JPH0513313A - Aligner - Google Patents

Abstract

PURPOSE:To shorten the exposure time and to speed up processing by a method wherein repeatedly used pattern data is selected from the specified data to be exposed and the selected pattern data is divided into several kinds of specified sizes and the divided data is defined as a candidate block data and then the specified number of highly effective block patterns are extracted out of the candidate block data. CONSTITUTION:A repeatedly used pattern is extracted as pattern data from data stored in a data storing means 3 and then a pattern table is generated at a pattern table generating section 5. From the pattern table, a sorting table is generated at the sorting table generating section 6 and then a matrix table for matrix recognition is generated at a matrix table generating section 7. These tables are put together in a specified region to generate a block table. According to the information from the block table, the desired number of blocks are selectively extracted out of candidata blocks in the order of an effectiveness as a block pattern. The group of the extracted blocks is used as a block pattern. By this method, an appropriate exposure pattern is generated.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an exposure apparatus, and more particularly, to an exposure apparatus which is suitable for use in the field of pattern exposure by a charged particle beam and is suitable for wafer exposure using a transmission mask with an electron beam. The present invention relates to an exposure device that creates exposure pattern data. In recent years, for example, LSI (Large Scale In)
Many exposure apparatuses have been developed which expose a predetermined pattern data with an electron beam or the like through a transmission mask on a wafer of a large-scale semiconductor integrated circuit having a high integration density such as an integrated circuit).

In this exposure apparatus, in order to perform the exposure efficiently, it is necessary to create appropriate pattern data.

[0003]

2. Description of the Related Art As a conventional exposure apparatus of this type, for example, as shown in FIG. 18, an electron beam drawing apparatus is known which directly exposes a wafer, which is a sample, with an electron beam B. The electron beam drawing apparatus can draw an extremely fine pattern corresponding to the beam diameter, and can electrically correct distortion and aberration inside the deflection field, which has the advantage that the accuracy can be increased depending on the control technology. , Is widely used as an LSI development tool and a mask manufacturing tool, and is particularly used for manufacturing a large-scale semiconductor integrated circuit having a high integration density.

However, such an electron beam drawing apparatus draws a pattern on a wafer by a so-called one-stroke writing method using a Gaussian beam or a variable beam using a field emission type or a thermoelectron gun. As the drawing pattern becomes finer and more complicated, the number of shots increases, the drawing time becomes longer, and the throughput decreases.

Further, in drawing with a Gaussian beam, the size of the pattern must be an integral multiple of the beam spot, and there is the problem that it is difficult to draw diagonal lines other than 45 degrees smoothly and accurately. Even in the drawing by the rectangular beam, there is a problem that the pattern cannot be smoothly and accurately drawn in the portion of the oblique line unless the pattern is divided into fine rectangles. As a result, the exposure by the electron beam is not suitable for mass production of fine LSI.

Therefore, in general, in a semiconductor integrated circuit, attention is paid to the fact that many of the basic circuits repeat the same pattern, and a mask (hereinafter, referred to as a block mask) having a large number of transmission holes corresponding to each repeating pattern shape is formed. That is, a block exposure apparatus is provided. In the exposure procedure in such an apparatus, first, an exposure position on a wafer is designated based on a predetermined "placement data", and the designated position is shaped by a transmission hole on a block mask based on a predetermined "pattern data". This is performed by irradiating the generated electron beam, and since shot exposure can be performed for each transmission hole unit, the exposure time can be greatly shortened.

[0007]

However, in such a conventional exposure apparatus, for example, when a plurality of positions of a wafer are exposed by using the same transmission hole, that is, the same pattern data. Information such as the number of exposures, exposure start coordinates, and repetitive exposure pitch is given to the arrangement data in advance as exposure position information, and the same pattern is sequentially formed at multiple positions on the wafer based on the arrangement data. Therefore, there is a problem in that the purpose of block exposure, which attempts to speed up the process by reducing the total number of shots, is impaired depending on how to provide the arrangement data and the pattern data.

That is, when the semiconductor integrated circuit becomes large in scale, the number of exposure patterns is extremely large, for example, 64.
In MDRAM, the number of shots is as large as 4000 × 10 6, but this number of shots changes depending on how the pattern data is divided into blocks, and the total number of shots depends on whether pattern data with high repeatability can be effectively used. Is determined.

[Purpose] Therefore, it is an object of the present invention to provide an exposure apparatus that speeds up processing by generating appropriate exposure pattern data.

[0010]

In order to achieve the above object, an exposure apparatus according to the present invention determines the shape of a transmission hole on a mask based on predetermined block data, and uses a charged particle beam shaped by the transmission hole to determine the shape of the transmission hole. An exposure apparatus that exposes a pattern based on the block data at an exposure position on a sample, wherein a pattern that is repeatedly used is extracted as pattern data from predetermined data to be exposed, and the pattern data is extracted for each predetermined size. A block that is divided into candidate block data, and a high degree of effectiveness is set to the candidate block data in ascending order of the total number of exposure shots to selectively extract a predetermined number of block patterns from the candidate block data. The pattern extracting means is provided.

