JP5821163B2 - Ion implantation condition setting method, process / device simulator, storage medium, and program - Google Patents

Description

  The present invention relates to a technology that makes it easy to set ion implantation conditions by evaluating energy with a desired range projection or junction depth using LSS theory, and evaluating distribution using Monte Carlo simulation or LSS theory with that energy. .

  As means for theoretically evaluating the ion implantation distribution, there are Monte Carlo simulation and LSS theory. Using these, it is possible to evaluate the ion implantation distribution of arbitrary ions to an arbitrary substrate. In these calculations, by calculating the ion implantation distribution with some energy as input values, it has become possible to search for the energy at the desired peak concentration position and junction depth.

Kunihiro Suzuki, “Ion implantation distribution analysis for high-precision process / device simulation: ion implantation distribution database based on tail function”, FUJITSU, January 2010, p. 70-79 Kunihiro Suzuki, “Ion implantation distribution analysis for high-precision process / device simulation—Ion implantation distribution analysis based on pseudo-crystal extended LSS theory”, FUJITSU, January 2010, p. 80-87 Kunihiro Suzuki, “Extended Lindhard-Scharf-Schiott Theory for Ion ImplantationProfiles Expressed with Pearson Function”, Japanese Journal of Applied Physics, vol. 48, pp. 046510-1 -046510-9, 2009 Kunihiro Suzuki, “Quasi Crystal Lindhard-Scharf-Schiott Theory and Database for IonImplantation Profiles in Si1-xGex Substrate Based on theTheory”, Japanese Journal of Applied Physics, vol. 48, pp. 071202-1-071202-12, 2009 Ashworth DG, Oven R, Mundin B. “Representation of ion implantation profiles by Pearson frequency distribution curves”, J. Phys. D: Appl. Phys., Vol. 23, pp. 870-876, 1990 Kunihiro Suzuki, Ritsuo Sudo, “Analytical expression for ion-implanted impurity concentration profiles”, Solid-State Electronics, vol. 44, pp. 2253-2257, 2000 Kunihiro Suzuki, Kazuo Kawamura, Yoshio Kikuchi, and Yuji Kataoka, “Compact Model for Amorphous Layer Thickness Formed by Ion Implantation Over Wide Ion Implantation Conditions”, IEEE Transactions on Electron Devices, vol. 53, No. 5, May 2006

  However, in Monte Carlo simulation, the track of each ion is followed and its statistical distribution is obtained. Therefore, it takes a lot of time to search for the desired energy by repeating it several times. In the LSS theory, since the calculation ends instantaneously, the time required for the condition search can be greatly reduced. However, in order to find the condition, it is necessary to perform the energy input manually and the calculation process based on the input value many times, and the user's burden and processing time are still required.

The disclosed technique is an ion implantation condition setting method in which a computer sets ion implantation conditions for simulating an ion implantation distribution, and the computer inputs first information on ion species and substrate material basic information of the ion implantation conditions. A screen is displayed on the input unit, a basic input procedure for acquiring the basic information from the user, and a second input screen for the distribution parameter relating to the shape of the ion implantation distribution is displayed on the input unit, and the user sets the dose φ. When input, a value input procedure for obtaining the user by inputting a value of one or more distribution parameters related to the prediction of energy E, energy from the dose φ acquired by the value input procedure and the value of the distribution parameter E is predicted, the basic information, the predicted energy E, the specified ion implantation conditions, the dose φ, and the amount A prediction output procedure to output the distribution parameter information specifying a value of the parameter, based on said ion implantation conditions that are output and the distribution parameter information from the predicted output procedure corresponding to the ion implantation conditions distribution parameter A distribution parameter setting procedure for calculating and setting the distribution parameter, and a distribution generation procedure for generating an ion implantation distribution based on the distribution parameter set by the distribution parameter setting procedure.

  The disclosed technology may be a process / device simulator for executing the above method, a computer readable storage medium storing a program for causing a computer to execute the above procedure as a function, and the program.

  With the disclosed ion implantation condition setting method, when simulating the ion implantation distribution, the user can set a desired distribution parameter instead of the ion implantation condition, and the ion implantation condition corresponding to the distribution parameter is set. can do.

