JP2000067797A - Pattern inspection device and its manufacture - Google Patents

Abstract

PROBLEM TO BE SOLVED: To realize a high speed inspection by deciding inspection parameters so that inspection sensitivity with a plurality of inspection optical systems becomes a similar level, based on image quality evaluation results. SOLUTION: When an image is evaluated, an image signal 9 of an evaluation sample or an inspection objective sample is inputted, evaluated with an image evaluation part 33, and an image quality evaluation result is sent to a whole control part 12. The whole control part 12 synthetically evaluates the results of the evaluation similarly conducted in each microscope part, and based on the evaluation results, a processing pattern in each microscope part is decided. An image obtained is inspected, defective information is compared, and fine adjustment of a parameter is conducted. If many small defectives are outputted to the microscope part which is decided that inspection sensitivity is high, an input filter or a threshold value is changed to reduce sensitivity. When many pieces of defective information are outputted to the results of the microscope part where is previously evaluated so that noise is much, the threshold value is raised. As a result of fine adjustment, if the inspection sensitivity in all the microscope part becomes a similar level, inspection is conducted in that condition.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention obtains an image showing the physical properties of an object, such as a semiconductor wafer, a TFT, or a photomask, using an electron beam or light, and uses the image to detect defects in the object. The present invention relates to a pattern inspection apparatus and method for inspecting, and more particularly, to a pattern inspection technique in which inspection is performed at high speed by a plurality of detection optical systems.

[0002]

2. Description of the Related Art Generally, in inspecting a pattern formed on a sample surface, an image of the pattern is detected by applying an optical microscope or an electron microscope, and these images are processed to obtain an image of the surface. A method for detecting a defect is used.

With today's high integration of semiconductor integrated circuit devices, circuit patterns formed on semiconductor wafers are rapidly becoming finer. For this reason, in addition to an optical inspection device conventionally used as a circuit pattern inspection device, a scanning type electronic device can be obtained because it has a high resolution in principle and a difference in electrical properties can be obtained as an image. The necessity of using a microscope (hereinafter, referred to as SEM) is increasing.

As a technique related to this, for example, Japanese Patent Application Laid-Open No. 5-258703 discloses an X-ray mask or a pattern formed on a conductive substrate equivalent to the X-ray mask.
An inspection method and system using M are disclosed.

Japanese Patent Application Laid-Open No. Sho 59-160948 discloses that an electron beam is scanned in one direction and a stage on which a semiconductor wafer is placed is continuously moved in a direction perpendicular to the direction.
There has been disclosed a method of generating an SEM image and using it to inspect a circuit pattern at high speed.

Further, Japanese Patent Application Laid-Open No. 63-218803 discloses that the time for irradiating a semiconductor wafer with an electron beam at the time of image acquisition is precisely controlled so that the charge-up and gradation drift of the semiconductor wafer affect image quality. SE used for inspection
A method for improving the reliability and sensitivity of an M image is disclosed.

[0007]

In today's field of manufacturing semiconductor integrated circuit devices, the size of semiconductor wafers has been increasing in step with the miniaturization of circuit patterns as described above. Therefore, inspection of a circuit pattern requires inspection of a wider area with higher resolution. For this purpose, it is necessary to speed up the inspection.

As described above, Japanese Patent Application Laid-Open No. 59-16094
No. 8 discloses a technique for speeding up a circuit pattern inspection using an SEM image by using both electron beam scanning and continuous movement of a stage. However, even if such a high-speed technique is adopted, the time required for the entire inspection of an 8-inch wafer currently used for manufacturing semiconductor integrated circuit devices is several minutes to several tens of minutes for an optical inspection device. For S
The EM method requires several tens of hours. It is certain that larger 12-inch wafers will be used in earnest in the future, and it is expected that it will be very difficult to cope with the current inspection speed.

The present invention has been made in view of the above points,
The object is to speed up the inspection by dividing the surface of the object to be inspected into a plurality of inspection regions and simultaneously inspecting them using a plurality of detection optical systems.

[0010]

According to the present invention, a plurality of electron optical systems and image processing systems are used to achieve the above-mentioned object. If the inspection area is divided into a plurality of parts and the electron optical system is arranged for each area, and SEM images are generated and processed at the same time, the inspection time can be reduced according to the number of electron optical systems. Can easily be imagined. However, when a plurality of electron optical systems are used, a new problem occurs which is not caused when a single electron optical system is used. However, they cannot take advantage of that. This problem is a variation in inspection sensitivity caused by a difference in characteristics of a plurality of detection optical systems used. In particular, in the SEM type inspection apparatus, the characteristics of the detected image such as resolution are mainly determined by the characteristics of the electron gun. However, in the inspection apparatus using images, the inspection performance is affected by the quality of the image quality. . Therefore, in order to realize an inspection apparatus using a plurality of electron optical systems, it is necessary to make the characteristics of the plurality of detection optical systems uniform. However, it is difficult to manufacture electron sources and detectors with completely uniform characteristics, and since the performance of these varies depending on the adjustment of the optical axis, etc., the characteristics of multiple electron optical systems are comparable. It will be difficult to keep. Even if an electron source having a certain degree of characteristics is used by sorting after manufacturing, etc., the electron source is a consumable item, and another one having different characteristics will be used at the time of replacement.

The present invention solves the problem of sensitivity variation among a plurality of electron optical systems by adjusting image processing parameters and filtering inspection results.

Further, the performance variation due to such a difference in the characteristics of the detection optical system is not limited to the case of the electron optical system. Even in an optical inspection apparatus, the defect detection performance varies due to a difference in detection lens distortion or the like. For this reason, in the case of configuring an inspection apparatus in which a plurality of detection optical systems are similarly introduced, sensitivity variations can be suppressed by the present invention. However, the present invention has high resolution and takes time to generate an image. This has a particularly great effect in an SEM type device.

[0013]

Embodiments of the present invention will be described below with reference to the drawings. FIG. 1 is an overall schematic configuration diagram of an inspection apparatus according to one embodiment of the present invention. This embodiment is an SEM type automatic inspection apparatus having N sets of detection optical systems.

