whose characteristics are to be investigated is sandwiched between semi-infinite electrodes 11 as shown in FIG. The electrode 11 is represented by structure data based on the type of atom, the position coordinates of the atom, and the like. Similarly, the channel region 12 is also expressed by structure data based on the type of atom, the position coordinates of the atom, and the like. Hereinafter, the electrode structure data is simply referred to as an electrode 11 (the same applies to an electrode 11a described later), and the channel region structure data is simply referred to as a channel region 12. A model in which the channel region 12 is sandwiched between the electrodes 11 on both sides is a structural model 10.

  (2) Next, a potential gradient corresponding to the applied voltage is generated between the electrodes 11 to converge the potential electronic state to self-consistent.

  (3) Using the obtained Green function, the transmission coefficient in the conduction direction at each k point in the reciprocal lattice space is obtained according to the Landauer formula.

  (4) This is calculated for all k points and summed to calculate the current value. Current voltage characteristics (electric conduction characteristics) are obtained by changing the applied voltage and repeating the calculation processes (2) to (4) above.

When investigating the electrical conductivity characteristics of graphene nanoribbons, as a structural model of the electrode 11 of (1) above,
(I) graphene sheet or
(Ii) Metal used as an electrode in an actual electronic device (Ni, Pd, etc.)
Is used. In this case, the number of k points is about several to 10 points, and the cost of the process (4) is high as described above. In addition, in the case of (i), an interface with a vacuum appears between the channel region 12 and the electrode 11, and in the case of (ii), a heterointerface appears, and an electric conduction characteristic taking into account the influence of these interfaces can be obtained. Therefore, the ideal electric conduction characteristic of the graphene nanoribbon itself that is the channel region 12 cannot be obtained.

  In the present embodiment, the number of k points is reduced by creating a model of the electrode 11 using an armchair graphene nanoribbon (AGNR) with zero energy gap, and the calculations of (2) to (4) above are performed. Reduce processing burden. In addition, it is possible to predict ideal electric conduction characteristics of the channel region 12 of the graphene sheet. In the present embodiment, zero energy gap or zero band gap means that it is within a predetermined value near zero including zero.

  Below, the electric conduction characteristic prediction method performed by the computer in this Embodiment is demonstrated.

  FIG. 2 is a diagram illustrating an example of a structural model in the present embodiment. In FIG. 2, in the structural model 10 a of the electrical conduction calculation in which the channel region 12 is sandwiched between the electrodes 11 a, the arm chair direction is applied to the arm chair type graphene nanoribbon having a ribbon width period N = 3 m−1 (m: integer) whose edges are hydrogen-terminated. Using the structure in which tensile strain is applied to the electrode 11a, the current-voltage characteristics of the channel region 12 are obtained by the processes (2), (3) and (4).

Here, the armchair direction is the direction A shown in FIG. 3, and the graphene nanoribbon having the structure B in which an edge parallel to the armchair direction A appears in the bond of the carbon atom 22 is referred to as an armchair graphene nanoribbon. Call. FIG. 3 shows an example of a structure of an armchair graphene nanoribbon having a ribbon width period N = 8, the edge of which is terminated by a hydrogen atom 23 (hydrogen termination). In FIG. 3, d _A shows a unit cell size of the armchair direction A of the unit cell 21 in which carbon atoms bonded to a hexagonal lattice.

  In general, the armchair graphene nanoribbon is a semiconductor having an energy band gap, and is inappropriate for use as a model of the electrode 11a. However, armchair-type graphene nanoribbons can control the size of the energy band gap by uniaxial strain, thereby creating a structural model with zero energy gap and adopting it as a model for the electrode 11a Is possible. As the armchair graphene nanoribbon used at this time, the use of a nanoribbon having a ribbon width period of N = 3m-1 (m: integer) reduces the uniaxial distortion applied when using a nanoribbon of N = 3m, 3m + 1. Can be the most realistic.

  FIG. 4 is a diagram for explaining the relationship between strain and energy gap. As shown in FIG. 4A, the energy band gap Eg when a tensile strain C is applied in the armchair direction to the unit cell 21 of the graphene nanoribbon of m = 3 (ribbon width period N = 8) is shown. The band structure shown in FIG. 4 (B) and having a zero band gap is shown in FIG. 4 (C).

