JP2964808B2 - Simulation method of impurity diffusion process - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

The present invention relates to a semiconductor manufacturing process, and more particularly to a simulation method in an impurity diffusion process.

[0002]

2. Description of the Related Art FIG. 5 shows an example of a conventional impurity diffusion simulation method. The method first comprises the step S
At 10, the chemical impurity concentration distribution Cci (r, t0) at time t = t0 and temperature T is initialized. Here, r indicates space coordinates. Next, in step S11, the activation impurity concentration distribution Cai (r, t0) is determined as a function of the chemical impurity concentration distribution Cci and the temperature T. Then, in step S12, a diffusion equation is numerically calculated at intervals of time Δt using the diffusion coefficient D obtained from the activation impurity concentration. This is referred to as t e as in steps S13 and S14.
By repeating the process up to nd, the activation impurity concentration distribution is obtained. 4A and 4B are diagrams showing the concentration distribution in this method. FIG. 4A shows the concentration distribution at the time t1 when the heat treatment is performed at the temperature T1, and FIG. 4B shows the concentration distribution at the time t2 when the heat treatment is subsequently performed at the temperature T2. The density distribution is shown.

[0003]

According to the conventional method of simulating the impurity diffusion process, the activation impurity concentration at an arbitrary time is expressed only by the chemical impurity concentration and the temperature at that time. Cannot take into account the effect of the impurity concentration activated by the heat treatment on the activation impurity concentration at that time, and cannot accurately represent the change in the activation impurity concentration between each step caused by a series of heat treatment processes. There's a problem. An object of the present invention is to provide a method of simulating an impurity diffusion process that can accurately represent a change in the concentration of an activated impurity in a series of thermal processes including heat treatment processes at different temperatures.

[0004]

According to the present invention, the chemical impurity concentration distribution and the temperature at a certain time t and the time t-
The activation impurity concentration distribution at that time is calculated based on the activation impurity concentration distribution existing in Δt, the diffusion coefficient is obtained based on the calculated concentration distribution, and the diffusion equation is integrated. The chemical impurity concentration distribution at time t + Δt is determined, and this is repeated to determine the temporal change in the activation impurity concentration distribution in a series of heat treatment steps.

[0005]

Next, the present invention will be described with reference to the drawings. FIG. 1 is a flowchart of one embodiment of the present invention.
First, in step S1, at time t = t0,
The activation impurity concentration distribution C1ai (r, t0) determined as a function of the chemical impurity concentration distribution Cci (r, t0) and the temperature T.
). Here, r indicates space coordinates. Then, the activation impurity concentration distribution C1ai (r, t0) is compared with the activation impurity concentration distribution Cai (r, t0-Δt) at the time t = t0-Δt immediately before in step S0, and the larger one is determined. Value to select a new activation impurity concentration distribution C2ai
(R, t0) is set. Further, a new activation impurity concentration distribution C2ai (r, t0) and a chemical impurity distribution Cci
From the comparison with (r, t0), the distribution formed by selecting the smaller value is defined as the activated impurity concentration distribution at time t = t0.

FIG. 2A shows a chemical impurity concentration distribution Cci.
(R, t0), the activation impurity distribution C1ai (r, t0) and Cai (r, t0-.DELTA.t). FIG. 2B shows the activation impurity concentration distribution Cai at the calculated time t = t0.
(R, t0) and chemical impurity distribution Cci (r, t0)
Is shown.

Next, in step S2, the diffusion coefficient D
Is determined based on the chemical impurity concentration distribution Cci (r, t0) and the activating impurity concentration distribution Cai (r, t0), and at step S3, the time t = t0
+ Δt chemical impurity concentration distribution Cci (r, t0 +
Δt). FIG. 2C shows the chemical impurity concentration distribution Cci (r, t0 + Δt) and the activation impurity concentration distribution Cai.
(R, t0) and C1ai (r, t0 + Δt).

Similarly, in step S4, the activation impurity concentration distribution Cai at time t = t0 + .DELTA.t
By calculating (r, t0 + Δt) and integrating the diffusion equation, the chemical impurity concentration distribution at the next time is determined. FIG. 2D shows a chemical impurity concentration distribution Cci (r, t).
0 + Δt) and the activation impurity concentration distribution Cai (r, t0 + Δ)
t). The above calculation process is repeated until t = t end as in step S5, thereby expressing the time change of the activation impurity concentration distribution.

FIG. 3 is a diagram showing a density distribution in another embodiment of the present invention. FIG. 3A shows the concentration distribution at time t1 when the heat treatment was performed at the temperature T1 when the method of the present invention was used, and FIG.
2 shows the density distribution in Example 2. This is shown in FIG.
Compared with the concentration distribution in the conventional method shown in (b), the conventional method has a physical disadvantage that the impurity concentration once activated in the first heat treatment is reduced in the next heat treatment, but the method of the present invention is used. This eliminates the problem.

[0010]

As described above, according to the present invention, the activation impurity concentration distribution is calculated from the chemical impurity concentration distribution and the temperature at a certain time, and the activation impurity concentration at the immediately preceding time. Since the diffusion coefficient is calculated and integrated, the chemical impurity concentration distribution at the subsequent time is obtained, and this is repeated to determine the temporal change of the activation impurity concentration distribution. The calculation can be performed without any contradiction, and there is an effect that the change in the concentration of the activated impurity in the thermal process can be accurately expressed.

[Brief description of the drawings]

FIG. 1 is a flowchart illustrating an impurity diffusion simulation method according to the present invention.

FIG. 2 is a schematic diagram showing a temporal change of an impurity concentration distribution corresponding to each step of FIG. 1;

FIG. 3 is a schematic diagram showing an impurity concentration distribution in another example of the present invention.

FIG. 4 is a schematic diagram showing an impurity concentration distribution in a conventional method.

FIG. 5 is a flowchart showing a conventional impurity diffusion simulation method.

[Explanation of symbols]

 Cci Chemical impurity concentration distribution Cai Activation impurity concentration distribution

Claims (1)

(57) [Claims] 1. An activation impurity concentration distribution at a certain time t is calculated based on a chemical impurity concentration distribution and a temperature at a certain time t, and an activation impurity concentration distribution existing at the immediately preceding time t-Δt. Then, a diffusion coefficient is obtained based on the calculated concentration distribution, a diffusion equation is integrated, and a time t + Δ
A method for simulating an impurity diffusion process, comprising: obtaining a chemical impurity concentration distribution at t; and repeating this to obtain a temporal change in an activation impurity concentration distribution in a series of heat treatment steps.