In FIG. 1, reference numeral 1 denotes an SEM unit,
The M unit 1 includes an electron generator 2 including a field emission type electron source,
It comprises a scanning device 3 including an electrostatic deflection system, a converging device 4 including an electrostatic lens system, and a detection device 5 including a semiconductor detector. The SEM unit 1 includes N sets (N = 2 in the example of FIG. 1), and the sample 7 is irradiated with the electron beam 6 converged from each of them. The sample 7 is held on a continuously movable stage 8, and a secondary electron image of the surface of the sample 7 can be obtained by a combination of scanning of the electron beam 6 and movement of the stage 8. The stage 8 can move the sample 7 in a plane.

The obtained secondary electronic image signal 9 is converted into a digital signal by an A / D converter 10 and then input to an image processing unit 11. The image processing unit 11 includes the SEM unit 1
Has the function of detecting a defect and evaluating the image quality by processing the secondary electronic image acquired by the method. The image processing unit 11 includes the same N sets as the SEM unit 1 and has a function of simultaneously processing secondary electronic images input from the respective SEM units.

The image processing results obtained by the N sets of the SEM unit 1 and the image processing unit 11 are input to the overall control unit 12. The overall control unit 12 has overall control of the stage control device 13, the scanning control device 14, and the focus control device 15, and has a function of using these to input a secondary electronic image under optimal conditions. Further, the present automatic inspection apparatus is provided with an input device 16 for receiving an instruction from a user, a display means 17 for displaying a state of the apparatus and an inspection result, and a storage device 18 connected to the overall control unit 12. . The overall control unit 12 controls each unit to input a secondary electron image, and also performs an inspection obtained by using a defect detection result or an image quality evaluation result obtained from the N sets of the SEM unit 1 and the image processing unit 11. A function is provided for adjusting the performance or the quality of the image at the time of review so that it does not differ between the SEM units 1. Details of this adjustment function will be described later.

Next, one of the image acquisition methods according to the present embodiment will be described.
An example will be described with reference to FIGS.

FIG. 2 is an enlarged view of a part of the apparatus configuration of FIG.
Although three-dimensionally displayed, here, an arrangement example in which the number of SEM units 1 is N = 4 is shown. As shown in FIG. 2, the sample 7 is held on a stage 8, and the stage 8 can move in two orthogonal axes directions. A high-speed image input is realized by a combination of the movement of the stage 8 and the scanning of the electron beam 6.

At the time of inspection, the electron beam 6 is scanned in one direction.
The moving direction perpendicular to the scanning direction of the electron beam is referred to as a continuous moving direction, and the moving direction parallel to the scanning direction of the electron beam 6 is referred to as a feed direction. In addition, the stage 8 is correspondingly divided into a continuous moving stage 20 and a feed stage 21. Stage 8
Is controlled by the stage control device 13, and the amount of movement of the stage is detected by the length measuring device 19 shown in FIG.
By controlling the position of the electron beam according to the amount of movement of the stage, it is possible to perform electron beam scanning corresponding to the speed fluctuation of the stage.
At the time of inspection, the image is moved in the continuous movement direction by the continuous movement stage 20 to acquire an image up to the end of the inspection area allocated to each SEM unit, and then the feed stage 21 is sent by an amount corresponding to the scanning width, and is turned back. Continuous movement is performed, and an image within the range of the adjacent scanning width is acquired. Hereafter, this S
The EM part is described as Si (i = 1, 2,..., N).

FIG. 3 shows the SE of the sample 7 viewed from the upper surface of the wafer.
An example of the arrangement of the M section and an inspection area of each SEM section are shown. As shown in FIG. 3, these four sets of SEM parts
(D is the wafer diameter, FIG. 3
D ′ in the figure is a value obtained by subtracting the orientation flat of the wafer from the diameter D. In the case of a notch, D may be used. For simplicity, it will be described here as D).

If each SEM unit 1 is arranged as shown in FIG.
The inspection area by each SEM unit 1 is one quarter of the inspection performed by using a single SEM unit, and the stroke required for the stage is one half. Each SEM unit 1 has a corresponding image processing unit 11 and the image signals 9 obtained by the four sets of SEM units are processed simultaneously, so that the time required for the inspection is reduced by a single SEM unit 1 and image processing unit 11 Is one-fourth that of the case of using, and the inspection can be speeded up.

Similarly, if the N sets of detection optical systems and image processing systems are used, the time required for inspection can be reduced to about N times. Hereinafter, the inspection region will be described as Ri (i = 1, 2,..., N) in correspondence with the SEM unit. Here, in FIG. 1 and FIG.
The image processing units are arranged one by one, but the image processing units are not necessarily one by one if the processing can be performed sufficiently fast with respect to the speed of image detection.

Problems to be solved by the present invention will be described with reference to FIG. FIG. 4 shows an example in which N = 4 sets of SEM units, similarly to FIGS. As shown in FIG.
When the inspection areas of a set of SEM sections are divided and the characteristics of these SEM sections are different from each other, the following problem occurs.

When an inspection is performed using an image of an inspection object as in the automatic inspection apparatus of the present invention, the inspection performance greatly depends on the quality of the image. Here, the image quality refers to resolution, distortion, S / N, etc., and a difference occurs depending on the characteristics of each detection optical system. Therefore, as shown in FIGS. 2 and 3, the automatic inspection apparatus having four sets of SEM units
When the inspection is performed by dividing the sample surface area, each SEM
Due to the variation in the characteristics of the parts, the inspection sensitivity differs for each inspection region.

FIG. 4 shows an example. The rhombus in FIG. 4 indicates the detected defect 22, and the size indicates the area of the defect. If the characteristics of each SEM part are different, FIG.
As shown in (1), the number and size of defects that can be detected are different for each region. In the region R1, a very large number of small defects are detected, while in the region R2, only large defects are detected. The reason for this is that there is a problem that an existing defect is not detected or that a defect is output as a defect (false alarm) even though no defect actually exists. It is caused by the difference in characteristics. The relationship between the image quality and the defect detection result will be described later. As described above, when a plurality of detection optical systems are used at the same time, a stable inspection result cannot be obtained unless a difference in their characteristics is taken into consideration.