It is a figure which shows the hardware constitutions of a simulator. It is a figure which shows the function structural example of a simulator. It is a flowchart figure for demonstrating the process by a simulator. It is a figure which shows the example of an experiment database. It is a flowchart figure for demonstrating the process for evaluating energy from a joining position. It is a figure which shows the process example which evaluates energy from a joining position. It is a flowchart for demonstrating the process for evaluating energy from a joining position in the case of a crystal substrate. It is a figure which shows the example of a screen.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings. The simulator according to the present embodiment has a hardware configuration as shown in FIG. FIG. 1 is a diagram illustrating a hardware configuration of a simulator. In FIG. 1, a simulator 100 according to this embodiment is a device controlled by a computer, and includes a CPU (Central Processing Unit) 11, a memory unit 12, a display unit 13, an output unit 14, and an input unit 15. And a communication unit 16, a storage device 17, and a driver 18, and are connected to the system bus B.

  The CPU 11 controls the simulator 100 according to a program stored in the memory unit 12. The memory unit 12 uses a RAM (Random Access Memory), a ROM (Read-Only Memory), or the like, and is obtained by a program executed by the CPU 11, data necessary for processing by the CPU 11, and processing by the CPU 11. Stored data. A part of the memory unit 12 is allocated as a work area used for processing by the CPU 11.

  The display unit 13 displays various information required under the control of the CPU 11. The output unit 14 includes a printer or the like, and is used for outputting various types of information according to instructions from the user. The input unit 15 includes a mouse, a keyboard, and the like, and is used by a user to input various information necessary for the simulator 100 to perform processing. The communication unit 16 is a device that is connected to, for example, the Internet, a LAN (Local Area Network), and the like and controls communication with an external device. For example, a hard disk unit is used as the storage device 17 and stores data such as programs for executing various processes.

  A program that realizes processing performed by the simulator 100 is provided to the simulator 100 by a storage medium 19 such as a CD-ROM (Compact Disc Read-Only Memory). That is, when the storage medium 19 storing the program is set in the driver 18, the driver 18 reads the program from the storage medium 19, and the read program is installed in the storage device 17 via the system bus B. . When the program is activated, the CPU 11 starts its processing according to the program installed in the storage device 17. 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. The program for realizing the processing according to the present embodiment may be downloaded via the network by the communication unit 16 and installed in the storage device 17. Further, if the simulator 100 is compatible with USB, it may be installed from an external storage device capable of USB connection. Further, if the simulator 100 is compatible with a flash memory such as an SD card, it may be installed from such a memory card.

  FIG. 2 is a diagram illustrating a functional configuration example of the simulator. In FIG. 2, the simulator 100 includes an experiment database 21, an LSS database 22, an input processing unit 3, an ion implantation condition acquisition unit 4, a distribution parameter setting unit 5, a distribution generation unit 6, and a Monte Carlo screen configuration unit 7. And a Monte Carlo simulation unit 8. The experiment database 21 and the LSS database 22 are stored in the storage device 17. When the CPU 11 executes the program according to the present embodiment, it functions as the processing units 3 to 8 and operates as a process / device simulator.

  The experimental database 21 is a database of results obtained by evaluating the ion implantation distribution when implanted under various ion implantation conditions by SIMS (Secondary Ion Mass Spectrometry).

The LSS database 22 is a database based on LSS (Lindhard-Scharff-Schiott) theory, and makes it possible to instantaneously predict the ion implantation distribution when experimental data is scarce. In the LSS database 22, a moment parameter of distribution (hereinafter simply referred to as distribution parameter) is calculated using a combination of ion species, substrate material, and dose Φ and acceleration energy E (hereinafter simply referred to as energy E) as input values. For example, R _p (E), ΔR _p (E), γ (E), β (E), ΔR _pt (E), and the like are stored in a database.

By R _p (E) can be obtained projection _{R p} projected range, by [Delta] R _p (E) can be obtained variations in the depth direction of the projection _{R p} projected range. Moreover, the skewness expressing the asymmetry of the distribution can be obtained by γ (E), and the kurtosis expressing the sharpness of the shape near the distribution peak can be obtained by β (E). Furthermore, the variation in the horizontal direction in the range projection R _p can be obtained by ΔR _pt (E).

