JPH08139044A - Simulation method in semiconductor manufacturing process, forming method of impurity diffusion region and heat treatment equipment - Google Patents

Abstract

PURPOSE: To provide a simulation method capable of simulation concerning impurity introduction and the impurity concentration distribution of a thermal diffusion region wherein dependency on dosage is considered, while applying a simple and high speed technique. CONSTITUTION: In a semiconductor process, when impurities are introduced in a semiconductor substrate and diffused by heat treatment like RTA, the concentration distribution of impurities is simulated by a process containing at least operation on the basis of dosage of introduced impurities. In the heat treatment process, the dosage of introduced impurities is measured (2), parameters are extracted (3) and operated (5) about the measured dosage, and a sample is formed at need by a heat treatment equipment part (1).

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a simulation method in a semiconductor manufacturing process, an impurity diffusion region forming method, and a heat treatment apparatus. In particular, the present invention relates to a simulation method, a method for forming an impurity diffusion region, and a heat treatment apparatus in a semiconductor manufacturing process in the case of including a step of introducing an impurity into a semiconductor substrate and performing a heat treatment to diffuse the impurity to obtain an impurity diffusion region. is there.

According to the present invention, for example, regarding the redistribution of impurities by the heat treatment after the impurity implantation into the semiconductor which is performed in the semiconductor manufacturing process, the prediction of the impurity concentration distribution by the semiconductor process simulation is improved and the prediction is performed. It can be used as a system for feedback to the process.

[0003]

2. Description of the Related Art Conventionally, when introducing impurities such as ion implantation in a semiconductor manufacturing process, a SIM which is actually measured.
S (Secondary Ion Mass Spec)
It is necessary to confirm the impurity concentration distribution by, for example, trometry measurement. On the other hand, this impurity concentration distribution is being predicted by simulation.

The semiconductor manufacturing process includes a step of activating the impurities injected into the semiconductor by heat treatment. Especially in recent years, heat treatment (RTA: Rapid T
Processes using thermal annealing are well established. It is common to predict the activated impurity concentration distribution by semiconductor process simulation, but in the RTA simulation of the conventional model currently used, the total amount of impurities (dose.
In the present specification, the difference due to the dose may be referred to hereinafter) is not taken into consideration. However, in reality, the state of diffusion differs depending on the dose level, and thus the impurity concentration distribution differs.

That is, in order to introduce the dependency on the total amount of impurities (dose amount) into the conventional simulation method, a file in which several thousand or more parameters used for calculation are set again is prepared individually every time the dose amount is changed. Since it has to be done, it is complicated and difficult to realize, and even if it is realized, it will inevitably require a huge amount of time for calculation.
For example, the simulation using a point defect module is not practical because the calculation is complicated, the calculation time is extremely long, and the parameters required are very large. If heat treatment conditions are added to this, the feasibility becomes even worse.

Conventionally, as described above, since the difference due to the dose of impurities cannot be calculated by the semiconductor process simulator, many experimental results are required every time the process is changed. Since many experimental results are required every time the process is changed in this way, a large amount of parameter values used for the semiconductor process simulation are required for each process at the same time. It was difficult.

Therefore, for impurity introduction (ion implantation) and its thermal diffusion technology, which are indispensable for semiconductor manufacturing technology such as LSI, the dependence of the total amount of impurities (dose) is important, Accurate and simple calculation of the impurity concentration in consideration of the dose dependency cannot be done, and therefore, the ion implantation / heat treatment technology in consideration of the dose dependency is not yet established.

[0008]

SUMMARY OF THE INVENTION The present invention has been made in view of the above circumstances, and it is a simulation method capable of simulating impurity concentration distribution in a thermal diffusion region and impurity introduction in consideration of dose dependency by a simple and fast method. It is an object of the present invention to provide a method for forming an impurity diffusion region and a heat treatment apparatus capable of realizing ion implantation and thermal diffusion in consideration of such dose dependency.