Next, one of the pattern inspection methods using an image is described.
An example will be briefly described, and the effect of image quality on defect detection performance will be described. On a wafer of a semiconductor device, there are patterns repeatedly formed in a unit such as a chip or a cell, and a method of comparing actual images of these repeated patterns and detecting a defect from the difference is often used.

FIG. 5 shows an example of a defect detection method. Images of the surface of the sample 7 as shown in a) and b) in FIG.
Acquired by the unit 1. These two images are an inspection part image 23 at the inspection position and an image of a similar pattern in the vicinity thereof (comparison image 24). These grayscale images are compared, and a defect image 25 is obtained from the difference between them. Using a threshold (Th) 27 in the defect image output 26,
A portion where the output exceeds the threshold value 27 is defined as a defect 22. Thus, the difference between the two images can be detected as a defect. Here, the threshold value 27 is the defect output 2
If a value larger than 8 is set, a defect cannot be detected, and if a value smaller than the noise 29 is set, a false alarm will be generated. The larger the margin 30, which is the difference between the defective portion output 28 and the threshold 27, the more stable the inspection can be made, and it is necessary to set such a threshold 27. Usually, the threshold value 27 at the time of the inspection is set so that the operator has a desired sensitivity after actually performing the inspection.

However, the defect image output 26 as described above
Varies depending on the characteristics of the detection optical system. Unless the detection signal amount is small and the inspection image 23 with sufficient contrast cannot be obtained, the defect output 28 cannot be sufficiently obtained. Further, if the resolution of the inspection image 23 is low, the output of the defective portion will also be dull. On the other hand, in an image having a poor S / N, the level of the noise 29 is increased, and the threshold 27 must be set to a large value. In addition, if the images to be compared have different distortions, different portions of the normal portion are compared, so that the images are normal,
The output of the defective image becomes large. For this reason, in the automatic inspection apparatus of the present invention, unless a threshold value corresponding to the characteristic is determined for each detection optical system, there arises a problem that the inspection sensitivity differs for each region as shown in the example of FIG. It will be. However, manually adjusting the inspection sensitivity of each of the plurality of detection optical systems is extremely difficult and requires much time. Therefore, the present invention provides a method for evaluating the characteristics of these detection optical systems and obtaining stable inspection sensitivity using the evaluation results.

A method for reducing the variation in the inspection sensitivity caused by the difference in the detection optical system will be described below.

An example of a method for evaluating the brightness and noise of an image and a method for correcting an image using the results, which are caused by the difference in the detection optical system, will be described with reference to FIGS. 6, 7, 8, and 9. I do. Here, for simplicity, the detection optical system N = 2
Although the case of a pair will be described, the method is exactly the same even if the number of detection optical systems increases. In order to obtain the same level of defect detection performance between different detection optical systems, the brightness of the detected images does not necessarily have to be the same, but the same pattern is not used for displaying an inspection image for visual confirmation. If the display is not performed with the same brightness, the operator may be confused. Therefore, it is desirable that the brightness is the same.

In the case of the SEM type inspection apparatus, the brightness of the detected image depends not only on the secondary electron emission efficiency on the sample surface, but also on the current amount of the irradiated electron beam, the capture efficiency of the detector, and the circuit of the detection system. It is determined by various factors such as gain. Similarly, the magnitude of the noise included in the detected image signal 9 varies depending on the noise of the electron source, the noise of the transmission system, and the like.

FIG. 6 shows a simple method for evaluating the difference in brightness and noise due to the difference in the detection optical system.
This will be described with reference to FIG. In order to evaluate the difference in image brightness, an image of the same sample surface may be detected, and the brightness of the image signal 9 may be evaluated.

Therefore, when an image is obtained using a silicon wafer or the like without a pattern as a sample, a histogram as shown in FIG. 6A is obtained. A histogram can be similarly obtained for each detection optical system. Here, a) and c) of FIG. 6 will be described as processing contents by the SEM unit S1, and b) and d) of FIG. 6 will be described as processing contents by the SEM unit S2. If a sample having no pattern and a uniform surface is used, the value of the detected image signal should be uniform, but actually contains variations due to noise.
As shown in FIGS. 6A and 6B, the brightness and variation of the detected image are different between the SEM units S1 and S2. In order to make the brightness the same in the SEM sections S1 and S2, using the average value of each input image and the value of the dark level (detection value without electron beam irradiation), c) and d in FIG. ) May be performed. That is, I0 (Ii) = (Ii-di) * (m0-d0) / (mi-di) + d0 (i = 1, 2,..., N) (1) Expression Tone conversion by the above expression (1) Should be done.

Here, m0 and d0 are the average value and the dark level after gradation conversion, and may be determined appropriately. For example, if the SEM unit S1 is always used as a reference, m0 = m1 and d0 = d1.
Further, using the average of all SNs from S1 to m0 = Σmi
/ N, d0 = Σdi / N. If the sample indicating the output level close to the dark level is not actually inspected, the dark level value d0 may be a negative value. If an image is detected in a wide range and only necessary portions are extracted by gradation conversion and processed with a reduced data amount, it is possible to cope with various samples having different brightness (for example, , An image is detected with 10 bits, and necessary gradation is converted into 8 bits for use), but in such a case, according to the brightness of the sample to be inspected,
Appropriate values of m0 and d0 may be determined so that the image signal does not deteriorate.

When the secondary electron emission efficiency on the sample surface and the brightness of the detected image have a linear relationship, FIG.
In the method shown in (1), it is possible to realize gradation conversion corresponding to all brightness samples. However, as described above, the brightness of the detected image is not determined only by the secondary electron emission efficiency, but is linear. There is no guarantee. In this case, if the secondary electron emission efficiency of the sample used for evaluation and the sample to be inspected are significantly different, the method of FIG. 6 may not be able to cope sufficiently. In such a case, another gradation conversion parameter adjustment method will be described later.

Further, an example of a method of adjusting the inspection sensitivity using the noise evaluation result will be described with reference to FIGS. As shown in FIGS. 6A and 6B, even when an image of a sample having uniform brightness is detected, the brightness of the detected image varies due to noise. This noise is the standard deviation of the image σ
, And based on the evaluation result, a threshold value in an image obtained by each detection optical system is adjusted. The procedure will be described below.

