KR100989746B1 - Optical proximity correction verification method using optical parameter and risk function - Google Patents

Abstract

The present invention relates to an optical proximity correction verification method. The technical problem to be solved is to perform optical proximity correction verification using optical parameters and a risk function, thereby distinguishing types of vulnerabilities in a more statistical manner, and performing effective optical proximity correction verification. To do it.

To this end, the present invention includes an OPC model acquiring step, an original database acquiring step, an OPC performing step of performing OPC using the OPC model and the original database, and an OPC verification step of verifying the OPC. An optical parameter calculation step of calculating an optical parameter, a risk function calculation step of calculating a risk function using the optical parameter, a display step of displaying a value of the risk function and a normal check result value, a value of the risk function, An optical proximity correction verification method using an optical parameter and a risk function consisting of a vulnerability analysis step of analyzing a vulnerability by referring to a value of a normal check result is disclosed.

Optical parameter, risk function, optical proximity correction, optical proximity correction verification

Description

OPTICAL PROXIMITY CORRECTION VERIFICATION METHOD USING OPTICAL PARAMETER AND RISK FUNCTION}

The present invention relates to an optical proximity correction verification method using an optical parameter and a risk function.

In general, to form a pattern used in a photo-lithography process for manufacturing a semiconductor, a reticle is required together with an exposure apparatus, a photoresist film, and the like. A reticle is a disc used to project a repetitive semiconductor circuit pattern on a silicon wafer, and is made of a quartz plate formed of a chromium pattern of 4 to 5 times the size of the reduction projection.

Patterns on these reticles should have the same critical dimensions for the same layout pattern. That is, fidelity of the pattern becomes an important factor in the reticle production. In recent years, as the line width of semiconductor devices decreases, the demand for fidelity increases.

On the other hand, while the wavelength of the light source used in the exposure equipment approaches the minimum feature size of the semiconductor device, the distortion of the pattern begins to appear due to diffraction, interference, or the like. That is, the optical system that projects the image on the reticle onto the wafer acts as a low-pass filter when expressed as Fourier transformation.

Therefore, the edge portion of the pattern, which is a high frequency portion, does not transmit, so that the image formed on the wafer is different from the original shape. In addition, a distortion phenomenon caused by the influence of the adjacent pattern also appears, which is called an optical proximity effect. In order to overcome the distortion of the pattern caused by the optical proximity effect, a method of deliberately changing the reticle pattern, that is, attaching a serif to the edge of the pattern, has been attempted. Hereinafter referred to as 'OPC' method.

Currently, OPC method is verified and verified by trial & error method through simulation and process experiment using commercial simulation tool. Therefore, in order to obtain accurate simulation results, it is essential to manufacture a reticle having an accurate OPC test pattern.

OPC methods include rule-based OPC and model-based OPC, and hybrid OPC is a mixture of both. Depending on which design you handle, you can choose one of two methods, especially rule based optical proximity correction (Rule Based OPC) for patterns with layouts that use repetitive patterns such as memory or SRAM cells. do.

Model based OPC (OPC) is a task of correcting a mask pattern so that an image that matches a target is implemented using an OPC simulation model. As design rules become smaller, the model based OPC becomes increasingly useful.

In the conventional model-based optical proximity correction verification method, a simulation is performed on an entire database, and pinches, bridges, and end-shifts are performed based on the simulation results. Patterning errors such as) are detected in advance.

Therefore, the accuracy of the model is required for accurate prediction. The optical proximity correction verification method performs simulation on all patterns under normal conditions and then verifies whether the optical proximity correction is correct.

As a method of reinforcing this, optical proximity correction and verification using a process window model are performed, but this is not easy to use due to data gathering and long runtime issues for modeling. there is a problem.

In addition, when a process tuning of optical proximity correction for weak points is required, it takes a lot of time, such as performing optical proximity correction and recalibration of optical proximity correction. There is a problem that the around-time is long.