  From FIG. 4B, when m = 3 (ribbon width period N = 8), there is an energy band gap of 0.23 eV when there is no distortion, but ε = 1.86% in the armchair direction. When the tensile strain C (FIG. 4A) is added, the energy band gap becomes 0.002 eV, which can be used as a model of the electrode 11a. FIG. 4C shows the band structure.

Here, the value of ε at which the energy band gap becomes almost zero is ε ₀ . FIG. 5 is a diagram showing a relational expression between ε ₀ and m for making the band gap zero. As shown in FIG. 5, there is a relationship of ε ₀ = 16.9 / 3 m between m and ε ₀ . If m is determined, ε ₀ can be obtained from this relational expression.

  The electric conduction characteristic predicting apparatus 100 for predicting the electric conduction characteristic of the graphene nanoribbon according to the present embodiment as described above has a hardware configuration as shown in FIG. FIG. 6 is a diagram illustrating a hardware configuration of the electrical conduction characteristic predicting apparatus.

  In FIG. 6, an electric conduction characteristic prediction apparatus 100 is a terminal controlled by a computer, and includes a CPU (Central Processing Unit) 11, a main storage device 52, an auxiliary storage device 53, an input device 54, and a display device. 55, an output device 56, a communication I / F (interface) 57, and a drive 58, which are connected to the bus B.

  The CPU 51 controls the electric conduction characteristic prediction device 500 according to a program stored in the main storage device 52. The main storage device 52 uses a RAM (Random Access Memory) or the like, and stores a program executed by the CPU 51, data necessary for processing by the CPU 51, data obtained by processing by the CPU 51, and the like. A part of the main storage device 52 is allocated as a work area used for processing by the CPU 51.

  The auxiliary storage device 53 uses a hard disk drive and stores data such as programs for executing various processes. A part of the program stored in the auxiliary storage device 53 is loaded into the main storage device 52 and executed by the CPU 51, whereby various processes are realized. The storage unit 130 includes a main storage device 52 and / or an auxiliary storage device 53.

The input device 54 includes a mouse, a keyboard, and the like, and is used for a user to input various information necessary for processing by the electrical conduction characteristic prediction device 100. The display device 55 displays various information necessary under the control of the CPU 51. The output device 56 has a printer or the like and is used for outputting various types of information in accordance with instructions from the user. The communication I / F 57 is a device for controlling communication with an external device connected to the Internet, a LAN (Local Area Network), or the like, for example. Communication by the communication I / F 57 is not limited to wireless or wired communication.
A program for realizing the processing performed by the electrical conduction characteristic prediction apparatus 100 is provided to the electrical conduction characteristic prediction apparatus 100 by a storage medium 59 such as a CD-ROM (Compact Disc Read-Only Memory). That is, the program is read from the storage medium 59 in which the program is stored and installed in the auxiliary storage device 53 via the bus B. When the program is activated, the CPU 51 starts its processing according to the program installed in the auxiliary storage device 53. The medium for storing the program is not limited to a CD-ROM, and any medium that can be read by a computer may be used. As a computer-readable storage medium, in addition to a CD-ROM, a portable recording medium such as a DVD disk or a USB memory, or a semiconductor memory such as a flash memory may be used.

  FIG. 7 is a diagram illustrating a functional configuration example of the electrical conduction characteristic prediction apparatus. In FIG. 7, the electric conduction characteristic prediction apparatus 100 includes an input parameter acquisition unit 31, a structural model creation unit 32, and a current-voltage characteristic calculation unit 33 as processing units. Each processing unit 31, 32, and 33 is realized by processing by the CPU 51 executing a corresponding program. Further, the storage unit 130 stores an input parameter 41, a specified value 42, a structural model 10a, output data 44, and the like. A procedure performed by each of the processing units 31 to 33 in the electric conduction characteristic prediction apparatus 100 will be described below.

  In the procedure (1), the input parameter acquisition unit 31 acquires an input parameter 41 including the graphene nanoribbon structure of the channel region 12 to be analyzed from the user, and stores it in the storage unit 130.

The input parameters 41, the type and the atomic coordinates of atoms of the channel region 12, includes information of the ribbon width period N _C, etc. in the channel region 12.

In Step (2), the input parameter acquisition unit 31 refers to the input parameter 41 for the ribbon width period N _C of graphene nanoribbons prepared in the channel region 12, the armchair graphene nanoribbons used for the electrode 11a ribbon The value of m is selected to be m ₀ so that the width period N = 3m−1 is the closest value. For example, if N _C = 7, m ₀ = 3.