As shown in the example of FIG. 6, an image of a sample having no pattern is obtained, and gradation conversion is performed so that the brightness becomes almost the same. Next, a standard deviation σi (i = 1, 2,..., N) is obtained for each detection optical system in the image after the gradation conversion. Since the noise component shown in FIG. 5 changes in the same manner as the standard deviation σi, the threshold value Thi (i = 1, 2,...)
.., N) may be changed accordingly. For example, the SEM unit in which the threshold is set is Ss, and the following equation may be used: Thi = (Ths−dd) * σi / σs + dd (2) Here, dd is the difference between the manually set threshold value and the maximum value of the noise, which may be measured at the time of setting the threshold value or an empirically appropriate value may be given.

Using the method of equation (2) makes it possible to set a threshold value according to the noise level of each detection optical system. However, if the threshold value differs for each detection optical system,
The inspection sensitivity may be different for each detection optical system. Therefore, the following expression (3) may be used instead of expression (2) to make the inspection threshold values in all the detection optical systems the same. Thi = (Ths−dd) * σMax / σs + dd (3) where σMax is the maximum value σMax of the noise evaluation result.
= Max (σi). Defect output 28 due to noise
Therefore, the sensitivities cannot be completely matched, but sensitivity variations among the detection optical systems can be reduced.

However, this threshold setting method also poses a problem when the brightness of the image of the sample to be evaluated is significantly different from that of the sample to be inspected, as in the case of the gradation conversion shown in FIG. . As shown in FIG. 7A, usually, noise included in an image has a non-linear characteristic with respect to the brightness of the image. Therefore, for example, two SEM units Ss, Sk
Have noise characteristics as shown in FIG. 7 a), their ratio Ss / Sk as shown in FIG.
Varies depending on the brightness of the input image. For example, assuming that the brightness of a sample subjected to noise evaluation based on the standard deviation σ is Ib in FIG. 7B) and the brightness of an image to be inspected is Ia, the noise relationship between the detection optical systems in Ia is given by I can't know.

The other threshold setting method in this case will be described later. However, if a uniform area made of the same material as the pattern to be inspected is used, the threshold can be set by this method. The same applies to the above-mentioned gradation conversion, but it is sufficient to have an image of about 100 pixels in order to evaluate noise. Therefore, in the case of a semiconductor device, an appropriate area with uniform brightness may be cut out from a relatively sparse pattern of a peripheral circuit and evaluated. The setting of the evaluation position may be manually instructed by the operator, or the image of the peripheral circuit may be processed and divided into regions, and an area having the same brightness as the inspection area may be automatically set. May be selected for evaluation.

In the noise evaluation using the actual standard deviation σ, since the image after the gradation conversion has a high possibility of image deterioration due to quantization, the standard deviation σ is obtained from the image before the gradation conversion. What is necessary is just to convert by multiplying by the gain at the time of gradation conversion. For example, in the case of equation (1), (m0−d0) /
(Mi-di) may be multiplied.

Next, as another method of adjusting the parameters of the gradation conversion, an example using an image of a pattern to be actually inspected will be described with reference to FIG. The sample to be inspected is mainly composed of two different materials, a base b and a pattern p. The histograms of the images are as shown in FIGS. 8A and 8B, respectively.
Has two peaks corresponding to.

Therefore, as shown in FIG. 8, a histogram is obtained in each detection optical system using an image to be inspected. The two maxima I of these histograms
Using bi and Ipi, gradation conversion may be performed in the same manner as in the example shown in FIG. That is, gradation conversion may be performed by the following equation (4). I0 (Ii) = (Ii−Ibi) * (Ip−Ib) / (Ipi−Ibi) + Ibi (4) Equation (4) where Ipi> Ibi. When Ipi <Ibi, Ipi and Ibi are exchanged. Also in this case, FIG.
As in the case of the above example, appropriate values may be set for Ip and Ib.

Another method of performing gradation conversion according to the detection optical system will be described with reference to FIG. FIG. 9 shows an example of the image quality evaluation sample 31. In the example of FIG. 9, a sample including five regions each having a different material is shown. There is no pattern in each area so that a uniform image can be obtained, and the material is conductive and does not change the image due to charging. If a gradation conversion table is created in advance for each detection optical system using the evaluation sample 31, it is not necessary to evaluate the brightness every time the sample is replaced.

As in the above-described examples of FIGS. 6 and 8, the reference brightness of each sample shown in FIG. 9 is first selected by selecting a reference SEM portion or using an average of all the SEM portions. Is determined. Similarly, the brightness Ii of each sample was measured in each SEM part, and the horizontal axis was Ii.
FIG. 9 is plotted as 0 and the vertical axis is Ii. The relationship between these brightnesses depends on the detection system, but an approximation curve of an appropriate function is obtained using a method such as least squares approximation according to the system. Using this curve, it is possible to perform gradation conversion from the image Ii detected by the SEM unit Si to the reference brightness I0.

In the example shown in FIG. 8, each time the sample is replaced, it is necessary to evaluate the image obtained by each detection optical system. However, according to the method of FIG. 9, the same curve is obtained for each SEM unit, and once the gradation conversion table is created, the characteristics of the detection optical system are changed until the characteristics of the detection optical system are changed by replacing parts. The same table can be used. Normally, the evaluation may be performed periodically, and by monitoring the change, an abnormality in the detection optical system can be found. Further, if only the brightness (for example, the average value) can be measured from the acquisition of the image of the sample to be inspected, it is easy to cut out the necessary gradation.

Similarly, for the noise evaluation, the problem described with reference to FIG. 7 can be solved by using the evaluation sample 31 shown in FIG. As with the evaluation of brightness in FIG. 9, the relationship between brightness and noise (FIG. 7) in all gradations is obtained using the evaluation sample 31, and the ratio σi / of the standard deviation between each SEM part and the reference value is obtained. σs can be obtained. The ratio σi / σs of the standard deviation obtained in this way is
By applying the equations (2) and (3), it is possible to easily adjust the threshold value between the detection optical systems even for samples having various secondary electron emission efficiencies.

