JPH0210461B2 - - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION [Field of Industrial Application] The present invention relates to a pattern inspection device that determines the quality of a pattern to be inspected.

[Conventional technology]

FIGS. 1a and 1b are explanatory diagrams of the principle of a conventional pattern inspection apparatus called a template matching method, respectively. In the figure, 1 and 1' are the patterns to be inspected, respectively, and 2 to 8 are sampling points indicated by + marks, respectively.

In FIG. 1a, it is assumed that the pattern 1 to be inspected is the character 8. At this time, using a template (not shown), sampling points 2 to 8
is determined as shown in the figure, and the presence or absence of a pattern at each point is inspected. For example, if there is a pattern, it is set to logic 1, if there is no pattern, it is set to logic 0, the inspection results at each point 2 to 8 are coded, and from that code it is recognized which known pattern the pattern to be inspected is classified into. .

In FIG. 1a, the inspection result is positive (logical 1) at any of points 2 to 8, and in this case, it is recognized that the pattern to be inspected 1 is the character 8. In FIG. 1b, only point 8 has no pattern (logical 0), and in this case, an algorithm for determining that it is a character 0 pattern is determined in advance, and it is recognized as 0 according to the algorithm.

This type of pattern inspection method using template matching has the advantages that the information to be examined is compressed and small (only the presence or absence of a pattern at a sampling point), and it is easy to classify patterns based on the examination results. For pattern recognition, such as OCR (optical character reader), it is sufficient to classify the unknown pattern to be inspected by determining which of the standard (known) patterns it corresponds to. It is known that it is effective when used in devices.

[Problem to be solved by the invention]

However, when the template matching method is applied to a pattern inspection device, several problems arise. One of them is that when testing the quality of a pattern, we consider a window area that extends the sampling point, and if we judge whether or not a pattern exists within the window area, we can identify a similar but defective pattern as a good item. There is a fatal flaw in that it may cause false judgments. This is because in the binary method of determining whether a pattern exists or not, if a pattern does not exist within the window area, it is determined to be a defective product, but if a pattern exists, but its size is different from that of a non-defective product. This is because if it is, it cannot be determined that it is defective.

In a commonly printed pattern, the area of the pattern portion in the window area varies greatly depending on the location.
Since the pattern portion has characteristics (in this case, size of area), correct pattern inspection cannot be performed unless these characteristics are extracted and measured.

The present invention was conceived in view of the above-mentioned conventional technical circumstances, and therefore, an object of the present invention is to develop it based on the principle of the template matching method and to apply it to general printing methods. It is an object of the present invention to provide a pattern inspection device that can correctly inspect patterns.

[Summary of the invention]

The outline of the present invention is as follows. In the present invention, first, the pattern to be inspected is imaged using a two-dimensional sequential scanning photoelectric conversion device (for example, an industrial television camera), and the obtained time-series electrical signals are processed to determine the quality of the pattern. However, a plurality (generally a large number) of window areas are set within the field of view of a television camera by electrical means or the like, and the features of the pattern portion seen from each window area are quantitatively extracted. On the other hand, the upper and lower threshold ranges are set independently for each window region, and if the extracted feature value is within the range, it is determined as logical 1, otherwise it is determined as logical 0 (this is (referred to as judgment). Next, some of the window areas are grouped, and for each group, the set of primary judgment results for each window area to which it belongs is
A pre-set primary judgment table (this is a table that is created by dividing the field of view into window areas and calculating a set of primary judgment results for each group for each area in the same way for patterns that are good products). The quality of the product is then compared and verified (this is called a secondary determination). This secondary judgment is performed for all groups, and based on the results, the quality of the pattern is comprehensively judged.

Note that for groups determined to be defective in the above secondary determination, correlation determination is performed as the next step, if necessary. The correlation determination is as follows.