[0009]

According to the invention of claim 1 of the present application, in a case where an impurity is introduced into a semiconductor substrate and a heat treatment is performed to diffuse the impurity to obtain an impurity diffused region, the impurity in the impurity diffused region is obtained. Is a simulation method in a semiconductor manufacturing process, characterized by simulating the concentration distribution of at least by a process including a calculation based on the total amount of introduced impurities, and achieves the above object.

The invention according to claim 2 of the present application is characterized in that the calculation based on the total amount of introduced impurities takes into consideration the influence on the diffusion length of the impurities. A simulation method in a process, which achieves the above object.

According to a third aspect of the present invention, there is provided a method for forming an impurity diffusion region in which an impurity is introduced into a semiconductor substrate by ion implantation, and heat treatment is performed to diffuse the impurity to obtain an impurity diffusion region. The impurity concentration distribution in the diffusion region is simulated by a process including at least a calculation based on the total amount of introduced impurities, and ion implantation and heat treatment of impurities into a semiconductor substrate are performed based on the impurity concentration distribution predicted by the simulation. And a method for forming an impurity diffusion region, which achieves the above object.

According to a fourth aspect of the present invention, there is provided a heat treatment apparatus for introducing an impurity into a semiconductor substrate by ion implantation and performing a heat treatment to diffuse the impurity to obtain an impurity diffusion region. Of the impurity concentration predicted by the simulation device unit based on at least the total amount of introduced impurities and by a process including a calculation considering the influence of the impurity on the diffusion length, and the impurity concentration predicted by the simulation device unit. A heat treatment apparatus characterized by comprising an ion implantation apparatus section and a heat treatment apparatus section for performing ion implantation of impurities into a semiconductor substrate and heat treatment based on the distribution, thereby achieving the above object.

According to the present invention, the total amount of impurities (dose) is added to the above simulation in a semiconductor process design that incorporates a method of calculating and predicting the impurity concentration distribution in a semiconductor substrate with high accuracy by a semiconductor process simulation.
It is possible to use a new calculation formula for high-temperature short-time heat treatment (RTA) in consideration of the dependency of the above, and to calculate and reproduce the impurity concentration distribution based on the experimental results with high accuracy.

Further, a system for performing more accurate semiconductor process design by extracting each parameter value required for calculation from the impurity concentration distribution based on the above experimental results and feeding back the result obtained from the above calculation to the semiconductor process design. Can be embodied as

[0015]

According to the present invention, the experimental result can be well reproduced by newly considering the dependence on the total amount of impurities (dose) in the calculation of the heat treatment (for example, RTA) in the semiconductor process simulation.

Moreover, by arranging the conventional experimental results without conducting new experiments, or by using the above-mentioned calculation formula with a minimum of additional experiments, it is possible to predict in the semiconductor process simulation to a range where there are no experimental results. It becomes possible. Further, it is not necessary to have many parameter values in the semiconductor process simulation for each process, and the calculation can be performed with the minimum parameter value.

As described above, in the semiconductor process, it becomes possible to easily predict the impurity concentration distribution in the semiconductor. Furthermore, this prediction has made it possible to provide a technique for accurately performing ion implantation and heat treatment having a desired impurity concentration distribution.

[0018]

Embodiments of the present invention will be described below with reference to the drawings. However, needless to say, the present invention is not limited by the following examples.

Embodiment 1 In this embodiment, the present invention is applied to a manufacturing process of a MOS semiconductor device or a bipolar semiconductor device which constitutes an LSI.

In the semiconductor manufacturing process of this embodiment, impurities are introduced into the semiconductor substrate and heat treatment (eg RT
In the case of performing A) and diffusing impurities to obtain an impurity diffusion region, the concentration distribution of impurities in the impurity diffusion region is simulated by a process including at least calculation based on the total amount (dose) of introduced impurities. More specifically, as shown in FIG. 1, in the heat treatment process, the total amount of introduced impurities is measured (2), and the parameters are extracted (3) and calculated (5) for this heat treatment. Simulate. In FIG. 1, reference numeral (1)
Indicates a heat treatment apparatus unit, and reference numeral (4) indicates a database.