Next, an example of a method of evaluating the resolution and a method of adjusting the inspection sensitivity based on the result will be described.

Conventionally, the evaluation of the resolution of the SEM has been carried out by humans by visually evaluating gold particles and the like deposited on carbon. However, the apparatus of the present invention does not require the absolute value of the resolution, but simultaneously performs the inspection. It is important to automatically evaluate the difference in resolution between a plurality of SEM units to be used. Therefore, a method for evaluating the resolution using the resolution evaluation sample 32 as shown in FIG. 10, adjusting the image processing parameters based on the result, and adjusting the sensitivity in a plurality of detection optical systems will be described.

In order to evaluate the resolution of each SEM unit, an evaluation sample as shown in FIG. 10 is used. As shown in FIG. 10, an image of a boundary portion of a sample having a remarkable contrast is obtained. The smaller the beam diameter and the better the resolution, the steeper the slope of the image signal at the boundary. Therefore, this gradient can be evaluated by its magnitude using the first derivative. Also, by looking at this first derivative, it is possible to know the position where the electron beam starts scanning the boundary portion and the position where the electron beam ends scanning. For example, the half-width of a curve drawn by the first derivative as shown in FIG. 10 can be used as the beam diameter of the electron beam.

As another resolution evaluation method in the example shown in FIG. 10, an image of a fine striped pattern is obtained.
A method of evaluating based on the amplitude or a method of acquiring images of two slits or spot-like adjacent patterns and evaluating the degree of separation can be considered. Further, in order to obtain a remarkable contrast in the resolution evaluation sample 32, a material having a large difference in atomic numbers, for example, tantalum and silicon may be selected. FIG.
Since the same evaluation can be performed for the resolution evaluation sample 32 shown in FIG. 9 by using a part of the image quality evaluation sample 31 shown in FIG. 9, the contrast becomes remarkable, for example, in the regions A and B in FIG. Such materials may be arranged and used for evaluating the resolution.

Next, the resolution evaluation sample 32 shown in FIG.
A method of suppressing a variation in inspection sensitivity due to a difference in resolution of the detection optical system will be described using a result of evaluating the resolution using the method.

When performing defect detection by image processing, a filter having a function such as smoothing may be used at the time of image input for the purpose of removing noise components and enhancing edges. Therefore, based on the result of the resolution evaluated in the previous example, the resolution of the image obtained by each detection optical system is increased by using a filter having a higher smoothing effect on the input image of the detection optical system having a high resolution. It can be adjusted to be about the same. At this time, since it is not possible to improve the one with the poor resolution, the resolution is evaluated, and processing is performed to match the one with the worst resolution among the plurality of SEM units.

For example, the half-width of the first derivative shown in FIG. 10 is evaluated as a beam diameter, and the beam diameter of each SEM part is set to dbi (i = 1, 2,..., N). Of the beam diameter evaluation result of the SEM part
w (i = w). Considering the case where a Gaussian filter generally used as a smoothing filter is used, the sensitivity is adjusted by changing the magnitude of the standard deviation which is a parameter of the filter. Assuming that the standard deviation of the input filter in the SEM section Si is Fi, the parameters of the filters of the other SEM sections may be determined by the following equation (5). Fi = Fw * dbw / dbi (5) Here, Fw is set in advance with a filter sufficient for noise removal.

As described above, when the resolution is adjusted using the input filter, the noise characteristic changes due to the effect of smoothing the input filter. For this reason, when performing the above-described noise evaluation, it is possible to use the image after passing through the input filter, or the effect of noise removal by the input filter can be estimated by the square root of the sum of squares of the filter coefficients. The threshold value may be determined in consideration of this.

As another method for reducing the variation in resolution, there is a method in which an inspection is performed on an image as it is and the result is filtered. As shown in FIG. 5, a defect can be detected by image processing. At this time, not only the position of the defect but also information such as its shape and size can be obtained at the same time. Therefore, if the inspection result obtained by the optical system with high resolution is filtered by the size of the defect according to the output of the optical system with low resolution from the detected defect information, even if the resolution of each optical system is different, Since the minimum size of the detected defect, that is, the inspection sensitivity is constant, it is possible to suppress the variation in the inspection result as shown in FIG. The minimum defect size may be set to a desired sensitivity by the operator, and the minimum value may be determined as a limit of the sensitivity that is guaranteed at least even if there is a variation in the apparatus. In this case, since the evaluation of the resolution is not particularly required, if the evaluation of the brightness and the noise is performed using the sample to be inspected, it is not necessary to prepare a special sample for the evaluation.

The outline of the inspection procedure will be described. FIG.
1 is an enlarged view of a part of FIG. 1 and shows the inside of the image processing unit 11.

At the time of image quality evaluation, an image signal 9 of a sample for evaluation or a sample to be inspected is input, evaluation is performed by an image quality evaluation section 33, and an image quality evaluation result 34 is passed to the overall control section 12. The overall control unit 12 comprehensively evaluates the results of performing the same evaluation for each SEM unit Si, and determines the processing parameters of each SEM unit from the result. When the resolution is evaluated and the input filter is changed, the filter coefficient 35 to be set is set in the preprocessing unit 36. Preprocessing unit 3
In step 6, processing such as filter processing at the time of input and image distortion correction are performed. When filtering the processing result, the minimum size of the defect to be detected is set in the output filter 38.
Based on the result of the image brightness evaluation, an appropriate gradation conversion table 39 is determined for each SEM unit, and this is set in the gradation conversion unit 40. The inspection threshold value 41 is obtained by setting the threshold value of each SEM unit based on a value set using any one set of SEM units so that the operator can obtain a desired inspection sensitivity in advance. Is determined in consideration of the above, and set in the defect detection unit 42.

The method of adjusting the inspection sensitivity using only the image quality evaluation result has been described. Next, a method of actually performing the inspection and optimizing the inspection parameters will be described.

FIG. 3 shows an example of the division of the inspection area on the sample in the case of four sets of SEM parts. As shown in FIG. 12, the inspection areas set by the SEM parts are set so as to overlap each other. For example, it is possible to evaluate the difference in inspection sensitivity using this common area. FIG. 12 shows a case where the width of the common inspection area 43 of R1 and R2 is M, and R3, R2
4 is not shown, but is set similarly. Then, Figure 1
2 is a region common to the adjacent SEM units 1 and a central region is a common inspection region 43 that can be inspected by all SEM units.

With reference to FIG. 13, a description will be given of a method of actually performing an inspection and adjusting the sensitivity between the SEM units when the common inspection area 43 is provided.

The image quality is evaluated by the above-described method, and the inspection parameters of all the SEM sections are set based on the evaluation result. Next, the image of the common inspection area 43 is
Detected in M section and stored in memory. Next, the obtained image is inspected, and the detected defect information is compared. For example, among the results obtained by the plurality of SEM units, the number of the cases where there is no defect having the same detection position is counted,
If the number def_num is larger than a certain threshold ε, fine adjustment of the inspection parameter is performed. This fine-tuning
For example, as a result of performing image quality evaluation in advance, if many small-sized defects are output to the SEM unit determined to have high inspection sensitivity, the input / output filter and the threshold are changed to lower the sensitivity. If a large amount of defect information is output as a result of the SEM unit that has been evaluated as having a large amount of noise in advance, there is a high possibility of a false report, so adjustments such as raising the threshold value may be performed. As a result of the fine adjustment, when the inspection sensitivities of all the SEM units are substantially the same, the inspection is performed under the conditions.

Here, the reason why the image of the common area is once stored in the memory is to avoid reacquiring the image at the time of fine adjustment of the parameter. This is because, in order to acquire the same image in all SEM units, it is necessary to move the stage greatly and acquire the images in order, which takes a lot of time, and the image acquisition of the same place is repeated many times. This is because if done, the sample may be damaged by electron beam irradiation. When performing the image detection of the same place several times,
It is desirable that there be an operation of correcting the image quality based on the detection order and changing the detection order by the optical system. FIG.
In the example of 2, the common inspection area of all the SEM parts exists, but if there are more SEM parts and they are separated, if the adjacent SEM part has the common area, it is used. It is only necessary to adjust the inspection sensitivity of each other, and a common area of all the SEM units is not necessarily required.

As in the example shown in FIG. 12, instead of providing only a part as a common inspection area, the entire sample wafer can be inspected in all SEM sections. In this case, the moving range of the stage cannot be reduced even if there are a plurality of SEM units, but there are other advantages. For example, when one of the plurality of SEM units becomes unusable due to the life of the electron gun or the like, the inspection can be performed using another SEM unit even during the waiting time during the replacement operation.

For example, as shown in FIG. 14, an abnormality such as a sudden decrease in the signal amount can be detected from the image quality evaluation result or the inspection result. When such an abnormality is detected, the operator is notified of the abnormality of the SEM unit Se, a suspicious part is displayed by the phenomenon, and a request for repair is urged. At this time, with an inspection apparatus having only a single SEM unit, inspection cannot be performed until a service engineer comes to repair by request. However, if a plurality of SEM units are provided and one SEM unit has a function of inspecting all the areas, the SEM unit S which needs repair is required.
Inspection can be performed using another SEM unit instead of e. Similarly, when a part of the SEM unit is removed and exchanged for replacement of a part or the like, the inspection can be performed by another SEM unit while the SEM unit is being removed and repaired.

As shown in FIG. 20, each of the vacuum sample chamber 49 and each of the SEM sections has a vacuum exhaust system 50, and a vacuum can be cut off between each of the SEM sections and the vacuum sample chamber. An opening / closing section 51 is provided. When a failure such as a failure occurs in the SEM section, only the failed SEM section can be cut off from the sample chamber and repaired or replaced as necessary. FIG. 14 shows an example of electron source exchange. If the device is constructed using heat-resistant members or magnetic shields, etc. so that it does not affect other SEM parts during baking, even during baking after replacing the electron source, which takes a very long time, , Inspection can be carried out. For example, as shown in FIG. 20, a cover 52 such that the SEM part during baking does not affect other parts.
You only have to attach.

As described above, if each SEM unit can inspect the entire sample, the occupation time of the apparatus at the time of parts replacement or repair can be reduced. Further, as shown in the example of FIG. 14, by periodically performing image quality evaluation and monitoring the change, it is possible to provide a function of monitoring an abnormality of the apparatus. If the parts in the SEM unit 1 are replaced or adjusted, their characteristics change, so the image quality must be evaluated again and the inspection parameters must be automatically re-adjusted or the operator must re-adjust. Instruct.

Next, as another example of image quality evaluation, an example of evaluating image distortion will be described with reference to FIG. As described above, if different image distortions occur between the images to be compared, this may cause a false report. Therefore, image distortion is evaluated, and the result is reflected in image processing.

The evaluation of the image distortion is performed by using an image having the same pattern repeated as shown in FIG. As shown in FIG. 15, an appropriate template is selected from the pattern to be inspected or the pattern created for evaluation, and the change in pitch is measured by a generally well-known technique such as template matching. The variation in pitch, which should be constant, is image distortion, and if this is large, it causes false information. Evaluation is performed periodically, and if this distortion is a steady one, the distortion evaluation result is given to the preprocessing unit in FIG. 11 to perform correction. Further, similarly to the example shown in FIG. 14, the image distortion is continuously monitored, and if this exceeds an allowable value, a warning is given to the operator. If image contamination is caused by contamination in the middle of the optical path of the electron beam, the electron beam may be bent due to electrification, etc., so it is necessary to check if the image distortion increases and no problem occurs. It is.

Next, the arrangement of the sample for image quality evaluation as shown in FIG. 9 or FIG. 10 will be described.

If a pattern for image quality evaluation is formed on a wafer in the same manner as the sample to be inspected, the image quality can be evaluated by exchanging the wafer. Further, it can be created on a stage that holds a sample. For example, as shown in FIG. 16, if the evaluation samples 44 are arranged at the same intervals as the intervals between the SEM portions, P1 and P2 and P3 and P4 can be evaluated at the same time. When the movable range of the stage is sufficiently wide, only one evaluation sample may be used, and the image may be obtained by moving the stage.

FIG. 17 shows examples of different samples for evaluating image quality. In the sample of FIG. 9 or FIG. 10, the inspection sensitivity could not be evaluated, and the actual sample 7 was inspected and adjusted. However, as shown in FIG. Sample 45 for sensitivity evaluation
Is prepared, the sensitivity can be evaluated by performing an inspection using the prepared data.

Next, the effect of the present invention will be described with reference to FIG. According to the present invention, it is possible to realize a high-speed inspection and to reduce a moving range of the moving stage required for the inspection.

As shown in FIG. 18 a),
In the apparatus 46 having only a single detection optical system, both the biaxial stages must have a movable range larger than the diameter D of the wafer in order to inspect the entire surface of the wafer. However, if three sets of detection optical systems are arranged, for example, as shown in FIG. 18B), the moving range of the stage required at the time of inspection is one third of the diameter D of the wafer. As described above, the N sets of detection optical systems are set to D / N in the axial direction of the stage movement.
By moving the stage, the amount of movement of the stage required at the time of inspection can be reduced to 1 / N.

In general, the stage movement amount is detected by using a laser interferometer or the like.
There is an advantage that it is relatively easy to secure the accuracy even in such stage movement amount detection. A further great advantage is that the device can be miniaturized. When a single normal detection optical system is used as shown in FIG. 18A, the size of the wafer moving stage needs to be twice or more the wafer diameter D. In contrast, FIG.
By using a plurality of detection optical systems as shown in 8b), if the movement amount of the stage can be reduced,
The size of the stage can be reduced, and as a result, the occupied floor area of the inspection device can be reduced.

Finally, an application example of the inspection parameter adjustment function using the image quality evaluation result of the present invention will be described with reference to FIG.

For example, a plurality of SEM type inspection apparatuses 48 used on the same
By adjusting the parameters in the same manner as when adjusting the unit, it is possible to reduce the machine difference between the inspection apparatuses. As in the example shown in FIG. 19, if the devices are connected by a network,
Data exchange is easy.

[0078]

According to the present invention, the inspection parameters can be adjusted so that the inspection sensitivities of the plurality of detection optical systems are substantially the same. By applying this to an inspection device having a plurality of detection optical systems, the surface of a non-inspection object is divided into a plurality of inspection regions, and these are inspected simultaneously. Higher speed can be achieved. This is very effective for the inspection of semiconductor wafers whose diameter is increasing in the future. Further, by dividing the inspection area, each inspection area becomes smaller, the moving range of the stage for moving the sample can be reduced, and the floor area occupied by the apparatus can be reduced.

[Brief description of the drawings]

FIG. 1 is a schematic configuration diagram of an inspection device according to an embodiment of the present invention.

2 is a perspective view showing a main part of the inspection apparatus of FIG. 1;

FIG. 3 is a diagram illustrating an example of an inspection area of each SEM unit according to the embodiment of the present invention.

FIG. 4 is a diagram for explaining a problem that occurs when the characteristics of four sets of SEM units are different.

FIG. 5 is a diagram illustrating an example of a defect detection method.

FIG. 6 is a diagram for explaining a method of evaluating the brightness of the detection optical system and converting the gradation.

FIG. 7 is a diagram for explaining an example of a method corresponding to a difference in noise characteristics for each detection optical system.

FIG. 8 is a diagram for explaining a method of brightness evaluation and gradation conversion of a detection optical system, which is different from FIG.

FIG. 9 is a diagram showing an example of a sample for evaluating brightness and noise.

FIG. 10 is a diagram showing an example of a sample for evaluating resolution.

FIG. 11 is a diagram illustrating an example of an image processing parameter setting procedure using an image quality evaluation result.

FIG. 12 shows a setting method of an inspection area of a plurality of detection optical systems.
It is a figure showing an example.

FIG. 13 is a diagram illustrating an example of a procedure for adjusting the inspection sensitivity in a plurality of detection optical systems.

FIG. 14 is a diagram illustrating an example of a countermeasure method when an abnormality occurs in the detection optical system.

FIG. 15 is a diagram illustrating an example of an image distortion evaluation method.

FIG. 16 is a diagram illustrating an example of a method of arranging a sample for image quality evaluation.

FIG. 17 is a diagram showing an example of a sample for sensitivity evaluation.

FIG. 18 is a diagram for explaining that the present invention can reduce the occupied floor area of the inspection device.

FIG. 19 is a diagram for explaining a method of adjusting the sensitivity of a plurality of inspection devices.

FIG. 20 is a diagram illustrating an example of a repair or replacement method performed when a detection optical system is abnormal.

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 1 SEM (scanning electron microscope) part 2 Electron beam generator 3 Scanning device 4 Converging device 5 Detector 6 Electron beam 7 Sample 8 Stage 9 Image signal 10 A / D converter 11 Image processing unit 12 Overall control unit 13 Stage control Apparatus 14 Scanning control device 15 Focus control device 16 Input device 17 Display means 18 Storage device 19 Length measuring device 20 Continuous moving stage 21 Feed stage 22 Defect (inspection result) 23 Inspection part image 24 Comparative image 25 Defect image 26 Defect image output 27 Threshold value 28 Defect portion output 29 Noise 30 Margin 31 Sample for image quality evaluation 32 Sample for resolution evaluation 33 Image quality evaluation unit 34 Image quality evaluation result 35 Filter coefficient 36 Preprocessing unit 37 Minimum defect size setting value 38 Output filter 39 floor Tone conversion table 40 Tone converter 41 Inspection threshold 42 Defect Protruding part 43 Common inspection area 44 Image quality evaluation sample 45 Sensitivity evaluation sample 46 Inspection apparatus having a single SEM part 47 Inspection apparatus having three sets of SEM parts 48 SEM inspection apparatus 49 Vacuum sample chamber 50 Vacuum exhaust system 51 Opening / closing part 52 Cover