Even if the pattern to be inspected is a good product, if it deviates from the predetermined position within the camera field of view, the feature quantity extracted through the predetermined window area may not fall within the predetermined threshold range. As a result, the primary judgment result is defective. However, since the pattern is only slightly shifted to the next side,
If we take the correlation between the feature detected through a given window region and the feature detected through the adjacent window region (in this case, correlation means addition, subtraction, etc.),
The obtained correlation amount falls within a predetermined threshold range, and the first judgment result is good. Based on this idea, we determined appropriate combinations of related window areas,
During this time, arithmetic processing (for example, addition) is performed on each feature quantity, and a primary judgment (this is referred to as a correlation primary judgment) is performed on the processing results. By comparing and collating the set of primary correlation determination results with a preset correlation primary determination table (calculated in advance in the same manner as the previous primary determination table), a secondary correlation determination is performed, and based on the results, Determine pass/fail.

As described above, in the present invention, in addition to the primary determination and the secondary determination, correlation determination is performed as necessary to determine the quality of the pattern to be inspected. Note that the shape, size, etc. of the window area may be determined arbitrarily and appropriately according to the pattern to be inspected, and are not limited to a rectangle or the like. Furthermore, as the feature quantity extracted through the window region, it is of course possible to use the area of the pattern portion within the window region, the circumference of the pattern portion, and others.

〔Example〕

Next, embodiments of the present invention will be described with reference to the drawings.
FIG. 2a is an explanatory diagram showing an example of setting the window area in the present invention, FIG. 2b is an explanatory diagram of an example of the pattern to be inspected, and FIG. 2c is an explanatory diagram of the pattern seen through the window area. It is a diagram.

In these figures, 21 to 27 are rectangular window areas, and 20 is a pattern to be inspected (letter 8). As seen in Figure 2c,
A portion of pattern 20 is visible through each of window regions 21-27.

FIG. 3 is an explanatory diagram showing another example of setting the window area. In the figure, window areas 31 to 36 are set outside the same window areas 21 to 27 as shown in Figure 2a, and window areas 41 to 48 are set inside. The configuration is such that it is possible to take a correlation between the central window regions 21 to 27 and the outer or inner window regions, even if the arrangement is misaligned.

FIG. 4 is a block diagram showing one embodiment of the present invention. In the figure, 50 is a pattern to be inspected;
51 is an industrial television camera (ITV camera), 52
are binarization/pixel division circuits, 53 and 53' are feature extraction circuits, 54 and 54' are counting circuits, 55 is a count storage circuit, 56 is a primary judgment circuit, 57 is a secondary judgment circuit, and 58 is a Correlation judgment necessity circuit, 59 is a correlation calculation circuit, 60 is a correlation primary judgment circuit, 61 is a correlation secondary judgment circuit, 62 is a comprehensive judgment circuit, 63 is a window area generation circuit, 64 is a control circuit, 6
5 is a setting value storage circuit, and 66 is a keyboard.

Next, an overview of the operation will be explained. First, a time-series electric signal obtained by imaging the target pattern 50 with the ITV camera 51 is binarized in the binarization/image division circuit 52 and divided into pixels (dots). Normally, the electrical signals for one screen are 320 in the horizontal X-axis direction and 240 in the vertical Y-axis direction, about 70,000 in total.
Divide into about 1,000 dots. Feature extraction circuit 53
receives the dot signal from the dividing circuit 52 and extracts its features. For example, if the feature is simply the size of the area, we can focus on the white part of the dot signal and the black part of the dot signal, and then calculate the length (number of dots) of the white part of the dot signal during horizontal scanning in the horizontal direction. circuit 5
The area is determined by counting in step 4 (that is, the area is determined by a set of lengths). 53' is a circuit that extracts other features other than area, for example, when detecting the boundary length between white and black, it is a circuit that extracts the boundary points, and the next counting circuit 54' calculates the number of boundary points. The boundary length can be found by counting. Note that since the counting circuits 54 and 54' perform counting operations under the control of the window area signal generated from the window area generating circuit 63, the counting results are the feature quantities extracted for each window area. These are organized by window area number and feature item and temporarily stored in the storage circuit 55. The window area generation circuit 63 is
It is possible to generate and output signals representing a large number of window areas of arbitrary shapes edited based on data inputted from the keyboard 66 to the setting value storage circuit 65.