In the present embodiment, also in the ion implantation process, as shown in FIG. 1, the total amount of introduced impurities is measured (2 '), and parameter extraction (3') and calculation (5 ') are performed for this. Then, the heat treatment was simulated. Reference numeral (1 ') is an ion implanter unit, and reference numeral (4') is a database.

In particular, in the present embodiment, the calculation based on the total amount of introduced impurities takes into consideration the influence on the diffusion length of impurities.

In this embodiment, the simulation result is specifically fed back to the ion implantation / heat treatment process to manufacture a semiconductor. That is, in this embodiment, when an impurity is introduced into a semiconductor substrate by ion implantation, and a heat treatment is performed to diffuse the impurity to obtain an impurity diffused region, the impurity concentration distribution in the impurity diffused region is set to at least the introduced impurity. The simulation is performed by a process including a calculation based on the total amount of the impurities, and the impurity ion implantation into the semiconductor substrate and the heat treatment are performed based on the impurity distribution predicted by the simulation. The apparatus configuration is an ion implantation / heat treatment apparatus in which an impurity is introduced into a semiconductor substrate by ion implantation, and heat treatment is performed to diffuse the impurity to obtain an impurity diffusion region, wherein the impurity concentration distribution in the impurity diffusion region is A simulation device section ((2) to (5) in FIG. 1 and (2 ') to be based on at least the total amount of introduced impurities and predicted by a process including a calculation in consideration of the influence on the diffusion length of impurities)
(5 ')), and an ion implantation apparatus section (1') and a heat treatment apparatus section (1) that perform ion implantation of impurities into the semiconductor substrate and heat treatment based on the impurity concentration distribution predicted by the simulation apparatus section. Prepare

In the process of this embodiment, parameters required for calculation are extracted by the following procedure prior to actual semiconductor manufacturing. This work is performed every time a new sample is prepared, stored as a database, and used for the next sample preparation. It should be noted that the parameters can be obtained from known materials, papers, etc. instead of being based on the prepared sample, and can be put into a database and used as parameters. Therefore, the parameter extraction (3) in FIG. 1 can be omitted.

In this example, the following procedure was performed. The heat treatment apparatus section (1) prepares a sample necessary for extracting parameters required for calculation. The impurity concentration measuring device (2) measures the impurity concentration of the sample prepared in the heat treatment device section (1) (here, a measuring means such as SIMS or SR is used). Based on the above results and the newly created calculation model formula and database (4) (based on past measurement results and papers) shown below, the parameter extraction device (3) extracts the parameters required for the calculation formula. I do. The parameters obtained as a result of the above are introduced into the heat treatment calculation device (5) (further, if necessary, the ion implantation calculation device (5 ′)), and the impurity concentration distribution of the sample produced in the heat treatment device section (1) is predicted and calculated. This heat treatment calculation device (5) is used.

Using the results obtained above, the characteristics of a semiconductor device to be actually manufactured are predicted.

Although this embodiment can be applied to various semiconductor device manufacturing processes, FIG. 2 shows a MOS transistor (MOS: Metal Oxi) to which this embodiment can be applied.
FIG. 3 shows a schematic cross-sectional view of a semiconductor device). In FIG. 2, the impurity concentration distribution of the activated impurity diffusion regions 11 and 12 injected into the substrate 10 (Si substrate such as single crystal Si or other semiconductor substrate) is predicted. In the semiconductor process simulation of this embodiment, not only such a MOS transistor but also a bipolar transistor or the like can be similarly applied, but here, the description will be given by taking a MOS transistor as a representative. In FIG. 2, reference numeral 13 is an insulator such as SiO ₂ forming a gate insulating film, and 14 is poly S.
Gate electrodes 15S, 15G, and 15D made of i or the like are source, gate, and drain extraction electrodes, respectively. In the normal semiconductor manufacturing process, first, impurities are implanted into the semiconductor substrate 10 by ion implantation (impurity implantation), and then heat treatment is performed to activate and re-distribute the impurities to form the impurity diffusion regions 11 and 12. To obtain the desired characteristics.