──────────────────────────────────────────────────続 き Continued on the front page (51) Int.Cl. ⁷ Identification symbol FI Theme coat ゛ (Reference) H01L 21/66 H01L 21 / 66Z G06F 15/62 405A (72) Inventor Akira Shimase Totsuka, Yokohama-shi, Kanagawa Prefecture 292, Yoshida-cho, Ward, Japan Hitachi, Ltd., Production Technology Research Laboratories (72) Inventor Hiroya Koshiba 292, Yoshida-cho, Totsuka-ku, Yokohama, Kanagawa, Japan Inside Hitachi, Ltd. 292 Yoshida-cho, Totsuka-ku, Yokohama-shi, Ltd.Hitachi Manufacturing Technology Laboratory (72) Inventor Junzo Higashi 292 Yoshida-cho, Totsuka-ku, Yokohama-shi, Kanagawa Prefecture Hitachi, Ltd.Production Technology Laboratory (72) Inventor Hiroyuki Shinada 1-280 Higashi Koigakubo, Kokubunji-shi, Tokyo Inside the Central Research Laboratory, Hitachi, Ltd. (72) Inventor Yutoshi Sugimoto 2326 Imai, Kyoto Ome City, Hitachi, Ltd.Device Development Center, Hitachi, Ltd. (72) Inventor Yasuteru Usami 882, Ma, Hitachinaka, Ibaraki Pref., Ltd.Measurement Equipment Division, Hitachi, Ltd. 1-280 Higashi Koigakubo F-term in Hitachi Central Research Laboratory Co., Ltd. (reference) CA07 CB01 DA07 EA08 EA11 EA14 EA16 EA19 EA24 EB01 EB02 ED11 FA01 4M106 AA01 AA09 BA02 BA04 CA39 DB05 DB30 DJ04 DJ13 DJ14 DJ15 DJ19 5B057 AA03 BA01 BA15 BA19 DA03 DB02 DC02 DC30 5C033 FF03 JJ05 MM05

Claims (22)

[Claims] 1. A plurality of detection optical systems for detecting images representing physical properties of a surface of an object in a plurality of regions on the object, and an image signal detected by each of the detection optical systems. A processable image processing unit, a stage capable of holding and moving the object, image quality evaluation means for quantitatively evaluating the quality of an image obtained by the detection optical system, and image quality evaluation by the image quality evaluation means Means for determining an inspection parameter based on a result so that the inspection sensitivities of the plurality of detection optical systems are substantially the same. 2. The method according to claim 1, wherein the evaluation of the image quality is performed by using at least one of a detection signal amount, resolution, detection signal noise, and image distortion. Pattern inspection device. 3. The method according to claim 1, wherein the evaluation of the image quality and the adjustment of the inspection parameters are performed using a sample for image quality evaluation and / or a pattern on an inspection object. Pattern inspection equipment. 4. The pattern inspection apparatus according to claim 3, wherein the sample for image quality evaluation is mounted on the stage. 5. The pattern inspection apparatus according to claim 1, wherein the two detection optical systems disposed adjacent to each other have at least one common inspection area. 6. The detection optical system according to claim 5, wherein the detection optical system is based on an image quality and / or an inspection result of a detection image of the common inspection area by the two detection optical systems arranged adjacent to each other. A pattern inspection apparatus for determining an image processing parameter having the same inspection sensitivity. 7. The apparatus according to claim 1, wherein all of the plurality of detection optical systems are capable of detecting an image of the entire inspection area, or of the entire inspection area and the image quality evaluation sample. Pattern inspection device. 8. The inspection system according to claim 1, wherein an entire inspection area can be inspected by selecting and using one or more arbitrary detection optical systems among the plurality of detection optical systems. Pattern inspection equipment. 9. The pattern inspection apparatus according to claim 1, further comprising: means for detecting a malfunction of the apparatus based on the image quality evaluation result; and means for presenting the malfunction to an operator. 10. The pattern inspection apparatus according to claim 1, wherein the plurality of detection optical systems are electron optical systems. 11. A plurality of detection optical systems for detecting images representing physical properties of the surface of the object in a plurality of regions on the object, and an image signal detected by each of the detection optical systems. A processable image processing unit, a stage capable of holding and moving the object, image quality evaluation means for quantitatively evaluating the quality of an image obtained by the detection optical system, and image quality evaluation by the image quality evaluation means Means for monitoring the states of the plurality of detection optical systems based on the results and detecting abnormalities. 12. A stage capable of holding and moving an object, and a plurality of detection optical systems for detecting an image of the surface of the object, which are arranged in a plurality of regions on the object in a moving axis direction of the stage. And a movable range of the stage is smaller than a projection length of the object onto a stage moving axis. 13. The pattern inspection apparatus according to claim 12, wherein intervals of the stages of the detection optical system in the moving axis direction are equal. 14. In a plurality of regions on an object, images showing physical properties of the surface of the object are detected by a plurality of detection optical systems, and the quality of an image obtained by the detection optical systems is quantified. And the detection parameters are determined based on the image quality evaluation results so that the inspection sensitivities of the plurality of detection optical systems are substantially the same. Using the determined parameters, the plurality of detection optical systems are determined. A pattern inspection method characterized by simultaneously processing image signals detected by a system. 15. The method according to claim 14, wherein the evaluation of the image quality is performed by using at least one of a detection signal amount, resolution, detection signal noise, and image distortion. Pattern inspection method. 16. The pattern according to claim 14, wherein the evaluation of the image quality and the adjustment of the inspection parameters are performed using a sample for image quality evaluation and / or a pattern on a non-inspection object. Inspection methods. 17. The pattern inspection method according to claim 14, wherein two detection optical systems arranged adjacent to each other have at least one or more common inspection regions. 18. The detection optical system according to claim 17, based on an image quality and / or an inspection result of a detection image of the common inspection region by the two detection optical systems arranged adjacent to each other. A pattern inspection method characterized by determining image processing parameters having the same inspection sensitivity. 19. The apparatus according to claim 14, wherein all of the plurality of detection optical systems are capable of detecting an image of the entire inspection area or the entire inspection area and the image quality evaluation sample. Pattern inspection method. 20. The inspection device according to claim 14, wherein the whole inspection area can be inspected by selecting and using one or more arbitrary detection optical systems among the plurality of detection optical systems. Pattern inspection method. 21. The pattern inspection method according to claim 14, wherein a defect of the apparatus is detected based on the image quality evaluation result, and the detected defect is presented to an operator. 22. The pattern inspection method according to claim 14, wherein the plurality of detection optical systems are electron optical systems.