  The input processing unit 3 is a processing unit that enables input of ion implantation conditions and distribution parameters, and includes a basic input unit 30, a first input unit 31, a second input unit 32, and a third input unit 33. Have.

  The basic input unit 30 is a processing unit that causes the input unit 15 to display an input screen that prompts the user to input basic information on ion implantation conditions, and acquires ion species to be implanted, substrate material, and the like.

Each of the first input unit 31 and the second input unit 32 causes the input unit 15 to display an input screen that allows the user to input a distribution parameter related to the ion implantation distribution shape, and obtains a parameter value desired by the user. Then, the basic information acquired by the basic input unit 30 is input to the ion implantation condition acquisition unit 4. The first input unit 31, if the user enters the dose [Phi, projection projected range _{R p,} the maximum concentration position _{R pm,} the joining position (junction depth) _{x j,} relates the distribution parameters HiAkirashitsusoAtsu _{d a,} This is a processing unit that allows the user to selectively input one or more distribution parameter values and obtain them. The second input unit 32, if the user enters the energy E, the bonding position x _j, HiAkirashitsusoAtsu d processing section distribution parameter user selectively to enter one or more distribution parameters values to get the _a It is.

  The third input unit 33 inputs the energy E and the dose Φ to the user, acquires these values, and inputs the values together with the basic information acquired by the basic input unit 30 to the distribution parameter setting unit 5 as the ion implantation condition 49.

  The ion implantation condition acquisition unit 4 selectively selects the experimental database 21 or the LSS database 22 from the energy E or dose Φ as the ion implantation condition input from the input processing unit 3 and the selected distribution parameter and its value. Is a processing unit that acquires the dose Φ or energy E as an output value, and includes a first output unit 41 and a second output unit 42. The selection of the database 21 or 22 may be made based on user settings.

  The first output unit 41 selectively uses the experiment database 21 or the LSS database 22, and uses the energy E from the dose Φ input from the first input unit 31, the distribution parameter selected by the user, and the value thereof. Is a processing unit that predicts and outputs. The ion implantation condition acquisition unit 4 designates basic information including information for specifying the ion species and substrate material acquired by the basic input unit 30 from the user, and the energy E output by the first output unit 41. 47, and the distribution parameter information 48 specifying the distribution parameter selected by the user and its value is input to the distribution parameter setting unit 5.

  The second output unit 42 selectively uses the experimental database 21 or the LSS database 22, and uses the energy E input from the second input unit 32, the distribution parameter selected by the user, and the value thereof as the dose Φ. Is a processing unit that predicts and outputs. The ion implantation condition acquisition unit 4 includes basic information including information specifying the ion species and substrate material acquired by the basic input unit 30 from the user, and the dose Φ output by the second output unit 42 together with the ion implantation condition 47. The distribution parameter information 48 specifying the distribution parameter selected by the user and its value is input to the distribution parameter setting unit 5.

The distribution parameter setting unit 5 is a processing unit that sets a distribution parameter for generating an ion implantation distribution in response to an input from the ion implantation condition acquisition unit 4 or the third input unit 33 of the input processing unit 3. The distribution parameter setting unit 5 sets the range projection R _p , depth direction variation ΔR _p , skewness γ, kurtosis β, lateral variation ΔR _pt , and dose Φ as distribution parameters.

  The distribution parameter setting unit 5 is based on the information input from the ion implantation condition acquisition unit 4 including the ion implantation conditions 47 predicted from the distribution parameters related to the distribution shape desired by the user and the distribution parameter information 48. This is a processing unit that sets the required distribution parameter values using the experimental database 21 or the LSS database 22 and inputs the distribution parameter values to the distribution generation unit 6 or the screen configuration unit 7 for Monte Carlo simulation.

  Also, the distribution parameter setting unit 5 sets the value of the distribution parameter using the LSS database 22 in accordance with the input of the ion implantation condition 49 specified by the user, and distributes the distribution parameter to the screen configuration unit 7 for Monte Carlo simulation. A processing unit for inputting a parameter value.