FIG. 3 shows a schematic diagram of the impurity concentration distribution in the AA cross section of FIG. 2 obtained by the simulation of the conventional method. On the other hand, a schematic diagram of the impurity concentration distribution in the same section obtained by actual measurement is shown in FIG. In each case,
This distribution is for the case where heat treatment is performed after ion implantation. Hereinafter, this impurity concentration distribution will be described as an example.

In the simulation by the analytical method generally used in the past, the dose (total amount of impurities (corresponding to the area of the impurity concentration distribution in FIGS. 3 and 4)) was changed as shown in FIG. In this case, the impurity concentration distribution moves in parallel in the vertical axis direction as shown by the dotted line in FIG.
That is, assuming that the distribution indicated by reference numeral I in FIG. 3 is a medium dose, in the case of a high dose II, it shifts upward in the vertical direction and a low dose I
In the case of II, it shifts vertically downward.

On the other hand, in the experimental results by actual measurement, as shown by the dotted line in FIG. 4, when the dose becomes larger than a certain amount, the width a of the convex portion at the top of the distribution curve centered on the projection range R _p is high. It is known that it becomes smaller at II 'and becomes larger at low dose III' (I 'shows the case of medium dose). Since this occurs at the time of ion implantation and also after the subsequent heat treatment, it becomes a serious problem when simulating the impurity concentration distribution after the heat treatment. With the conventional method, as shown in FIG. 3, this actual behavior could not be simulated. Therefore, in the present embodiment, this is solved and the heat treatment after the ion implantation is performed at a high temperature for a short time (RTA: Rapid Thermal).
A simulator for predicting the impurity concentration distribution after performing the annealing once) according to the change of the dose with higher accuracy than before has been realized.

As described above, in the experimental result of the heat treatment after the ion implantation, as shown by the dotted line in FIG.
Becomes smaller at higher doses and larger at lower doses. Further, it is known that the width b at the bottom of the distribution (broadening in the deep part) becomes the largest at a certain dose, and decreases even if the dose is more or less than that. Therefore, a new calculation model formula is designed so that a and b are changed according to the dose, so that the experimental result can be predicted with high accuracy.

The new calculation model formula will be described below.

The diffusion equation of impurities by the conventional heat treatment is expressed as follows.

[0034]

[Equation 1] ∂C / ∂t = -divJ J = -DgradC (1)

Here, C is the impurity concentration, D is the diffusion constant,
t is a diffusion time (heat treatment time). The RTA calculation gives the diffusion constant D time dependence and is expressed as follows.

[0036]

## EQU2 ## D = D (t) = D ₀ {1 + Kexp (-t / τ)} (2)

Here, τ is a time constant, and D ₀ is a normal diffusion constant without enhanced diffusion (no time dependence). Equation (2)
Relates to FIG. 3b.

Further, FIG. 3A is expressed as follows using the parameters R _p (peak position of distribution) and σ (spread of distribution) at the time of ion implantation.

[0039]

Equation 3] _{a = (Y L × σ-} R p) + (R P -Y U × σ) (3)

Here, Y _L and Y _U are constants.

In the conventional calculation method, equations (1) and (2) are used.
(3) is used to predict the impurity concentration distribution. That is, τ, K, Y _L and Y _U are constant values, and the calculation result is as shown by the dotted line in FIG. Therefore, as shown in FIG. 3, a, b
The width becomes constant regardless of the dose, and the dose dependency as shown in FIG. 4 cannot be calculated.

Therefore, in the present invention, in order to improve the values of a and b in FIG. 3, D and a have a dose dependency. That is,

[0043]

## EQU00004 ## D = D (dose, t) (K = K (dos
e), τ = τ (dose)), a = a (dose),
(Y _L = Y _L (dose), Y _U = Y _U (dose))
And (Dose is dose).

From the experimental results, the dose dependence is given to the equation (2) as follows.

[0045]

(Equation 5)

Further, the dose dependency is given to the equation (3) as follows.