SUMMARY OF THE INVENTION The present invention has been made to overcome the above-mentioned problems, and an object of the present invention is to perform optical proximity correction verification by using optical parameters and risk functions, thereby distinguishing types of vulnerabilities in a more statistical manner, and efficient optical proximity correction. An object of the present invention is to provide an optical proximity correction verification method capable of performing verification.

In order to achieve the above object, an optical proximity correction verification method using an optical parameter and a risk function according to the present invention includes an OPC model obtaining step, an original database obtaining step, and an OPC using the OPC model and the original database. An OPC performing step, an OPC verification step of verifying the performed OPC, an optical parameter calculating step of calculating an optical parameter with reference to the verified OPC, and a risk function of calculating a risk function using the optical parameter A calculation step, a display step of displaying the value of the risk function and the normal check result together, and a vulnerability analysis step of analyzing the vulnerability with reference to the value of the risk function and the value of the normal check result.

In the optical parameter calculation step, the optical parameter may include a slope of an optical image intensity, an edge placement error sensitivity, a mask error enhancement factor, and an optical image intensity control. It may include at least one of the contrast.

In the risk function calculation step, a risk function may be obtained by the following equation.

Where MEEF is the mask error improvement factor, EPE is the edge placement error sensitivity, Slop is the slope in optical image intensity, contrast is the contrast in optical image intensity, and a, b, c and d are integers Weight.

The display of the normal check result value during the display step may be performed by displaying the difference between the threshold image on the target image and the target contour image on the simulation obtained using the OPC model and the original database.

The vulnerability analysis step may be determined as a weak point in the actual process when the critical dimension, which is the normal check result value, is relatively small and the risk function value is relatively large.

In the vulnerability analysis step, when both the critical dimension and the risk function value, which are the result of the normal check, appear relatively small, it may be determined that the OPC process needs to be changed or the OPC vulnerability needs to be improved.

As described above, the present invention can perform the optical proximity correction verification using the optical parameters and the risk function, thereby distinguishing the type of vulnerability in a more statistical manner and performing the efficient optical proximity correction verification.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings, so that those skilled in the art can easily carry out the present invention.

Here, the present invention can be implemented as a software application implemented by a computer, and the present invention will be described in this regard. However, the invention may be implemented in hardware and may also include other modules or functions not described herein.

1 is a sequential explanatory diagram showing an optical proximity correction verification method using an optical parameter and a risk function according to the present invention.

As shown in FIG. 1, an optical proximity correction verification method using an optical parameter and a risk function according to the present invention includes an OPC model obtaining step S1, an original database obtaining step S2, an OPC performing step S3, An OPC verification step S4, an optical parameter calculation step S5, a risk function calculation step S6, a display step S7, and a vulnerability analysis step S8 are included.

Here, the OPC model acquisition step S1, the original database acquisition step S2, the OPC execution step S3, and the OPC verification step S4 are the same as before.

Of course, the OPC model means generated based on the reticle of the sample test pattern and the critical dimension data of the wafer, and the original database means the initial layout before OPC. In addition, performing OPC means performing OPC on the entire original database, and OPC verification means verifying that a pattern having a desired critical dimension can be obtained by simulating the result of OPC.

In the optical parameter calculation step S5, an optical parameter is calculated with reference to the verified OPC.

For example, at least one of slope, edge placement error sensitivity, mask error enhancement factor, and contrast of optical image intensity in optical image intensity. The optical parameter of is calculated.

This will be described in more detail with reference to FIGS. 2 to 4.

2 and 3 are diagrams for describing a slope among optical parameters. 2 actually shows a wafer image, and FIG. 3 shows the optical image intensity for it. In FIG. 3, the X axis is the cutline distance and the Y axis is the optical image intensity. As in Figures 2 and 3, the slope at point S1 is approximately 3.2.