  The distribution generation unit 6 is a processing unit that generates an ion implantation distribution that reflects the value of the distribution parameter specified by the distribution parameter setting unit 5 using the LSS theory.

  The Monte Carlo screen configuration unit 7 configures a screen for displaying the result of the Monte Carlo simulation 8 that predicts the track of ions implanted into the substrate based on the value of the distribution parameter specified by the distribution parameter setting unit 5. It is a processing unit. The Monte Carlo screen configuration unit 7 can adjust in advance the distance units in the depth direction and the lateral direction from the substrate surface in the screen so that the entire track of ions can be easily seen.

  The Monte Carlo simulation unit 8 is a processing unit that displays a track of each ion predicted by the Monte Carlo simulation in the screen configured by the Monte Carlo screen configuration unit 7. Since the simulation result is displayed in the screen configured by the Monte Carlo screen configuration unit 7, problems such as the screen being too large and difficult to see depending on the simulation result are improved.

  FIG. 3 is a flowchart for explaining processing by the simulator. In FIG. 3, the basic input unit 30 of the input processing unit 3 acquires basic information specifying the ion species and substrate material from the user (step S11). Step S12 or S12-2 is performed according to a user input.

  The first input unit 31 of the input processing unit 3 is a desired distribution related to the ion implantation distribution selected and input by the user when the dose Φ is input by the user and the energy E is not input. A parameter is acquired (step S12). Alternatively, in step S12, the second input unit 32 of the input processing unit 3 is the ion implantation distribution selected and input by the user when the energy E is input by the user and the dose Φ is not input. Get the desired distribution parameters related to.

  Or the 3rd input part 33 of the input process part 3 acquires as the desired ion implantation conditions 49, when the energy E and dose (PHI) are input by the user (step S12-2). In this case, the process proceeds to step S15 where processing by the distribution parameter setting unit 5 is performed.

  Based on the input information acquired from the user in step S12, the ion implantation condition acquisition unit 4 calls the experiment database 21 or the LSS database 22 (step S13), and calculates the energy E or the dose from the desired distribution parameter specified by the user. Φ is evaluated (step S14). An ion implantation condition 47 that specifies the evaluated energy E or dose Φ and basic information, and distribution parameter information 48 that specifies the selected distribution parameter are input to the distribution parameter setting unit 5.

  The distribution parameter setting unit 5 calls the experiment database 21 or the LSS database 22 (step S15), and calculates and sets distribution parameters corresponding to the ion implantation conditions 47 or 49 (step S16).

  Based on the distribution parameters set by the distribution parameter setting unit 5, the distribution generation unit 6 generates an ion implantation distribution, or the simulation unit 8 executes a Monte Carlo simulation via the screen configuration unit 7, so that the display unit 13 It is displayed (step S17). Then, this process ends.

  A method for evaluating the energy E will be described below.

FIG. 4 is a diagram illustrating an example of an experiment database. In FIG. 4, the vertical axis indicates the range projection R _p , the depth variation ΔR _p , the maximum concentration position R _pm , the horizontal axis indicates energy E, and the ion species Os and the substrate material Te are taken as examples. The energy dependence of the projection R _p , the variation ΔR _p in the depth direction, and the maximum density position R _pm is shown.

Thus, the range projection R _p , the variation in depth direction ΔR _p , and the maximum concentration position R _pm showing energy dependence are R _p (E), ΔR _p (E), R _pm (E) based on the LSS theory. Can be reproduced more faithfully. For example, when the range projection R _p is designated, the energy E can be simply obtained from this R _p (E).

When the maximum density position R _pm is selected, the corresponding energy E can be obtained from the following formula (1).

  Next, a method of calculating distribution parameters by the LSS theory and evaluating two energies E related to desired distribution parameter values will be described with reference to FIG.

Figure 5 is a flowchart for explaining the process for evaluating the energy E from the joining position x _j. In the description, the process is simply described as the process performed by the ion implantation condition acquisition unit 4, but when the junction position _xj is selected as a desired distribution parameter, the first output unit 41 or the second output unit 42 performs the same process. Process.