[0047]

A = {(Y _L0 + Y _L1 × log ₁₀ (dose)) × σ−R _P } + {R _P − (Y _U0 + Y _U1 × log ₁₀ (dose)) × σ} Y _L = Y _L (Dose) = Y _L0 + Y _L1 × log ₁₀ (dose) (5) Y _U = Y _U (dose) = Y _U0 + Y _U1 × log ₁₀ (dose)

By using the formulas (1), (4), and (5), the calculation of the impurity concentration distribution after ion implantation and RTA can be predicted in accordance with the change in dose as shown in FIG. Become.

Therefore, by using this simulator, the PN junction depth, the total amount of impurities, and the like, which are related to the characteristics of semiconductors such as LSI, can be predicted more accurately than before.

Next, a specific process will be described. First, the outline of the sample will be described. The following samples were used in this example.

The substrate is high resistance, N-type, plane orientation {10
0}, single crystal Si is used. First, a 10 nm oxide film is formed on the surface of the substrate. Next, BF ₂ is ion-implanted with an implantation energy of 20 keV. The dose is 1 × 10 ¹⁴ , 3 × 10 ¹⁴ , 1 × 10
The four conditions are ¹⁵ , 3 × 10 ¹⁵ atoms / cm ² . For each sample, RTA is performed at 900 ° C. to activate and re-distribute impurities. The diffusion time is 1,
There are five conditions of 3, 10, 30, 100 seconds. 20 samples above (40 including sample immediately after ion implantation)
SIMS measurement is performed on the sample).

For the above samples, equations (1) and (4)
Calculated using (5). K = K (dose), τ = τ
(Dose), Y _L = Y _L (dose), Y _U = Y
_The following values were used for _U (dose).

[0053]

(Equation 7)

Here, dose is atoms / cm ² ,
τ, τ ₀ and τ ₁ have dimensions of s and Y _L , Y _L0 , Y _L1 and Y _U have dimensions of μm.

In the above embodiment, when the ion implantation is very shallow and a in FIG. 4 reaches the surface of the Si substrate, it is difficult to derive the relational expression of Y _U -dose.
Y _U was set to a sufficiently large constant value.

The above relational expressions are shown in FIGS. 5 to 7.
Is. Figure 5 is τ- dose, 6 K- dose, 7 Y _L - shows the dose relationship view, respectively.

Next, according to the present embodiment, the result of calculating the impurity concentration distribution by using the Pearson function considering the dose dependence for the ion implantation and using the above new calculation model for the heat treatment will be shown. It is shown that there is good agreement by superimposing it on the SIMS measurement result, which is the actual measurement. However, for comparison, the result obtained by the conventional calculation using equations (1), (2), and (3) and S
Also shown is an overlay with IMS. Here, 1 × 10 ¹⁵ atoms / cm ² where the effect of the present embodiment is remarkably exhibited.
And the results for two doses of 3 × 10 ¹⁵ atoms / cm ² .

FIG. 8, FIG. 10, FIG. 12, FIG. 14, FIG.
18, FIG. 20, FIG. 22, FIG. 24, and FIG.
(Secondary Ion Mass Spec
(B) and the Pear calculated by the heat treatment calculation device (5) according to the present embodiment, in particular, according to the present embodiment.
It is the figure which piled up the result (A) of a son function.

In addition, in FIGS. 9, 11, 13, 13, 15, 17, 19, 21, 23, 25, and 27, S
It is the figure which piled up the result (B) of IMS and the conventional calculation result (C).

8 and 9, the dose amount is 1 × 10 ¹⁵ a
FIG. 9 is a diagram in which the calculation result (A) of Example 1 and the measurement result (B) of SIMS when the diffusion time is 1 second and the measurement result (B) in the case of toms / cm ² are overlapped (FIG. 8), and FIG. It is the figure which overlapped and showed the calculation result (C) and the measurement result (B) of SIMS (FIG. 9).

Similarly, in FIGS. 10 and 11, the dose amount is 1
As for the case of × 10 ¹⁵ atoms / cm ² ,
Calculation results (A) and S of Example 1 when the diffusion time is 3 seconds
It is the figure which piled up the measurement result (B) of IMS (FIG. 1).
0), and the calculation result (C) of the conventional method and the measurement result (B) of SIMS are overlaid (FIG. 11).