In procedure (3), the structural model creation unit 32 determines the value of ε ₀ from the relational expression of ε ₀ = 16.9 / 3m ₀ in FIG. For example, if m ₀ = 3, ε ₀ = 1.86.

In the procedure (4), the structural model creation unit 32 refers to the specified value 42 stored in advance in the storage unit 130 for the armchair graphene nanoribbon with the ribbon width period N = 3 m ₀ −1. Unit cell size d _A is the unit cell size of the graphene sheet

からε_０％広げ、そのユニットセル２１に対して相対的な位置が変わらないようにユニットセル２１内の炭素原子や水素原子の位置座標を変化させた構造を作成する。

Ε spread _0% from to create a relative position has changed the coordinates of the carbon atoms and hydrogen atoms in the unit cell 21 so as not to change the structure for the unit cell 21.

  In the procedure (5), the structural model creation unit 32 combines the structure of the electrode 11a and the channel region 12 created by the above procedures (1) to (4) to obtain the structural model 10a by electrode / channel region / electrode. create. The created structural model 10a is stored in the storage unit 130.

  In the procedure (6), the current-voltage characteristic calculation unit 33 uses the structural model 10a created in the procedure (5) stored in the storage unit 130 to perform the existing electrical conduction calculation based on the nonequilibrium green function method. The current-voltage characteristics of the channel region 12 are obtained by a program.

  The process from the procedure (2) to the procedure (3) will be described with reference to FIG. FIG. 8 is a flowchart for explaining the processing from the procedure (2) to the procedure (3). In FIG. 8, steps S11 to S16 correspond to the process in the procedure (2) performed by the input parameter acquisition unit 31, and step S17 corresponds to the process in the procedure (3) performed by the structural model creation unit 32. To do.

In step (2), the input parameter acquisition unit 31 obtains m that makes the ribbon period N _C of the channel region 12 close to the ribbon period N of the electrode 12a. Input parameter acquisition unit 31 substitutes 1 into m (step S11), and the absolute value of the difference between the ribbon period _{N C} and 3m-1 of the channel region 12 (hereinafter, simply referred to as the difference.) The Diff (m) (Step S12).

Thereafter, the input parameter acquisition unit 31 increments m by 1 (step S13), and updates the difference Diff (m) (step S14). The difference between the ribbon period N _C of the channel region 12 and 3m−1 after adding 1 to m is obtained.

  Next, the input parameter acquisition unit 31 determines whether or not the difference Diff (m) obtained this time is larger than the previous difference Diff (m−1) (step S15). If the current difference Diff (m) is equal to or less than the previous difference Diff (m−1), the input parameter acquisition unit 31 returns to step S13 and performs the same processing as described above.

When the current difference Diff (m) is larger than the previous difference Diff (m−1), the input parameter acquisition unit 31 sets m−1 to m ₀ (step S16), and the procedure by the input parameter acquisition unit 31 The process (2) is terminated. In m ₀ , m is set such that the ribbon period N _C of the channel region 12 and the ribbon period N of the electrode 12 a are close to each other and stored in the storage unit 130.

When the process of the procedure (2) is completed, the process of the procedure (3) is performed by the structural model creation unit 32. The structural model creation unit 32 determines the value of ε ₀ from the relational expression ε ₀ = 16.9 / 3m ₀ in FIG. 5 using m ₀ obtained in the procedure (2) (step S17).

  Next, the structural model creation unit 32 proceeds to the procedure (4) shown in FIG. FIG. 9 is a flowchart for explaining the processing in the procedure (4). In FIG. 9, steps S18 to S2 correspond to the processing in the procedure (4) performed by the structural model creation unit 32.

In FIG. 9, the structural model creation unit 32 calculates the size d _A of the unit cell 21 after applying tensile strain by expanding the size d _A ^GS of the unit cell 21 by ε ₀ % in the armchair direction (step S18). ), 1 is substituted into the variable i (step S19).