5, the ion implantation condition acquiring unit 4 obtains the energy E ₁ of the following formula (2),
R _p + n ₁ ΔR _p = x _j (2)
The ion implantation distribution in the case of the energy E ₁ is evaluated by the LSS theory, and the junction position x _j1 is evaluated (step S31).

The ion implantation condition acquiring unit 4 obtains the energy E ₂ to be the following formula (3),
R _p + n ₂ ΔR _p = x _j (3)
Evaluate the ion implantation distribution when the energy _{E 2} at LSS theory, to evaluate the joining position _{x j2} (step S32).

Then, the ion implantation condition acquisition unit 4 numerically interpolates from (E ₁ , x _j1 ) and (E ₂ , x _j2 ) obtained from the two constraint conditions in steps S31 and S32 to obtain (E, The energy E that becomes x _j ) is evaluated (step S33). Then, this process ends.

FIG. 6 is a diagram illustrating a processing example in which energy is evaluated from the bonding position. FIG. 6A shows a case where the junction position _xj is selected as the distribution parameter and the value “0.1 μm” is designated. In addition, it is assumed that B 1 × 10 ¹⁵ cm ⁻² and N _c = 1 × 10 ¹⁷ cm ⁻³ are specified. FIG. 6B shows the evaluation result.

In step S31 of FIG. 5, the following equation (4) when the coefficient n ₁ in the above equation (2) is “5”:
R _p + 5ΔR _p = 0.1 μm (4)
Energy E ₁ is obtained from the above, the ion implantation distribution in the case of the energy E ₁ is evaluated by LSS theory, and the junction position x _j1 is evaluated,
E ₁ = 6.5 keV, x _j1 = 0.068 μm
An evaluation result is obtained.

In step S32 in FIG. 5, the following equation (5) when the coefficient n ₂ in the above equation (3) is “1”.
R _p + ΔR _p = 0.1 μm (5)
The energy E ₂ is obtained from the above, the ion implantation distribution in the case of the energy E ₂ is evaluated by LSS theory, and the junction position x _j2 is evaluated.
E ₂ = 18.3 keV, x _j2 = 0.144 μm
An evaluation result is obtained.

In step S33 of FIG. 5, by numerically interpolating (first interpolation),
E ₃ = 11.5 keV, x _j3 = 0.104 μm
The evaluation results made Tokue can acquire the energy E ₃ corresponding to the value closer to the value specified in the joining position x _j selected by the user "0.1μm".

7, in the case of crystalline substrates, is a flowchart for explaining the process for evaluating the energy E from the joining position x _j. In the description, the process is simply described as the process performed by the ion implantation condition acquisition unit 4, but when the junction position _xj is selected as a desired distribution parameter, the first output unit 41 or the second output unit 42 performs the same process. Process.

7, the ion implantation condition acquiring unit 4 obtains the energy E ₁ of the following formula (6),
R _p + n _p1 ΔR _p + n _L1 L = x _j (6)
The ion implantation distribution in the case of the energy E ₁ is evaluated by the QCLSS theory, and the junction position x _j1 is evaluated (step S41).

The ion implantation condition acquiring unit 4 obtains the energy E ₂ to be the following equation (7),
R _p + n _p2 ΔR _p + n _L2 L = x _j (7)
Evaluate the ion implantation distribution when the energy _{E 2} at LSS theory, to evaluate the joining position _{x j2} (step S42).

Then, the ion implantation condition acquisition unit 4 numerically interpolates from (E ₁ , x _j1 ) and (E ₂ , x _j2 ) obtained from the two constraint conditions in steps S41 and S42, thereby (E, The energy E that becomes x _j ) is evaluated (step S43). Then, this process ends.

Next, the case where HiAkirashitsusoAtsu d _a is selected as the distribution parameter. If HiAkirashitsusoAtsu d _a is selected, the first output portion 41 or the second output unit 42 evaluates the energy E which satisfies the following equation (8).

  In this way, the energy E that realizes the distribution parameter selected by the user can be acquired, and ion implantation is performed by the distribution generation unit 6 or the Monte Carlo simulation unit 8 using the LSS theory using the energy E acquired from the distribution parameter. A distribution can be generated.