12 and 13, the dose amount is 1 as well.
As for the case of × 10 ¹⁵ atoms / cm ² ,
It is the figure which overlapped the calculation result (A) of Example 1 and the measurement result (B) of SIMS in case the spreading | diffusion time was 10 seconds (FIG. 12), and also shows the calculation result (C) of a conventional method, and SIMS. It is the figure which piled up and showed the measurement result (B) (FIG. 13).

Similarly, in FIGS. 14 and 15, the dose amount is 1
As for the case of × 10 ¹⁵ atoms / cm ² ,
FIG. 15 is a diagram in which the calculation result (A) of Example 1 and the measurement result (B) of SIMS when the diffusion time is 30 seconds are superimposed (FIG. 14), and the calculation result (C) of the conventional method and SIMS are shown. It is the figure which piled up and showed the measurement result (B) (FIG. 15).

In FIGS. 16 and 17, the dose amount is 1
As for the case of × 10 ¹⁵ atoms / cm ² ,
Calculation result (A) of Example 1 when the diffusion time is 100 seconds
And FIG. 17 is a diagram in which the SIMS measurement result (B) is overlaid (FIG. 16), and is a diagram in which the calculation result (C) of the conventional method and the SIMS measurement result (B) are overlaid (FIG. 17). .

18 and 19, the dose amount is 3 × 10.
FIG. 19 is a diagram in which the calculation result (A) of Example 1 and the measurement result (B) of SIMS when the diffusion time is 1 second and the measurement result (B) are overlapped for the case of ¹⁵ atoms / cm ² (FIG. 18), and the conventional method. It is the figure which overlapped and showed the calculation result of (C) and the measurement result of SIMS (B) (FIG. 19).

20 and 21, the dose amount is 3 as well.
As for the case of × 10 ¹⁵ atoms / cm ² ,
Calculation results (A) and S of Example 1 when the diffusion time is 3 seconds
It is the figure which piled up and showed the measurement result (B) of IMS (FIG.
0), and the calculation result (C) of the conventional method and the measurement result (B) of SIMS are overlaid (FIG. 21).

22 and 23, the dose amount is 3 as well.
As for the case of × 10 ¹⁵ atoms / cm ² ,
FIG. 23 is a diagram in which the calculation result (A) of Example 1 and the measurement result (B) of SIMS when the diffusion time is 10 seconds are shown in an overlapping manner (FIG. 22), and the calculation result (C) of the conventional method and SIMS It is the figure which piled up and showed the measurement result (B) (FIG. 23).

24 and 25, the dose amount is 3 as well.
As for the case of × 10 ¹⁵ atoms / cm ² ,
FIG. 25 is a diagram in which the calculation result (A) of Example 1 and the measurement result (B) of SIMS when the diffusion time is 30 seconds are superimposed (FIG. 24), and the calculation result (C) of the conventional method and SIMS are also shown. It is the figure which piled up and showed the measurement result (B) (FIG. 25).

26 and 27, the dose amount is 3 as well.
As for the case of × 10 ¹⁵ atoms / cm ² ,
Calculation result (A) of Example 1 when the diffusion time is 100 seconds
And FIG. 27 is a diagram showing the SIMS measurement result (B) in an overlapping manner (FIG. 26), and is a diagram showing the calculation result of the conventional method (C) and the SIMS measurement result (B) in an overlapping manner (FIG. 27). .

Since the conventional calculation result (C) does not deal with the dependency of the total amount of impurity implantation (dose amount), it is far from the result of SIMS measurement (B) which is an actual measurement value, whereas in the present embodiment. The calculation result (A) of 1 is in good agreement with the actual measurement result. In this embodiment, in particular, the dose is 3 × 10 ¹⁴ at
For the case of the amount exceeding oms / cm ² , a better result was given than the conventional calculation.