Then, the structural model creating unit 32 obtains the position coordinates of the carbon atoms when the arm model is expanded by adding a tensile strain ε ₀ % in the armchair direction A (step S20). The structural model creation unit 32
Mod (i, 4) = 0
⇒ x (i) = 0,
y (i) = 0 + int (i / 4) * d _A / sqrt (3),
z (i) = 0
・ Mod (i, 4) = 1
⇒x (i) = 1/6 * d _A ,
y (i) = d _A / sqrt (3) / 2 + int (i / 4) * d _A / sqrt (3),
z (i) = 0
Mod (i, 4) = 2
⇒x (i) = 3/6 * d _A ,
y (i) = d _A / sqrt (3) / 2 + int (i / 4) * d _A / sqrt (3),
z (i) = 0
Mod (i, 4) = 3
⇒x (i) = 4/6 * d _A ,
y (i) = 0 + int (i / 4) * d _A / sqrt (3),
z (i) = 0
As described above, the coordinates corresponding to the positions of the four carbon atoms forming the armchair (convex) portion in the hexagonal lattice bond of the carbon atoms are calculated by the remainder calculation by dividing the variable i by 4.

The structural model creation unit 32 determines whether or not the variable i is (3m ₀ −1) × 2 (step S21). If the variable i is not (3m ₀ −1) × 2, the structural model creation unit 32 increments the variable i by 1 (step S21-2), returns to step S20, and calculates the position of the carbon atom.

When the variable i is (3m ₀ −1) × 2, the structural model creation unit 32 obtains the position coordinates of the hydrogen atom when the arm chair direction A is expanded by adding a tensile strain ε ₀ % (step S22). . The structural model creation unit 32 sets the ribbon period N of the electrode 12a and the difference ΔdA between the electrodes 12a and obtains the position coordinates of the four hydrogen terminations.

N = 3 m ₀ −1, Δd _A = (100 + ε ₀ ) / 100
X (2N + 1) = x (2N-1) -d _CH / 2 * Δd _A ,
y (2N + 1) = y (2N-1) + d _CH / 2 * sqrt (3),
z (2N + 1) = 0
X (2N + 2) = x (2N) + d _CH / 2 * Δd _A ,
y (2N + 2) = y (2N) + d _CH / 2 * sqrt (3),
z (2N + 2) = 0
X (2N + 3) = x (1) + d _CH / 2 * Δd _A ,
y (2N + 3) = y (1) -d _CH / 2 * sqrt (3),
z (2N + 3) = 0
X (2N + 4) = x (2) -d _CH / 2 * Δd _A ,
y (2N + 4) = y (1) -d _CH / 2 * sqrt (3),
z (2N + 4) = 0
When the position coordinate of each atom is obtained with respect to the unit cell 21 of the unit size d _A after being pulled, the structural model creation unit 32 combines the structure of the electrode 11a based on the unit cell 21 and the channel region 12 to obtain the structural model 10a. The process in the creating procedure (5) is performed, and the process in the structural model creating unit 32 is terminated. The structural model 10a is stored in the storage unit 130.

  Then, the current / voltage calculator 33 uses the structural model 10a stored in the storage unit 130 to calculate the current value of the channel region using an existing electrical conduction calculation program based on the nonequilibrium Green function method. The current value calculated by the current voltage calculator 33 is output and stored in the storage unit 130 as the output result 44.

  As described above, in the present embodiment, the structural model 10a using the graphene sheet having a zero band gap as the electrode 11a can be created. Therefore, since the current-voltage characteristic calculation unit 33 can predict the current value with only one k point, the processing load can be reduced.

Further, with respect to the ribbon width period N _C graphene nanoribbon channel region 12, so that the ribbon width period N = 3m-1 of armchair graphene nanoribbons electrode 11a becomes a value closest to determine the m. Since the structure of the channel region 12 and the electrode 11a are both graphene nanoribbons and the ribbon width period is close to each other, the channel region 12 and the channel region 12 are not affected by the interface between the electrode 11a and the channel region 12. Current-voltage characteristics can be predicted.

  An example of electric conduction characteristic prediction performed by the electric conduction characteristic prediction apparatus 100 according to the present embodiment will be described below as a first example and a second example.

[First embodiment]
FIG. 10 is a diagram showing a structural model 10a for electrical conduction calculation in the first embodiment. In the first example, the structure (a) in which the channel region 12 indicates a graphene nanoribbon with N = 8 and the structure (b) in which the channel region 12 indicates a graphene nanoribbon with N = 8 are described above. The current-voltage characteristics are calculated according to procedures (1) to (6).