Furthermore, when performing the Monte Carlo simulation, the distribution parameter setting unit 5 sets distribution parameters (moment parameters) in advance, so that the screen configuration unit 7 sets the ion particle trace screen to the following equation (9). By
R _p + nΔR _p (9)
The state of the entire track of ion particles can be displayed with an appropriate screen size.

  FIG. 8 is a diagram illustrating a screen example. An input screen 51 shown in FIG. 8A is a screen for inputting a distribution parameter, energy or / and dose desired by the user, which is displayed on the display unit 13 by the input processing unit 3. Based on various input information input from the input screen 51, ion implantation conditions are set by the ion implantation condition acquisition unit 4 and the distribution parameter setting unit 5.

  The simulation result 52 shown in FIG. 8B shows an example displayed on the ion particle trace screen in which the screen size is appropriately adjusted by the screen configuration unit 7.

  A distribution generation result 53 shown in FIG. 8C shows an example of the impurity concentration distribution in the depth direction from the substrate surface, generated according to the moment parameter based on the ion implantation conditions.

  As described above, in this embodiment, the user can set a desired distribution parameter instead of setting the ion implantation condition, and the distribution parameter and the ion implantation condition can be easily set by an appropriate screen display. Can be evaluated.

  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 above description, the following items are further disclosed.
(Appendix 1)
An ion implantation condition setting method in which a computer sets ion implantation conditions for simulating an ion implantation distribution, the computer comprising:
A parameter selection procedure that allows the user to select a desired distribution parameter Y related to the ion implantation distribution;
A value setting procedure for allowing the user to set a value of the desired distribution parameter Y;
An ion implantation condition setting procedure for setting ion implantation conditions according to the value of the desired distribution parameter Y;
The ion implantation condition setting method characterized by performing this.
(Appendix 2)
The ion implantation condition setting procedure is as follows:
A distribution parameter calculation procedure for calculating a distribution parameter of ion implantation distribution using LSS theory;
An energy selection procedure for selecting two energies for ion implantation associated with the value of the desired distribution parameter Y;
The ion implantation condition setting method according to supplementary note 1, characterized by comprising:
(Appendix 3)
When the range projection R _p is selected as the desired distribution parameter Y by the parameter selection procedure, the ion implantation condition setting procedure uses R _p (E) indicating the energy dependence of the range projection R _p. The ion implantation condition setting method according to claim 1, further comprising an energy acquisition procedure for acquiring the energy E of ion implantation from the value.
(Appendix 4)
In the ion implantation condition setting procedure, when the maximum concentration position R _pm is selected as the desired distribution parameter Y by the parameter selection procedure,
From the ion implantation condition setting method according to Supplementary Note 1, wherein it has an energy acquisition procedure for acquiring the energy E corresponding to said maximum concentration position R _pm.
(Appendix 5)
In the ion implantation condition setting procedure, when the desired distribution parameter Y is the junction position _xj ,
_First , an ion implantation distribution in the case of energy E ₁ where R _p + n ₁ ΔR _p = x _j is evaluated by the LSS theory, and a junction position x _j1 obtained as a first constraint condition from the evaluated ion implantation distribution is evaluated. Evaluation procedure;