According to this embodiment, it is possible to reduce the number of times the impurity concentration is measured by ion implantation / heat treatment. In addition, the introduction of new impurities (calculation model) and the introduction of new impurities (calculation model) in consideration of the total impurity implantation (dose) dependency and the calculation of the impurity concentration were made possible by a simple calculation method.

Moreover, the calculation time can be shortened by applying the conventional simple calculation method.

Further, the parameters required for the calculation have been greatly reduced, and the parameter extraction has been simplified. By reducing the parameters, we were able to reduce the memory in the computer.

Example 2 In Example 1, the impurity concentration measuring devices (2) and (2 ′) and the parameter extracting device which are independent of each other for both the heat treatment apparatus section (1) and the ion implantation apparatus section (1 ′). Although (3) and (3 '), the heat treatment calculation device (5) and the ion implantation calculation device (5') are provided, in this embodiment, as shown in FIG. Impurity concentration measuring device (2), parameter extraction device (3), heat treatment ion implantation calculation device (5) common to the device section (1 ')
Was used. The database (4) was also common. This embodiment also exhibits the same operational effects as the first embodiment.

[0075]

As described in detail above, according to the present invention,
To provide a simulation method capable of simulating the impurity concentration distribution considering the dose dependency and the impurity concentration distribution in the thermal diffusion region by a simple and fast method, and ion implantation taking such dose dependency into consideration. And a method for forming an impurity diffusion region capable of realizing thermal diffusion,
And a heat treatment apparatus can be provided.

[Brief description of drawings]

FIG. 1 is a flowchart of a semiconductor manufacturing process according to a first embodiment.

FIG. 2 is a diagram showing a cross section of a typical MOS transistor to which the first embodiment can be applied.

FIG. 3 is a schematic diagram showing a dose dependence of an impurity concentration distribution by a conventional simulation with respect to the AA cross section of FIG. 2.

FIG. 4 is a schematic diagram showing the dose dependence of the impurity concentration distribution according to the experimental results for the AA cross section of FIG.

FIG. 5 is a parameter τ showing the diffusion time dependence of the diffusion constant.
It is a figure which shows the dose amount dependence of.

FIG. 6 is a parameter K showing the diffusion time dependence of the diffusion constant.
It is a figure which shows the dose amount dependence of.

FIG. 7 is a diagram showing a dose dependence of a parameter Y _L relating to an amorphized region.

FIG. 8 is a calculation result (A) of Example 1 in the case where the dose amount is 1 × 10 ¹⁵ atoms / cm ² and the diffusion time is 1 second.
It is the figure which piled up and showed the measurement result (B) of SIMS.

FIG. 9 is a diagram similarly showing the calculation result (C) of the conventional method and the measurement result (B) of SIMS in an overlapping manner.

FIG. 10 shows a dose amount of 1 × 10 ¹⁵ atoms / cm ² ,
It is a figure which shows the calculation result (A) of Example 1, and the measurement result (B) of SIMS in the case of spreading | diffusion time 3 seconds.

11] Similarly, the calculation result (C) of the conventional method and SIMS
It is the figure which piled up and showed the measurement result (B).

FIG. 12 shows a dose amount of 1 × 10 ¹⁵ atoms / cm ² ,
It is a figure which shows the calculation result (A) of Example 1, and the measurement result (B) of SIMS in the case of spreading | diffusion time 10 seconds in piles.

13] Similarly, the calculation result (C) of the conventional method and SIMS
It is the figure which piled up and showed the measurement result (B).

FIG. 14 shows a dose amount of 1 × 10 ¹⁵ atoms / cm ² ,
It is a figure which shows the calculation result (A) of Example 1, and the measurement result (B) of SIMS in a case where a diffusion time is 30 seconds.

FIG. 15 Similarly, the calculation result (C) of the conventional method and SIMS
It is the figure which piled up and showed the measurement result (B).

FIG. 16 shows a dose amount of 1 × 10 ¹⁵ atoms / cm ² ,
It is a figure which shows the calculation result (A) of Example 1, and the measurement result (B) of SIMS in the case where a diffusion time is 100 seconds.