In the procedure (1), the input parameter acquisition unit 31 includes input parameters including the structures of the armchair graphene nanoribbons of N _C = 8 and N _C = 7 as the channel regions 12 of the structures (a) and (b), respectively. 41 is stored in the storage unit 130. In these structures (a) and (b), all edges are hydrogen terminated.

In step (2), the input parameter acquisition unit 31 sets m ₀ = 3 for both N _C = 8 and 7, and the ribbon width period of the armchair graphene nanoribbon used for the electrode 11a is N = 3 m. _It is determined that ₀ −1 = 8.

In procedure (3), the structural model creation unit 32 obtains ε ₀ = 16.9 / (3 × 3) = 1.86.

In procedure (4), the structural model creation unit 32 obtains d _A = d _A ^GS × Δd _A = 4.360, and each atomic coordinate in the unit cell of the electrode 11a is determined.

  In procedure (5), the structural model creation unit 32 creates the structural model 10a shown in FIG.

  In step (6), the current-voltage characteristic calculation unit 33 predicts a current value using each structural model 10a shown in FIG. The first principle calculation code OpenMX (http://www.openmx-square.org/) may be used as an existing electric conduction calculation program based on the nonequilibrium Green function method.

FIG. 11 shows the current-voltage characteristics obtained in this way. FIG. 11 is a diagram showing a comparative example of calculation results of current-voltage characteristics in the first embodiment. In FIG. 11, the vertical axis indicates the amount of current (μA), and the horizontal axis indicates the bias voltage V _b (V).

  Ideally, structure (a) has no energy band gap, while structure (b) has an energy band gap of 1.3 eV. In FIG. 11, the structure (a) shows a metallic property that the current amount increases linearly from 0 V with respect to the bias voltage, whereas the structure (b) almost increases the current amount below the bias voltage of about 1 V. In addition, it shows a semiconductor property in which the current amount increases linearly with the bias voltage thereafter, and it can be confirmed that the current-voltage characteristic of the ideal channel region can be well predicted in this embodiment.

[Second Embodiment]
FIG. 12 is a diagram showing a structural model 10a for electric conduction calculation in the second embodiment. In the second embodiment, the channel region 12 is a graphene nanoribbon of N = 7, and has a structure in which all edges are hydrogen-terminated (structure (b)) and a structure in which all edges are fluorine-terminated (structure (c)). For the type of structure, the current-voltage characteristics are calculated according to the above-described procedures (1) to (6). Structure (b) is the same as in the first embodiment.

In the procedure (1), the input parameter acquisition unit 31 stores the input parameter 41 including N _C = 7 armchair graphene nanoribbons in the storage unit 130 as the channel region 12 of the structures (b) and (c). The In structure (b), all edges are hydrogen terminated, and in structure (c), all edges are fluorine terminated.

  Since the processing after the procedure (2) is the same as that of the first embodiment, the description thereof is omitted.

The current-voltage characteristics thus obtained are shown in FIG. FIG. 13 is a diagram showing a comparative example of calculation results of current-voltage characteristics in the second embodiment. In FIG. 13, the vertical axis indicates the amount of current (μA), and the horizontal axis indicates the bias voltage V _b (V).

  From FIG. 13, it can be seen that when the edge is terminated with fluorine, the bias voltage at which the amount of current starts increasing is lower than that in the case of hydrogen termination, and the amount of current also increases. As described above, the electric conduction characteristic prediction apparatus 100 according to the present embodiment can also predict the influence of the edge termination difference on the current-voltage characteristic, and the design of the graphene nanoribbon used in the channel region of the electronic device Useful for.

  The present invention is not limited to the specifically disclosed embodiments, and various modifications and changes can be made without departing from the scope of the claims.