The ion implantation distribution in the case of energy E ₂ where R _p + n ₂ ΔR _p = x _j is evaluated by the LSS theory, and the junction position x _j2 obtained as the second constraint condition is evaluated from the evaluated ion implantation distribution. Evaluation procedure;
A third evaluation procedure for evaluating an energy E that is numerically (E ₂ , x _j ) from (E ₁ , x _j1 ) and (E ₂ , x _j2 ) obtained from the first and second constraints; ,
The ion implantation condition setting method according to supplementary note 1, characterized by comprising:
(Appendix 6)
The ion implantation condition setting procedure, if the desired distribution parameter Y is bond location x _j in the crystal substrate,
R _p + n _p1 ΔR _p + n _L1 The ion implantation distribution in the case of energy E ₁ where L = x _j is evaluated by the QCLSS theory, and the junction position x _j1 obtained as the first constraint condition from the evaluated ion implantation distribution is evaluated. A first evaluation procedure to
R _p + n _p2 ΔR _p + n _L2 The ion implantation distribution in the case of energy E ₂ where L = x _j is evaluated by the QCLSS theory, and the junction position x _j2 obtained as the second constraint condition from the evaluated ion implantation distribution is evaluated. A second evaluation procedure to
A third evaluation procedure for evaluating an energy E that is numerically (E ₂ , x _j ) from (E ₁ , x _j1 ) and (E ₂ , x _j2 ) obtained from the first and second constraints; ,
The ion implantation condition setting method according to supplementary note 1, characterized by comprising:
(Appendix 7)
The ion implantation condition setting procedure, the case desired distribution parameter Y is HiAkirashitsusoAtsu d _a,
The method for setting ion implantation conditions according to appendix 1, wherein energy E satisfying the above is evaluated.
(Appendix 8)
The computer
8. The distribution generation procedure for generating an ion implantation distribution is further executed by LSS theory or Monte Carlo simulation using the energy E that realizes the desired distribution parameter Y. Ion implantation condition setting method.
(Appendix 9)
9. The ion implantation according to claim 8, wherein the distribution generation procedure sets the size of the particle trace screen to Rp + nΔRp based on a predetermined moment parameter when the ion implantation distribution is generated by the Monte Carlo simulation. Condition setting method.
(Appendix 10)
A process / device simulator for executing the ion implantation condition setting method according to any one of appendices 1 to 9.
(Appendix 11)
A computer-readable storage medium storing a program for causing a computer to simulate an ion implantation distribution, the computer comprising:
A parameter selection procedure that allows the user to select a desired distribution parameter Y related to the ion implantation distribution;
A value setting procedure for allowing the user to set a value of the desired distribution parameter Y;
An ion implantation condition setting procedure for setting ion implantation conditions according to the value of the desired distribution parameter Y;
The computer-readable storage medium characterized by performing this.
(Appendix 12)
A computer-executable program for causing a computer to simulate an ion implantation distribution, comprising:
A parameter selection procedure that allows the user to select a desired distribution parameter Y related to the ion implantation distribution;
A value setting procedure for allowing the user to set a value of the desired distribution parameter Y;
An ion implantation condition setting procedure for setting ion implantation conditions according to the value of the desired distribution parameter Y;
A computer-executable program characterized by causing