4 is a diagram for explaining an edge placement error (EPE) among optical parameters. In FIG. 4, solid gray is a simulation contour, and clear gray is the actual target.

Here, the edge placement error means a value obtained by dividing the value obtained by subtracting the critical dimension CD 'of the simulation contour by 2 from the critical dimension CD of the target actually targeted. The edge placement error is defined by the following equation.

EPE = (CD-CD ') / 2

In addition, the sensitivity (S) of the edge placement error is defined as the difference between the two resist model thresholds as follows.

S = | EPE (threshold1) -EPE (threshold2) |

On the other hand, the mask error improvement factor (MEEF) is calculated by the following equation.

Where M is a process constant representing the magnification transferred from the mask to the wafer. For example, M is 4 when mask: wafer = 1: 4.

In addition, the contrast is calculated by the following equation.

5 is an example of a histogram illustrating slope distribution among optical parameters.

In FIG. 5, the X axis means an image log slope, and the Y axis means an number ea. In FIG. 5, the slope is most 3.0-3.5.

6 is an example of a histogram showing an edge placement error (EPE) distribution among optical parameters. The X-axis of FIG. 6 means EPE (unit is nm), and the Y-axis means number (ea). In FIG. 6, the EPE has the greatest number of 0 to -20.

 FIG. 7 is an example of a histogram showing a distribution of mask error correction elements (MEEF) among optical parameters. The X-axis of FIG. 7 means MEEF, and the Y-axis means number ea. In FIG. 7, it can be seen that the number of MEEFs having 1.5-2.0 is the largest.

5 to 7 are histograms at the full chip level, and the smaller the slope and the contrast in the histogram, the lower the mask error improvement factor (MEEF) and the edge placement error. The larger the sensitivity S, the weaker the pattern in the process.

Subsequently, in the risk function calculating step S6, the risk function is calculated using the optical parameter as in the following equation.

As mentioned above, MEEF is a mask error improvement factor, EPE is edge placement error sensitivity, Slop is slope in optical image intensity, contrast is contrast in optical image intensity, a, b, c and d Is an integer weight. Here, the weights a, b, c and d are relative and may be set higher than those considered by the person skilled in the art to be important.

Subsequently, in the display step S7, the value of the risk function and the normal check result value are displayed together.

Herein, the values of the risk function may be displayed in order of large values. For example, the value of the risk function may be displayed by dividing into A (danger), B (high weak), C (middle weak), ..., Z (warning), etc. according to the settings of those skilled in the art. Of course, the division is defined by one skilled in the art.

In addition, the normal check result value is displayed along with the value of the risk function. The normal check result value refers to a difference in the critical dimension between the contour image in the simulation obtained using the OPC model and the original database and the target image actually targeted.

Of course, at this time, the value of the risk function and the normal check result value is preferably the same unit to be used in conjunction with each other.

Finally, the vulnerability analysis step (S8) analyzes the vulnerability by referring to the value of the risk function and the value of the normal check result.

For example, when the critical dimension, which is the normal check result value, is relatively small, and the risk function value is relatively large, it is determined as a weak point in the actual process. That is, the weak point is managed as the main monitoring point.

In addition, when both the critical dimension and the risk function value, which are normal check result values, appear to be relatively small, it is determined that the OPC process needs to be changed or the OPC vulnerability needs to be improved.

In this way, the present invention uses the optical parameters and the risk function together, so that those skilled in the art can distinguish the types of vulnerabilities in a more statistical manner and perform efficient OPC verification.

8 is a block diagram illustrating a computer system for implementing an optical proximity correction verification method using an optical parameter and a risk function in accordance with the present invention.

The optical proximity correction verification method using the optical parameter and the risk function according to the present invention may be implemented as a personal computer and a software application mounted thereto, for example, as described above.

In one example, personal computer 10 includes display unit 14, such as cathode ray tube (CRT), liquid crystal display, processing unit 16, and one or more inputs / outputs that allow a user to interact with a software application executed by the personal computer. Device 18 may also be included. In the example shown, input / output device 18 may include a keyboard 20 and a mouse 22, but may also include other peripheral devices such as printers, scanners, and the like. The processing unit 16 may further include a permanent storage device 26 and a memory 28, such as a CPU 24, a hard disk, a tape drive, an optical disk system, a removable disk system, and the like. The CPU 24 may control the persistent storage 26 and the memory 28. Typically, a software application may be stored permanently in persistent storage 26 or may be loaded into memory 28 when the software application is executed by CPU 24. In the example shown, the memory 28 may include a software application 30 related to the optical proximity correction verification method using the optical parameters and the risk function according to the present invention. The software application 30 related to the optical proximity correction verification method using the optical parameters and the risk function may be implemented as one or more software modules executed by the CPU 24. According to the present invention, the optical proximity correction verification method using optical parameters and risk functions may be implemented using hardware and implemented in other types of computer systems such as client / server systems, web servers, mainframe computers, workstations, and the like. May be

What has been described above is only one embodiment for implementing the optical proximity correction verification method using the optical parameter and the risk function according to the present invention, the present invention is not limited to the above-described embodiment, in the claims As claimed, any person having ordinary skill in the art without departing from the gist of the present invention will have the technical spirit of the present invention to the extent that various modifications can be made.

1 is a sequential explanatory diagram showing an optical proximity correction verification method using an optical parameter and a risk function according to the present invention.

2 and 3 are diagrams for describing a slope among optical parameters.

4 is a diagram for explaining an edge placement error (EPE) among optical parameters.

5 is an example of a histogram illustrating slope distribution among optical parameters.

6 is an example of a histogram showing an edge placement error (EPE) distribution among optical parameters.

FIG. 7 is an example of a histogram showing a distribution of mask error correction elements (MEEF) among optical parameters.

8 is a block diagram illustrating a computer system for implementing an optical proximity correction verification method using an optical parameter and a risk function in accordance with the present invention.

Claims (6)

OPC model acquisition step, The original database acquisition step, Performing an OPC using the OPC model and the original database; An OPC verification step of verifying the performed OPC, Calculating an optical parameter by referring to the verified OPC; A risk function calculating step of calculating a risk function using the optical parameter; A display step of displaying the value of the risk function together with the value of a normal check result; Optical proximity correction verification method using the optical parameter and the risk function comprising the vulnerability analysis step of analyzing the vulnerability by referring to the value of the risk function and the value of the normal check result. The method of claim 1, During the optical parameter calculation step, the optical parameter is Including at least one of slope, edge placement error sensitivity, mask error enhancement factor, and contrast of optical image intensity in optical image intensity. Optical proximity correction verification method using an optical parameter and the risk function, characterized in that made. The method of claim 2, The risk function calculation step is an optical proximity correction verification method using an optical parameter and the risk function, characterized in that the risk function is obtained by the following equation.

Where MEEF is the mask error improvement factor, EPE is the edge placement error sensitivity, Slop is the slope in optical image intensity, contrast is the contrast in optical image intensity, and a, b, c and d are integers Weight. The method of claim 1, The display of the normal check result value during the display step is performed by displaying the difference in the critical dimension on the target image of the target image and the simulation contour image obtained by using the OPC model and the original database. Optical proximity correction verification method using The method of claim 1, The vulnerability analysis step The critical dimension, which is a value of the normal check result, appears to be relatively small, If the risk function value is relatively large, Optical proximity correction verification method using an optical parameter and a risk function characterized in that it is determined to be a weak point in the actual process. The method of claim 1, The vulnerability analysis step If both the critical dimension and the risk function value, which are the result of the normal check, appear relatively small, A method for verifying optical proximity correction using optical parameters and a risk function characterized in that it is necessary to change the OPC process or to improve the OPC vulnerability.

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