Regarding the embodiment including the first and second examples, the following additional notes are disclosed.
(Appendix 1)
An electrical conduction property prediction method based on a nonequilibrium Green's function method executed by a computer,
Create and store in the storage unit the electrode structure data of the armchair graphene nanoribbons with hydrogen-terminated edges and tensile strain in the armchair direction,
A method for predicting electrical conduction characteristics, wherein a structural model is created by sandwiching a channel region to be an analysis target system with the electrodes, using the structural data of the electrodes stored in the storage unit.
(Appendix 2)
The electrical conduction characteristic prediction method according to appendix 1, wherein the structure of the channel region sandwiched in the structural model is a graphene nanoribbon.
(Appendix 3)
The ribbon width period of the electrode is represented by N = 3m ₀ −1 (m ₀ is an integer), m ₀ closest to the ribbon width period of the channel region is specified, and the ribbon width period N of the electrode is determined. The method for predicting electrical conduction characteristics according to supplementary note 2, characterized by:
(Appendix 4)
The relational expression expressing the amount of the tensile strain in the armchair direction applied to the electrode, the relationship between the amount of strain at which the band gap of the electrode is in the vicinity of zero and m ₀ related to the ribbon width period of the electrode. Calculated using
In accordance with the calculated amount of tensile strain, the unit cell size of the electrode in the armchair direction is expanded,
Determining the atomic coordinates of the electrode based on the expanded unit cell size;
The electrical conduction characteristic prediction method according to supplementary note 3, wherein structure data represented by the determined atomic coordinates of the electrode is stored in the storage unit.
(Appendix 5)
The electrical conduction characteristic prediction method according to any one of additions 1 to 4, wherein the electrical conduction characteristic of the channel region is predicted at one k point using the structural model stored in the storage unit.
(Appendix 6)
Create and store in the storage unit the electrode structure data of the armchair graphene nanoribbons with hydrogen-terminated edges and tensile strain in the armchair direction,
Using the electrode structure data stored in the storage unit, create a structure model sandwiching a channel region to be analyzed by the electrode,
An electrical conduction characteristic prediction program for causing a computer to execute a process of predicting an electrical conduction characteristic of the channel region based on a nonequilibrium green function method using the created structural model.
(Appendix 7)
A storage unit for storing the structure data of the channel region to be analyzed;
Based on the structure data of the channel region stored in the storage unit, the structure data of the electrode is created by applying tensile strain in the armchair direction to the armchair graphene nanoribbon whose edges are hydrogen-terminated. Using the data, a structural model creation unit that creates a structural model with the channel region sandwiched between the electrodes,
A calculation unit that predicts electrical conduction characteristics by calculating a current value of the channel region based on a nonequilibrium green function method using the structural model created by the structural model creation unit; Electrical conductivity characteristic prediction device.

DESCRIPTION OF SYMBOLS 10, 10a Structural model 11, 11a Electrode 12 Channel area | region 21 Unit cell 22 Carbon atom 23 Hydrogen atom 31 Input parameter acquisition part 32 Structural model creation part 33 Current-voltage characteristic calculation part 51 CPU
52 Main storage device 53 Auxiliary storage device 54 Input device 55 Display device 56 Output device 57 Communication I / F
58 drive 59 storage medium 100 electrical conductivity measurement prediction device

Claims (5)

An electrical conduction property prediction method based on a nonequilibrium Green's function method executed by a computer,
Create and store in the storage unit the electrode structure data of the armchair graphene nanoribbons with hydrogen-terminated edges and tensile strain in the armchair direction,
A method for predicting electrical conduction characteristics, wherein a structural model is created by sandwiching a channel region to be an analysis target system with the electrodes, using the structural data of the electrodes stored in the storage unit.   The method for predicting electrical conduction characteristics according to claim 1, wherein the structure of the channel region sandwiched in the structural model is a graphene nanoribbon. The ribbon width period of the electrode is represented by N = 3m ₀ −1 (m ₀ is an integer), m ₀ closest to the ribbon width period of the channel region is specified, and the ribbon width period N of the electrode is determined. The method for predicting electrical conduction characteristics according to claim 2. The relational expression expressing the amount of the tensile strain in the armchair direction applied to the electrode, the relationship between the amount of strain at which the band gap of the electrode is in the vicinity of zero and m ₀ related to the ribbon width period of the electrode. Calculated using
In accordance with the calculated amount of tensile strain, the unit cell size of the electrode in the armchair direction is expanded,
Determining the atomic coordinates of the electrode based on the expanded unit cell size;
4. The method according to claim 3, wherein structure data represented by the determined atomic coordinates of the electrode is stored in the storage unit. Create and store in the storage unit the electrode structure data of the armchair graphene nanoribbons with hydrogen-terminated edges and tensile strain in the armchair direction,
Using the electrode structure data stored in the storage unit, create a structure model sandwiching a channel region to be analyzed by the electrode,
An electrical conduction characteristic prediction program for causing a computer to execute a process of predicting an electrical conduction characteristic of the channel region based on a nonequilibrium green function method using the created structural model.

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