[0011]

According to the present invention, pattern data that is repeatedly used from predetermined data to be exposed is divided into predetermined block sizes to form candidate block data, and a predetermined number of highly effective block patterns are selected from the candidate block data. An appropriate exposure pattern is generated by being extracted.

That is, by generating appropriate exposure pattern data, the exposure time can be shortened and the processing speed can be increased.

[0013]

DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described below with reference to the drawings. FIG. 1 is a diagram showing an embodiment of an exposure apparatus according to the present invention,
It is a block diagram which shows the schematic structure of a present Example. First, the configuration will be described.

The exposure apparatus of this embodiment is roughly divided into an exposure unit 1 and an exposure data generation unit 2, and the exposure data generation unit 2 is provided with a data storage unit 3 and a block pattern extraction unit 4, and a block. Pattern extraction means 4
Is composed of a pattern table creating section 5, a classification table creating section 6, a matrix table creating section 7, a block table creating section 8, and a block extracting section 9.

The pattern table creating section 5 creates a pattern table by extracting patterns that are repeatedly used from the data stored in the data storage means 3 as pattern data. The classification table creating unit 6 selects a reference pattern serving as a reference for pattern classification from the pattern table created by the pattern table creating unit 5, and divides a pattern having the same shape as the reference pattern or a pattern including the same shape portion. The obtained patterns are classified and a classification table is created.

The matrix table creation unit 7 represents each pattern data of the classification table created by the classification table creation unit 6 by information such as start point coordinates, pitch, number, etc., and creates a matrix table for matrix recognition. To do. The block table creation unit 8 collects each pattern data of the matrix table created by the matrix table creation unit 7 in a predetermined area, and creates a block table including information such as the number of patterns, the number of arrangements, the number of appearances, and the figure shape. To create.

The block extraction unit 9 selectively extracts a desired number of block patterns from candidate blocks in order of increasing effectiveness as a block pattern from the information of the block table created by the block table creation unit 8. Is. Next, the operation will be described. First, in the data of the data storage means 3, for example, as the extraction target range, the cell part of the memory in the LSI pattern, the decoder part,
When there are many repetitive patterns such as in the sense amplifier section, the block pattern extraction unit 4 extracts a pattern region that is highly likely to be a block pattern.

As shown in FIG. 2, the data for one layer of the LSI pattern, that is, the pattern group of the block pattern is called a group, and when the size of the group is equal to or smaller than the block size, the extraction processing is performed. (Hereinafter, the size is represented by □, the group size is represented by G □, the pattern size is represented by P □, and the block size is represented by B □).

That is, the block pattern is extracted from the data for one layer of the LSI pattern, and the block pattern extraction process is performed for each layer.
Data for a chip is obtained, and this operation shortens the data processing time. By the way, the pattern is data described in the shaped representation. Hereinafter, FIGS.
The processing operation will be described in detail based on the flowchart shown in FIG.

As shown in FIG. 3, the extraction processing by the block pattern extraction means 4 is preprocessing (pattern table creation), pattern classification processing (classification table creation), matrix recognition processing (matrix table creation), and blocking processing. (Block table creation), group grouping processing (block pattern extraction), post-processing (block pattern determination), Steps 1 to 6 described above. The above processing is the pattern table creation unit 5, and the processing is the classification table creation unit. 6,
The process of (1) is executed by the matrix table creating unit 7, the process of (2) is executed by the block table creating unit 8, and the process of ,, is executed by the block extracting unit 9.

[Preprocessing] FIG. 9 is a diagram for explaining the preprocessing (pattern table creation). First, as shown in FIG. 4, the pattern table creating unit 5 extracts a pattern that is repeatedly used from the data in the group stored in the data storage unit 3 as pattern data, and the pattern data is sorted based on the starting point coordinates. (Step 11).

Next, each point other than the starting point of the pattern is defined by relative coordinates from the starting point, and P □ is defined by a min-max value with the starting point as the origin. Is P □ less than or equal to B □? A pattern table is created by setting a certain flag (step 12). And G
If □> B □, the process is moved to the pattern classification process (classification table creation), and if G □ ≦ B □, the process is moved to the block formation process (block table creation) (step 1).
3).

[Pattern Classification Processing] FIG. 10 is a diagram for explaining pattern classification processing (classification table creation). First, as shown in FIG. 5, the classification table creating unit 6 checks whether or not there is an unprocessed pattern in the pattern table created by the pattern table creating unit 5 (step 21), and there is an unprocessed pattern. In this case, the sizes of P □ and B □ are compared (step 2
2) In the case of P □ ≦ B □, the pattern that first appears in the pattern table is within the size of B □ as the reference pattern. If P □> B □, that is, the size of B is B. If there is nothing within □, the unprocessed pattern that appears first in the pattern table is set as the reference pattern (step 23).