FIG. 17 Similarly, the calculation result (C) of the conventional method and SIMS
It is the figure which piled up and showed the measurement result (B).

FIG. 18 shows a dose amount of 3 × 10 ¹⁵ atoms / cm ² ,
It is the figure which overlapped and showed the calculation result (A) of Example 1, and the measurement result (B) of SIMS about the case where a spreading | diffusion time is 1 second.

19] Similarly, the calculation result (C) of the conventional method and SIMS
It is the figure which piled up and showed the measurement result (B).

FIG. 20 shows a dose amount of 3 × 10 ¹⁵ atoms / cm ² ,
It is a figure which shows the calculation result (A) of Example 1, and the measurement result (B) of SIMS in the case of spreading | diffusion time 3 seconds.

21] Similarly, the calculation result (C) of the conventional method and SIMS
It is the figure which piled up and showed the measurement result (B).

FIG. 22 shows a dose amount of 3 × 10 ¹⁵ atoms / cm ² ,
It is a figure which shows the calculation result (A) of Example 1, and the measurement result (B) of SIMS in the case of spreading | diffusion time 10 seconds in piles.

23] Similarly, the calculation result (C) of the conventional method and SIMS
It is the figure which piled up and showed the measurement result (B).

FIG. 24 shows a dose amount of 3 × 10 ¹⁵ atoms / cm ² ,
It is a figure which shows the calculation result (A) of Example 1, and the measurement result (B) of SIMS in a case where a diffusion time is 30 seconds.

FIG. 25 Similarly, the calculation result (C) of the conventional method and SIMS
It is the figure which piled up and showed the measurement result (B).

FIG. 26 shows a dose amount of 3 × 10 ¹⁵ atoms / cm ² ,
It is a figure which shows the calculation result (A) of Example 1, and the measurement result (B) of SIMS in the case where a diffusion time is 100 seconds.

27] Similarly, the calculation result (C) of the conventional method and SIMS
It is the figure which piled up and showed the measurement result (B).

FIG. 28 is a flowchart of a semiconductor manufacturing process according to the second embodiment.

[Explanation of symbols]

(1) Heat treatment equipment part (2) Impurity concentration distribution measurement (equipment) (3) Parameter extraction (equipment) (4) Database (5) Heat treatment calculation (equipment) (Heat treatment-ion implantation calculation (equipment)) (1 ') Ion implantation equipment (2 ') Impurity concentration distribution measurement (equipment) (3') Parameter extraction (equipment) (4 ') Database (5') Ion implantation calculation (equipment)

Claims (4)

[Claims] 1. When impurity is introduced into a semiconductor substrate and heat treatment is performed to diffuse the impurity to obtain an impurity diffused region, the impurity concentration distribution in the impurity diffused region is based on at least the total amount of the introduced impurities. A simulation method in a semiconductor manufacturing process characterized by simulating by a process including calculation. 2. A calculation based on the total amount of impurities introduced is
2. The simulation method in the semiconductor manufacturing process according to claim 1, wherein the influence of the impurity on the diffusion length is taken into consideration. 3. A method of forming an impurity diffused region, wherein an impurity is introduced into a semiconductor substrate by ion implantation, and a heat treatment is performed to diffuse the impurity to obtain an impurity diffused region. An impurity characterized by performing a simulation by a process including calculation based on at least the total amount of introduced impurities, and performing ion implantation of impurities into a semiconductor substrate and heat treatment based on an impurity concentration distribution predicted by the simulation. Method of forming diffusion region. 4. A heat treatment apparatus which obtains an impurity diffusion region by introducing impurities into a semiconductor substrate by ion implantation and performing heat treatment to diffuse the impurities, wherein at least impurity concentration distribution of the impurity diffusion region is introduced. And the semiconductor device based on the impurity concentration distribution predicted by the simulation device unit, which is predicted by a process including a calculation that considers the influence of the impurity on the diffusion length. A heat treatment apparatus comprising: an ion implantation apparatus section for performing impurity ion implantation and heat treatment; and a heat treatment apparatus section.