Now, when the scanning of one field by the ITV camera 51 is completed, the measurement and storage of the characteristic data for each window area is completed. Based on this characteristic data, the following circuits 56 to 65 determine whether the target pattern is good or not. Determine.

The feature data for each window area read out from the storage circuit 55 is passed to the primary determination circuit 56 by an upper limit set value α _ij and a lower limit set value β _ij set for each window area and each feature (where i is the feature , where j indicates the number of the window area), and a primary determination is made as to whether or not it is within the upper and lower limit range (threshold range). The determination processing operation for feature item i will be specifically explained.

The primary determination circuit 56 checks whether the data D _ij in the window area j regarding the feature item i (for example, the size of the area) is within the range of the upper limit setting value α _ij and the lower limit setting value β _ij and The determination is made for the entire window area. The primary judgment result is
If it is within the range of the upper and lower thresholds, it is expressed as logic 1, and if it is outside the range, it is expressed as logic 0. Note that the upper and lower thresholds can be selected appropriately for each window region and each feature item, and these threshold data are input from the keyboard 66 and stored in the set value storage circuit 65. Therefore, the primary determination circuit 56 reads and uses this.

In general, the pattern to be inspected can often be considered to be composed of N small patterns (N is any integer), so each window area is divided into N groups corresponding to N small patterns. It can be summarized.
For example, if the pattern to be inspected is "ABCD", it is considered to consist of four small patterns (each character constitutes a small pattern). Note that the small pattern does not necessarily consist of one character,
It is not composed of one graphic unit, but may be composed of multiple character units.

A combination of grouped window area numbers is input using the keyboard 66 and stored in the setting value storage circuit 65. The secondary determination circuit 57 reads out the grouped combinations of window area numbers from the storage circuit 65, and divides the primary determination results for each window area from the primary determination circuit 56 into groups accordingly. Next, the secondary determination circuit 57 compares the grouped primary determination results with the primary determination table read from the set value storage circuit 65 to determine whether the group is good or bad. Note that the primary determination table is obtained in advance for each target pattern and stored in the set value storage circuit 65.

FIG. 5 shows a primary determination table when the window area set as shown in FIG. 3 is used and the numbers 8 (FIG. 2b) and 7 are used as target patterns. When the target pattern is the number 8, the primary judgment table is coded data [11111110...0] arranged in the order of the window area number, and when the target pattern is the number 7, it is [1110000...0]. One thing will be understood from FIG.

The secondary determination operation by the secondary determination circuit 57 is performed for all groups. If the secondary determination as a group is NG, a correlation determination necessity circuit 58 determines whether or not a correlation determination is necessary. The correlation determination necessity circuit 58 determines that correlation determination is necessary in some cases, and determines that it is not necessary (that is, not) in other cases, according to various preset conditions. When it becomes necessary to determine the correlation,
The correlation calculation circuit 59 calculates the combination of the set window areas (data regarding this combination is also stored in the storage circuit 6).
5), the extracted feature data is calculated (for example, the data in the window area 21 and the data in the window area 31 are added), and the result is output as new data DC _jk .

The new data DC _ij obtained by the calculation is sent to the correlation primary judgment circuit 60, where it is sent to the set value storage circuit 6.
A first correlation judgment is made as to whether or not the upper limit set value αC _jk and the lower limit set value βC _jk read from 5 are within the range, and if it is within the range, logic 1 is output, and if it is outside the range, logic 0 is output. . Such primary correlation determination processing is performed for all specified combinations of window areas.

Next, the correlation secondary determination circuit 61 performs a correlation secondary determination by comparing and collating the correlation primary determination result from the circuit 60 with the correlation primary determination table read from the set value storage circuit 65. The judgment for the characteristic item of the group is good.

Using the window area set as shown in Figure 3,
FIG. 6 shows, as an example, a correlation primary determination table obtained when the number 8 shifts from the relative position to the window area shown in FIG. 2C toward the re-drying area. In FIG. 6, since the pattern 8 has shifted slightly upward, the amount of feature extraction in the window area 21 alone is small, and the data cannot become a logic 1 and remains at a logic 0. On the other hand, the data in the window areas 21 and 31 is It will be understood that the data finally becomes logical 1 based on the sum of the feature amounts. Window areas 24, 27
The same can be said for each. The correlation primary judgment table is also determined in advance and entered on the keyboard 66.
The fact that it is stored in the set value storage circuit 65 according to the above is the same as in the case of the primary determination table described above.

As a result of the above, we ended up performing secondary judgment processing using feature data directly measured through the window area and correlation secondary judgment processing using correlation calculation data obtained by processing the measurement data, and only one of them resulted in a good product. For example, the comprehensive judgment circuit 62 outputs a judgment result that the product is non-defective.

FIG. 7 shows an outline of the operation of the embodiment shown in FIG. 4, and FIG. 8 shows the details of the operation as a flowchart. Note that in FIG. 8, the window area is simply expressed as a window.

In the explanation of the embodiments described above, the shape of the window area is explained as a rectangle, but it may be any other shape such as a quadrangle, a pentagon, an n-sided polygon, a circle, or an annular shape. In particular, the pattern to be inspected is a character. If the window area is a figure rather than a figure, it is important to select a window area with an appropriate shape.

Furthermore, in the explanation of the above embodiment, when performing the correlation determination process, it was explained that only the first correlation determination result is used during the second correlation determination, but in addition to the first correlation determination result, the first determination result may also be referred to. Sometimes it is done. Furthermore, although an example of addition has been described as a correlation operation, depending on the pattern conditions, there may be cases where it is advantageous to take a difference or ratio. Next, in the embodiment described above, the correlation of only the feature item i was taken, but depending on the conditions of the pattern, there may be significant merits in taking the correlation between different feature items. An operational flowchart for such a case is shown in FIG.

In the above description, area and boundary length have been described as characteristic items, but various local surface information such as intersection points, end points, arcuate points, and diagonal lines can be employed.

As mentioned above, in this device, conditions such as the shape, position, and number of window areas, various threshold values, correlation conditions, various judgment conditions, etc. can be freely set in the memory circuit by keyboard input depending on the target pattern. It's becoming like that. Therefore, conventional pattern inspection equipment has significantly different hardware and algorithms depending on the target pattern, making it difficult to mass-produce, but this equipment has significantly higher adaptability to changes in the pattern to be inspected, and The structure is such that it can be mass-produced.

〔Effect of the invention〕

Next, the effects of the present invention will be described.

The window area is not only the place where the pattern component exists as in the conventional case, but also the area 31 to 4 in FIG.
As shown in 8, it can be set even in areas where no pattern exists. Therefore, even in the case of [inspection pattern=defective pattern+α], which was previously impossible, a defect can be easily determined as a defect.

Judgments in each window area are made using independent upper and lower threshold thresholds for each feature item and each window area, so it is possible to check not only the presence or absence of a pattern within the window area, but also changes in the size and shape of the pattern. It can be quantitatively determined, and the determination accuracy can be significantly improved even for slight changes in the pattern. Further, even when the density of the target pattern differs depending on the region, inspection accuracy can be obtained depending on the pattern by adjusting the threshold value.

Since the window areas can be grouped and each group can be judged independently, there is no need to worry about wastage (mistaking a good product as a defective one) even when there are variations (minor positional deviations or density changes) between parts of a non-defective sample. becomes less.

By using not only measured data but also correlation calculation data between different window regions to determine the quality of a pattern, the pattern can be inspected with high precision and stability. In particular, in non-defective samples, the absolute value of the measured value often changes depending on the location, transportation conditions, etc. In such cases, the method of comparing measured values using upper and lower limit values results in a large number of wasted springs or a poor detection rate of defects. However, even under such conditions, ``the relative ratio of the measured values of different window areas remains constant.''
This is because there are often relationships such as ``the sum of the measured values of different window areas is constant'', or ``changes in the pattern become clear when taking the difference between the measured values of different window areas''. , high precision inspection can be performed using such conditions.

When performing a secondary correlation determination, not only the results of the primary correlation determination are compared with those of non-defective products, but also the results of the primary determination of non-defective products under the conditions under which the correlation determination is performed are combined, as mentioned earlier. If you check it, the judgment will be more precise.
In particular, even when the target sample is misaligned, the pattern can be inspected with high precision without wasting time.

When taking a correlation, it may be possible to obtain a significant effect by examining the correlation not only with data of the same feature but also with data of other features (which may be in the same window area). For example, when a correlation is made between the area and the difference in perimeter length, as in the case of stain inspection, the difference between good and bad becomes clear.

By making the shape of the window region substantially arbitrary, it can be applied to various target patterns, and the number of window regions can be reduced, so that high-speed determination can be performed.

By employing local surface information as a measurement feature item, changes such as minute chips in the pattern can be extracted with high precision.

[Brief explanation of drawings]

Figures 1a and b are explanatory diagrams of the principle of a conventional pattern inspection device, Figure 2a is an explanatory diagram showing an example of setting a window area, Figure 2b is an explanatory diagram of an example of a pattern, and Figure 2c is an explanatory diagram of a window area setting example. Illustration of the pattern seen through the area, 3rd
The figure is an explanatory diagram showing another example of setting the window area, Fig. 4 is a block diagram showing an embodiment of the present invention, Fig. 5 is an explanatory diagram of the primary judgment table, and Fig. 6 is an example of the correlation primary judgment table. 7 is a flowchart showing an overview of the operation of the embodiment shown in FIG. 4, FIG. 8 is a flowchart showing details of the operation, and FIG. 9 is a flowchart showing another example of operation. . Code explanation, 1, 1'...pattern, 2-8...
Sampling point, 20...Target pattern,
21-47...Window area, 50...Target pattern,
51... ITV camera, 52... Binarization/pixel division circuit, 53, 53'... Feature extraction circuit, 54,
54'...Counting circuit, 55...Count value storage circuit,
56...Primary judgment circuit, 57...Secondary judgment circuit,
58...Correlation judgment necessity circuit, 59...Correlation calculation circuit, 60...Correlation primary judgment circuit, 61...Correlation secondary judgment circuit, 62...Comprehensive judgment circuit, 63...Window area generation circuit, 64... ...Control circuit, 65...Setting value storage circuit, 66...Keyboard.

Claims (1)

[Scope of Claims] 1. A two-dimensional successive scanning photoelectric conversion device that scans an optical image of a pattern to be inspected and outputs a time-series electrical signal, and divides the electrical signal obtained from the photoelectric conversion device into pixels. A binarization circuit that binarizes and outputs it, means for setting a plurality of window areas within the field of view of the photoelectric conversion device, and inspection within each window area from the binarized output corresponding to each window area. Inspection within each window area by means of measuring a certain feature quantity of the target pattern and whether each measured feature quantity is within a threshold range independently set for each window area. A primary determination means for determining the quality of the target pattern; and a primary determination means for determining the quality of the target pattern; the window areas are grouped into groups, and for each group, the set of primary determination results of the window area to which it belongs is compared with a preset primary determination table; A pattern inspection device comprising: a secondary determination means for determining the quality of a pattern; Means for performing a primary correlation determination based on whether the calculation result is within a predetermined threshold range, and a means for performing a secondary correlation determination by comparing and collating a set of primary correlation determination results with a preset correlation primary determination table. and a correlation calculation/judgment means consisting of;
A pattern inspection apparatus characterized in that, when the result of the secondary determination is negative, the correlation calculation/determination means is activated, and the quality of the pattern to be inspected is determined from the result of the secondary determination of correlation.