Next, the pointer of the reference pattern, that is, the address of the pattern table and the starting point coordinates are stored as information in the classification table (step 24), and it is determined whether the processing of the next pattern data is unprocessed. (Step 25). As a result of the determination, if it is unprocessed, it is checked whether or not it has the same shape as the reference pattern (step 26). Step 27), and if it is not included, the process returns to the process from step 25.

Here, when a portion having the same shape as the reference pattern is included, that is, when a pattern larger than B □ includes the reference pattern, the portion having the same shape as the reference pattern is separated and divided, The table is updated (step 28). Then, this updated data is similar to the case where the unprocessed pattern in the process of step 26 has the same shape as the reference pattern,
The start point coordinates of the same shape pattern are stored in the classification table, the coordinate values including the start point of the reference pattern are sorted, and the processed flag is set in the pattern table of the pattern in which the coordinate values of each point other than the reference start point are the same. (Step 29).

As described above, the processes of steps 25 to 29 are performed for all the patterns in the pattern table, and when there are no unprocessed patterns in the pattern table, the processes from steps 25 and 21 to the matrix recognition process (matrix table creation) are performed. Is transferred. [Matrix Recognition Processing] FIGS. 11 to 14 are diagrams for explaining the matrix recognition processing (matrix table creation).

First, as shown in FIG. 6, the starting point coordinates (FIGS. 11 and 12) in the classification table created by the matrix table creating section 6 by the matrix table creating section 7 are described.
Is checked, data having the same Y-coordinate value is checked for data having equal X-coordinate pitches, which are expressed by the pitch and the number, and are displayed in a matrix table as shown in FIG. Stored (step 3)
1).

Next, based on the information stored in the matrix table shown in FIG. 13, Y is selected from the data having the same X coordinate value.
It is checked that the coordinate pitches are evenly spaced, and expressed by the pitch and the number, as in step 31 described above.
As shown in FIG. 14, the matrix table is completed (step 32). [Blocking Process] FIGS. 15 and 16 are diagrams for explaining the blocking process (block table creation).

First, as shown in FIG. 7, the block table creating unit 8 causes the matrix table creating unit 7 to operate.
From the information of the matrix table created by the above, patterns having the same pitch and the same number of X and Y and having the patterns within B □ are grouped (step 41), and the arrangement number in the X direction (XN). The number within which the pattern existence range is within B □ is obtained in order from the largest divisor (step 42).

Specifically, taking the case of (1) and () in FIG. 15 as an example, the number of arrangements of (1) and (3) in the matrix X direction (XN =
In the case of 4), the divisor becomes 4, 2, 1 in descending order, and in this case, the number within B □ becomes 2. The Y-direction arrangement number (YN) is similarly grouped and stored in the block table (step 43).

Similar to the process of step 42 described above, FIG.
Taking the cases of (1) and () in 5 as an example, when the number of matrix arrangements in the Y direction (YN = 2) of (1) and (2), the divisors are in descending order of 2, 1, and in this case, the number within B □. Is 1. In the same way, the four patterns of ◆ are also grouped together. Next, from the information of the block table, patterns having the same pitch and the same number of X and Y and having patterns within B □ (for example, ◆ and ★ in FIG. 15) are collected (step 44). ), As the information at the time of determining the block pattern, for example, the number of patterns, the number of arrangements, the number of repeated appearances, the figure shape (rectangle, fine figure, arbitrary angle figure, curved figure), etc. are stored in the block table as shown in FIG. It is stored (step 45).

Then, the above matrix recognition processing and blocking processing are repeated until the data of the classification table is completed. [In-group grouping process] FIG. 17 is a diagram for explaining the in-group grouping process (block pattern extraction).

First, as shown in FIG. 8, the block extracting unit 9 divides a pattern larger than the size of the block table created by the block table creating unit 8 so as to fit within B □ (step 51). ),
From the information in the block table, if the pitch and the number are the same for both XY, the patterns existing within B □ are grouped (step 52).

By the way, the block table is completed by performing the above-mentioned preprocessing, pattern classification processing, matrix recognition processing, blocking processing, and group grouping processing on the data of all groups. [Post-Processing] As shown in FIG. 8, the block extracting unit 9 selects a more effective block exposure from the information of the block table created by the intra-group grouping process, and determines the block pattern for one layer. (Step 61).

As described above, in the present embodiment, the pattern that is repeatedly used, such as the cell portion of the memory, is divided from the obtained patterns while considering the block size, and is extracted as a candidate block pattern. . And
By effectively and efficiently extracting the finally determined number of block patterns from the information on the candidate block patterns, excellent effects in terms of speed and image quality can be obtained, as described below.