DESCRIPTION OF SYMBOLS 3 Input processing part 4 Ion implantation condition acquisition part 5 Distribution parameter setting part 6 Distribution generation part 7 Screen structure part 8 Simulation part 11 CPU
12 memory unit 13 display unit 14 output unit 15 input unit 16 communication unit 17 storage device 18 driver 19 storage medium 21 experimental database 22 LSS database 30 basic input unit 31 first input unit 32 second input unit 33 third input unit 41 first One output part 42 Second output part 47 Ion implantation conditions 48 Distribution parameter information 48
49 Ion implantation conditions 100 Simulator

Claims (9)

An ion implantation condition setting method in which a computer sets ion implantation conditions for simulating an ion implantation distribution, the computer comprising:
A basic input procedure for displaying the first input screen of the basic information of the ion species and the substrate material of the ion implantation condition on the input unit, and acquiring the basic information from the user;
When a second input screen of distribution parameters related to the shape of the ion implantation distribution is displayed on the input unit and the user inputs a dose φ, the user inputs values of one or more distribution parameters related to prediction of energy E Value input procedure to get and
The energy E is predicted from the dose φ acquired by the value input procedure and the value of the distribution parameter, the basic information, the predicted energy E, the designated ion implantation condition, the dose φ, and the distribution parameter A predictive output procedure for outputting distribution parameter information specifying a value;
A distribution parameter setting procedure for calculating and setting a distribution parameter corresponding to the ion implantation condition based on the ion implantation condition and the distribution parameter information output from the prediction output procedure;
A distribution generation procedure for generating an ion implantation distribution based on the distribution parameter set by the distribution parameter setting procedure is executed. The distribution parameter setting procedure includes:
A distribution parameter calculation procedure for calculating a junction position which is a distribution parameter of ion implantation distribution using LSS theory;
An energy selection procedure for selecting two energies for ion implantation associated with the junction location;
The ion implantation condition setting method according to claim 1, wherein: In the distribution parameter setting procedure, when the range projection R _{p is} acquired as the distribution parameter by the value input procedure, R _p (E) indicating the energy dependence of the range projection R _p is used to calculate ions from the values. 2. The ion implantation condition setting method according to claim 1, further comprising an energy acquisition procedure for acquiring the energy E of the implantation. In the distribution parameter setting procedure, when the maximum density position R _pm is selected as the distribution parameter by the value input procedure,
The ion implantation condition setting method according to claim 1, further comprising: an energy acquisition procedure for acquiring energy E corresponding to the maximum concentration position R _pm . In the distribution parameter setting procedure, when the distribution parameter is the joint position x _j ,
_First , an ion implantation distribution in the case of energy E ₁ where R _p + n ₁ ΔR _p = x _j is evaluated by the LSS theory, and a junction position x _j1 obtained as a first constraint condition from the evaluated ion implantation distribution is evaluated. Evaluation procedure;
The ion implantation distribution in the case of energy E ₂ where R _p + n ₂ ΔR _p = x _j is evaluated by the LSS theory, and the junction position x _j2 obtained as the second constraint condition is evaluated from the evaluated ion implantation distribution. Evaluation procedure;
A third evaluation procedure for evaluating an energy E that is numerically (E, x _j ) from (E ₁ , x _j1 ) and (E ₂ , x _j2 ) obtained from the first and second constraints;
The ion implantation condition setting method according to claim 1, wherein: It said value input step, when the user enters the dose phi, selectively to the user, the projection projected range R _p, the maximum concentration position R _pm, joining position x _j, and HiAkirashitsusoAtsu d _a 1 Get the above values by inputting them,
By the distribution parameter setting procedure, and the projection R _p projected range, and the variation [Delta] R _p the depth direction, and skewness gamma, and kurtosis beta, and lateral variation [Delta] R _pt, and the dose [Phi, as the distribution parameter The ion implantation condition setting method according to claim 1, wherein the ion implantation condition is set.   A process / device simulator for executing the ion implantation condition setting method according to any one of claims 1 to 6. A computer-readable storage medium storing a program for causing a computer to simulate an ion implantation distribution, the computer comprising:
A basic input procedure for displaying the first input screen of the basic information of the ion species and the substrate material of the ion implantation condition on the input unit, and acquiring the basic information from the user;
When a second input screen of distribution parameters related to the shape of the ion implantation distribution is displayed on the input unit and the user inputs a dose φ, the user inputs values of one or more distribution parameters related to prediction of energy E Value input procedure to get and
The energy E is predicted from the dose φ acquired by the value input procedure and the value of the distribution parameter, the basic information, the predicted energy E, the designated ion implantation condition, the dose φ, and the distribution parameter A predictive output procedure for outputting distribution parameter information specifying a value;
A distribution parameter setting procedure for calculating and setting a distribution parameter corresponding to the ion implantation condition based on the ion implantation condition and the distribution parameter information output from the prediction output procedure;
A computer-readable storage medium that executes a distribution generation procedure for generating an ion implantation distribution based on the distribution parameter set by the distribution parameter setting procedure. A computer-executable program for causing a computer to simulate an ion implantation distribution, comprising:
A basic input procedure for displaying the first input screen of the basic information of the ion species and the substrate material of the ion implantation condition on the input unit, and acquiring the basic information from the user;
When a second input screen of distribution parameters related to the shape of the ion implantation distribution is displayed on the input unit and the user inputs a dose φ, the user inputs values of one or more distribution parameters related to prediction of energy E Value input procedure to get and
The energy E is predicted from the dose φ acquired by the value input procedure and the value of the distribution parameter, the basic information, the predicted energy E, the designated ion implantation condition, the dose φ, and the distribution parameter A predictive output procedure for outputting distribution parameter information specifying a value;
A distribution parameter setting procedure for calculating and setting a distribution parameter corresponding to the ion implantation condition based on the ion implantation condition and the distribution parameter information output from the prediction output procedure;
A computer-executable program for executing a distribution generation procedure for generating an ion implantation distribution based on the distribution parameter set by the distribution parameter setting procedure.