That is, in terms of speed, if pattern block data is created, a beam having a pattern shape can be formed by block exposure, and several tens of shots are required for a Gaussian beam or a variable rectangular beam. Even a pattern can be exposed with one shot.
If the pattern can be formed in the block, the miniaturization does not directly affect the throughput.

For example, the size of the block on the sample surface is 5 μm.
When the length is within m, if the pattern rule is 0.3 μm, several tens of memory cells can be simultaneously extracted as one block pattern. In terms of image quality, in block exposure, the pattern is 100 times to 100 times on one mask.
It is possible to manufacture and transfer at a magnification of 0 times, and it is possible to transfer a pattern of any shape with high accuracy without limitation on the pattern size and angle.

Therefore, the electron beam graphic selective exposure is
Higher speed and higher image quality than the conventional electron beam exposure method,
In this embodiment, a pattern group of a small area that is frequently used in the LSI pattern is extracted as a block, and this block is created as aperture pattern data on the stencil mask, thereby It becomes possible to draw an LSI pattern in the graphic selective exposure, and mass production of a fine LSI is realized.

Specifically, by creating the electron beam figure selective exposure data by the block exposure data extraction of the present invention, for example, high-speed drawing is possible even for a fine pattern of 0.2 μm rule or less, and 256 MDRAM or more. Mass production of large-scale LSI patterns can be realized.

[0040]

According to the present invention, pattern data that is repeatedly used from predetermined data to be exposed is divided into predetermined block sizes to obtain candidate block data, and a block pattern having a high degree of effectiveness is predetermined from the candidate block data. An appropriate exposure pattern can be generated by extracting a number.

Therefore, by generating appropriate exposure pattern data, the exposure time can be shortened and the processing speed can be increased.

[Brief description of drawings]

FIG. 1 is a block diagram showing a schematic configuration of an embodiment of the present invention.

FIG. 2 is a diagram showing an outline of processing of this embodiment.

FIG. 3 is a flowchart for explaining an operation example of the present embodiment.

FIG. 4 is a flowchart for explaining an operation example of creating a pattern table.

FIG. 5 is a flowchart for explaining an operation example of creating a classification table.

FIG. 6 is a flowchart for explaining an operation example of creating a matrix table.

FIG. 7 is a flowchart illustrating an operation example of creating a block table.

FIG. 8 is a flowchart for explaining an operation example of block pattern extraction and block pattern determination.

FIG. 9 is a diagram for explaining preprocessing.

FIG. 10 is a diagram for explaining pattern classification processing.

FIG. 11 is a diagram for explaining matrix recognition processing.

FIG. 12 is a diagram for explaining a matrix recognition process.

FIG. 13 is a diagram for explaining matrix recognition processing.

FIG. 14 is a diagram for explaining matrix recognition processing.

FIG. 15 is a diagram for explaining blocking processing.

FIG. 16 is a diagram for explaining blocking processing.

FIG. 17 is a diagram for explaining an in-group grouping process.

FIG. 18 is a schematic diagram showing a configuration of a main part of a conventional example.

[Explanation of symbols]

1 Exposure means 2 Exposure data generation means 3 data storage means 4-block pattern extraction means 5 Pattern table creation section 6 Classification table creation section 7 Matrix table creation section 8 Block table creation department 9 Block extractor

Claims (2)

[Claims] 1. A shape of a transmission hole on a mask is determined based on predetermined block data, and a charged particle beam shaped by the transmission hole exposes a pattern based on the block data at an exposure position on a sample. An exposure apparatus, in which a pattern that is repeatedly used is extracted from predetermined data to be exposed as pattern data, and the pattern data is divided into predetermined block sizes to form candidate block data. An exposure apparatus is provided with a block pattern extraction unit that sets a high degree of effectiveness in the order of decreasing the total number of exposure shots and selectively extracts a predetermined number of block patterns from the candidate block data. 2. The block pattern extraction means extracts a pattern that is repeatedly used from the predetermined data as pattern data, and creates a pattern table, and a reference serving as a reference for pattern classification from the pattern table. A classification table creation unit that creates a classification table by selecting a pattern and classifying patterns obtained by dividing a pattern having the same shape as the reference pattern or a pattern including the same shape portion, and each pattern data of the classification table A matrix table creation unit that creates a matrix table for recognizing a matrix by expressing information of starting point coordinates, pitch, and number, and collects each pattern data of the matrix table in a predetermined area, Block table consisting of number and figure shape information And a block extraction unit that selectively extracts a desired number of block patterns from candidate blocks in order of increasing effectiveness as block patterns from the information of the block table. The exposure apparatus according to claim 1, wherein: