Patent Application
20110182496
The present invention relates to a check or inspection for detecting a minute pattern defect and/or a foreign substance upon basis of a result of comparison, while comparing an image of an object to be inspected (i.e., a check image), which is obtained with using a light, a laser or an electron beam, etc., with a reference image, and in particular, it relates to a defect check or inspection method and a device thereof being suitable for conducting a visual inspection upon a semiconductor wafer, a TFT and/or photo mask and so on.
As the conventional technology for conducting a defect detection with comparison between a detection image and a reference image is already known a method, which is described in a Patent Document 1. In this, an image of an object to be inspected, on which patterns are aligned repeatedly, is taken by a line sensor, sequentially, to be compared with an image delayed by an amount of a repetitive pattern pitch, and thereby detecting a discrepancy portion to be a defect.
Explanation will be made on a semiconductor wafer, for example, by referring to
In the conventional defect check, in particular, within the peripheral circuit portion 20-2, brightness (i.e., a brightness value) of the images is compared between the positions corresponding to the neighboring chips, i.e., the area 22 and the area 23, etc., in
On the semiconductor wafer, being an object to be inspected, a delicate difference is generated in a film thickness on the patters even if they are neighboring to each other, due to flattering or planarization through CMP, and there is a local difference (i.e., brightness difference) on the image between the chips. If detecting a portion having the brightness difference being equal to or greater than a specific threshold value “th” as the defect, as is in the conventional method, an area or region where the brightness differs from due to such difference of the film thickness is also detected as the defect. However, inherently this should not be detected as the defect. Thus, it is erroneous information. Conventionally, as one method for avoiding generation of the erroneous information, the threshold value for defect detection is determined to be large. However, this lowers the sensitivity, i.e., it is impossible to detect the defect having the difference value being equal to or less than that.
Also, the difference of brightness due to such difference of the film thickness may occur, among the chips aligned as shown in
Also as a factor of hampering the sensitivity is a difference of brightness between the chops, caused due to variation of thickness of the patterns. In the conventional comparison check with using the brightness, if there is such variation of the brightness, it results into a noise when conducting the check.
On the other hand, the defects are various in the kinds thereof, and they can be divided roughly, into a defect not necessary to be detected (i.e., can be considered as a normal pattern noise) and a defect tp be detected. According to the present invention, what is detected as the defect, erroneously, in spite of the fact that it is not defect (i.e., erroneous information), and the normal pattern noise, etc., they are called “non-defect”, collectively. In the visual inspection, it is demanded to extract only the defect, which the user wishes, from among an enormous number of defects; however, with comparison between the brightness difference and the threshold value, it is difficult to achieve this. Also, it is very often that a view is changed for each kind of detects, upon factors depending on the object to be inspected, such as, a material, surface roughness, sizes, depth, etc., and also combination with the factors depending on a detecting system, such as, a lighting condition, etc., therefore it is difficult to set up the condition for extracting only the defect, which the user desires.
An object of the present invention is, for dissolving the drawbacks of such conventional inspection technology, within a defect check apparatus for determining the discrepancy portion of an image as a defect, comparing the images of corresponding areas or regions of patterns, which are formed to be the same pattern, to achieve a defect inspection for detecting the defect (s) desired by the user with high-sensitivity and at high-speed, which are buried in noises and the defects not necessary to be detected, without conducting the troublesome setup of the threshold value.
Brief explanation of an outlook of a representative one of the inventions disclosed herein will be as follows:
(1) A defect inspection method for inspecting a defect(s) on an object to be inspected, comprising the following steps of:
a step for obtaining detected image of a pattern of said object to be inspected with irradiation under a predetermined optical condition upon said object to be detected; a step for determining a parameter of discriminant function to be formed upon basis of feature quantity, which is calculated from detected image; and a step for detecting a defect on said object to be inspected, with using said discriminant function to be formed upon basis of the parameter, which is determined in said step for determining the parameter, wherein said step for determining the parameter includes: a step for extracting a defect candidate on said object to be inspected with using said discriminant function with determining an arbitrary parameter; and a step for automatically renewing the parameter of said discriminant function, upon basis of teaching of defect information relating to said defect candidate, which is extracted in said step for extracting said defect candidate.
(2) The defect inspection method defined in the (1), wherein the parameter of said discriminant function is automatically renewed while teaching only that said defect candidate is a non-defect, within said step for automatically renewing said parameter.
According to the present invention, it is possible to detect the kind of defects, which the user wishes, with high-sensitivity, while teaching the non-defect.
a) and 2(b) are views for showing an example of the structures of an object to be inspected (e.g., a semiconductor wafer);
a) is a histogram of brightness difference of the detection image and the reference image, and 5(b) is a view for showing an example of a polygonal discriminant function;
a) and 7(b) are views for showing an example of a method for setting up the threshold-value-surface function, within the defect candidate detecting portion;
a) to 11(c) are views for showing an embodiment of a defect inspection method, according to the present invention;
a) to 12(d) are views for explaining a defect, which cannot be detected through the comparison inspection with a neighboring chip;
a) and 14(b) are views for showing a variation of the defect inspection method, according to the present invention;
a) and 17(b) are views for showing a variation of the defect inspection method, according to the present invention.
Hereinafter, an embodiment according to the present invention will be fully explained by referring to
The image processor portion 18 is constructed, appropriately, so as to have a pre-processor portion 18-1, a defect candidate detector portion 18-2, and a defect extractor portion 18-3 therein. The scattering-light intensity signal inputted into the image processor portion 18 is conducted with a signal correction and image dividing, etc., which will be mentioned later. Within the defect candidate detector portion 18-2, a process is treated, which will be mentioned later and thereby detecting a defect candidate, from the image produced in the pre-processing portion 18-1. In the defect extractor portion 18-3, from image information of defect candidates, which are detected within the defect candidate detector portion 18-2, the defect kinds and noises determined unnecessary by a user are excluded, while the defect kind(s) determined to be necessary by the user is/are extracted from (a post-step); thereby to be outputted to a total controller portion 19. In
The scattering lights 3a and 3b indicate distributions of the scattering lights, which are respectively generated corresponding to the lighting portions 15a and 15b. If the optical condition of the illumination light by means of the lighting portion 15a differs from the optical condition of the illumination light by means of the lighting portion 15b, the scattering light 3a and the scattering light 3b, each generating therefrom, differ from each other. In the present specification, an optical feature and a feature of the scattering light generating by a certain illumination light is called a “scattering light distribution of that scattering light”. The scattering light distribution indicates, in more details thereof, distribution of optical parameter values, such as, intensity, amplitude, a phase, a polarization, a wavelength, a coherency, etc., with respect to an emission portion, an emission direction, and an emission angle.
Next, a schematic diagram of the defect check device is shown in
The check device, according to the present invention is constructed, appropriately, so as to include plural numbers of lighting portions 15a and 15b for irradiating the illumination lights upon the object to be inspected (e.g., the semiconductor wafer 11), a detecting optic system 16 (e.g., an upper detector system) 16 for forming an image of scattering light in the vertical direction from the semiconductor wafer 11, the detector portion 17 for receiving the formed optical image thereon and converting it into an image signal, the memory 2 for storing therein the image signal obtained, the image processor portion 18 and the total controller portion 19. The semiconductor wafer 11 is mounted on a stage (e.g., an X-Y-Z-θ stage) 12, which can move and rotate within a XY plane and move in Z direction, and the X-Y-Z-θ stage 12 is driven by a mechanical controller 13. In this instance, mounting the semiconductor wafer 11 on the X-Y-Z-θ stage 12 and detecting the scattering lights from a foreign matter(s) on the target to be inspected while moving the X-Y-Z-θ stage 12 in the horizontal direction, a detection result can be obtained in the form of a two-dimensional (2-D) image.
As the illumination light source of the lighting portion 15a or 15b may be applied a laser, or a lamp in the place thereof. Also, the wavelength of the light of the illumination light source may be short wavelength, or a light of wide band wavelength (e.g., a white light). In case of applying the short wavelength light, for the purpose of increasing a resolution power of the image to be detected (i.e., for detecting a minute defect), it is possible to apply a Ultra Violet Light (e.g., UV light). In case of applying a laser as a light source, and in particular, where that is a laser of a single wavelength, it is also possible to provide a means for reducing the coherence (not shown in the figure) on the lighting portion 15a or 15b.
The detector portion 17, applying an image sensor of time-delay integration type (i.e., a Time Delay Integration Image Sensor: TDI image sensor), which is built up by aligning plural numbers of one-dimensional (1-D) image sensors in 2-D manner, as the image sensor thereof, and thereby transmitting a signal detected by each 1-D image sensor to a next-stage 1-D image sensor, in synchronism with movement of the X-Y-Z-θ stage 12, to be added with, is able to obtain a 2-D image with relatively high-speed and high-sensitivity. As this TDI image sensor, with applying a parallel output type sensor having plural numbers of output taps, it is possible to process outputs from the sensor in parallel with, and thereby enabling further high-speed detection.
The image processor portion 18 is that for extracting a defect (s) on the semiconductor wafer 11, being the object to be inspected, and it is constructed, appropriately, so as to include the pre-processing portion 18-1 for conducting image correction, such as, shading correction, dark level correction, etc., upon the image signal inputted from the detector portion 17, thereby dividing it into images, each having sizes of a constant unit, the defect candidate detector portion 18-2 for detecting a defect candidate(s) from the corrected and divided images, the defect extractor portion 18-3 for extracting the defect (s) other than the unnecessary defects and the noises, which the user designates, from the defect candidates detected, a defect classifying portion 18-4 for classifying the defect (s) extracted, depending on the defect kinds, and a teaching data setup portion 18-5 for setting teaching data, being inputted from an outside, into the defect candidate detector portion 18-2 and the defect extractor portion 18-3, upon receipt thereon.
The total controller portion 19 comprises a CPU (i.e., being built within the total controller portion 19) for executing various kinds of controls, and it is connected, appropriately, with a user interface portion 19-1 having a display means and an input means, for display the image of defect candidate (s) detected, the image of the defect, which is finally extracted, etc., upon receipt of the teaching data (though will be mentioned later, patterns from the user, which can be detected in a large amount, such as, the normal patter noise, the unnecessary defects, etc., for example) and also design information of the semiconductor wafer 11, and a memory device 19-2 for memorizing feature quantities and images or the like of the detected defect candidates therein. The mechanical controller 13 drives the X-Y-Z-θ stage 12 upon basis of control instructions from the total controller portion 19. Further, the image processor portion 18, the detecting optic system 16, and so on, are also driven upon the instructions from the total controller portion 19.
Herein, on the semiconductor wafer 11, being the object to be inspected, as is shown in
On the semiconductor wafer 11, as was mentioned above, are formed the same patterns, regularly, and although the images of the areas 21 through 25 should be properly the same, but actually, the brightness differs from between the images. By referring to
Further, as a primary factor of the noises, there are some, which are caused due to variation of thickness of the patterns. Those expressing waveforms of the brightness signals of the images of the neighboring chips are a brightness signal 44 of the detection image and a brightness signal 45 of the reference image, respectively, and that laying one on top of the other is a superimposing 47 of brightness signals between the brightness signal 44 of the detection image and the brightness signal 45 of the reference image. As is a difference image 46 between the brightness signal 44 of the detection image and the brightness signal 45 of the reference image, when the difference of brightness between the images at a specific pixel due to the variation of thickness of the patterns is equal to or greater than the threshold value, it can be detected as the defect. Further, if advancement is made on the high-sensitivity of the inspection device, a number of the defects and the kinds of defects are also huge, and therefore, if the user conducts a high-sensitivity inspection with comparison of the brightness, while setting the threshold value to be low, then almost of the defect candidates come to be the noises and the unnecessary defects; i.e., it is difficult to find out the defect(s) desired by the user from among the defect candidates.
a) shows a histogram of brightness differences of the detection image and the reference image. Since the detection image has a bright portion and/or a dark portion, comparing to the reference image, the brightness difference has both, positive and negative values. In case of detecting the defect(s) with comparison of the brightness, as is in the conventional technology, the threshold values 51 and 52 are determined on the plus side and the minus side of the histogram, respectively, and that lying outside of those results to be detected as the defect candidate. Those threshold values can be setup by the user, manually, while watching a manner of appearance of the noises, or can be set up automatically, parametrically, from distribution values of the histogram. If determining the threshold values 51 and 52 outside so that no noise can be detected, the sensitivity comes to be low, and then only a large defect can be detected. If determining the threshold values inside much more, then the sensitivity comes to be high, then the defect can be detected even from a meshed portion of the histogram. With this, it is possible to detect the defect being minute much more, but at the same time it happens that the normal pattern noises 55 are also detected in a large amount thereof; i.e., although it is possible to detect the minute defect 54 desired by the user, but it is buried within the normal pattern noises, then it is impossible to specify that from among the defect candidates.
For this reason, according to the present invention, in particular, with the defects, which cannot be discriminated only from the difference of brightness, and the normal pattern noises, as is shown in
First of all, detection is made on a positional shift volume between the detection image 31 to be the inspection target and the reference image 32 corresponding thereto (herein, as an image of the neighboring chip is used one attached with the reference numeral 22 in
Next, for each pixel of the detection image 31, on which the positioning is conducted, the feature quantity is calculated between it and the pixel of the reference image corresponding thereto (step 304). As an example thereof, there are (1) brightness, (2) contrast, (3) shading or tint difference, (4) a brightness distribution value of the pixel in vicinity, (5) a correlation coefficient, (6) increase/decrease of brightness comparing to the pixel in vicinity, and (7) a secondary differential value, etc. The example of those feature quantities can be expressed as follows, assuming that the brightness of each point on the detection image is f(x, y), and the brightness of the reference image corresponding thereto is g(x, y), respectively:
Brightness; f(x,y), or {f(x,y)+f(x,y)}/2 (Eq. 1)
Contrast; max {f(x,y)}, f(x+1,y), f(x,y+1),f(x+1,y+1)}−min {f(x,y)},f(x+1,y),f(x,y+1),f(x+1, y+1)} (Eq. 2)
Tonic difference; f(x,y)−g(x,y) (Eq. 3)
Distribution; [Σ{f(x+I,y+j)2−Σ{f(x+I,y+j)2/M}/(M−1) i,j=−1,0,1M=9 (Eq. 4)
As the feature quantities, various ones indicating features of the noises and the defect kinds can be used herein, other than the above-mentioned (1) to (7).
And, among those feature quantities is formed the feature space by plotting each pixel within the space, using several or all feature quantities as axes thereof (step 305). On this feature space is formed the discriminant function, which sets up parameters (will be mentioned later) thereon, arbitrarily (step 306), and detection is made on the pixel as the defect candidate, which is plotted outside the discriminant function, among the pixels building up the feature space, i.e., the pixel having an off value in the meaning of the features thereof (step 307).
Herein, because the image of the semiconductor wafer 11 can be obtained, continuously, accompanying with movement of the X-Y-Z-θ stage 12 shown in
Next, a method for setting the discriminant function for detecting an off pixel will be explained, by referring to
First of all, an inspection chip is set to be used in a setup of the discriminant function (step 71). When setting up the discriminant function, for the purpose of reducing a process time, an area of the object to be inspected (i.e., a black painted portion in
Next, with the parameters of an appropriate discriminant function, or the parameters of the discriminant function being set up by default, the defect candidate detection process shown in
The user confirms on whether it is the defect or non-defect, while observing an image of the defect candidate, which is detected through the test inspection (step 73). The confirmation may be made on the image, which can be obtained by an lighting optic system of the device according to the present invention, or that, which can be obtained by other detecting system, such as, an image formed by electron beams, for example, as far as distinction can be made between the defect and the non-defect. Also, according to the present invention, when executing the defect candidate detection process, since the pixel to be the defect candidate and small images in the peripheral portions thereof are cut out from the detection image, and the small images at the position corresponding to thereto are cut out from the reference image, on a set, and they are reserved, therefore confirm can be made on the image of those. And, teaching of the defect information upon basis of the defect candidate, which is confirmed, is made (step 704). Herein, the defect information is the information indicative of whether the pixel extracted as the defect candidate is either the non-defect or the defect, and the user teaches the teaching data, with using the defect information on whether it/they is/are the defect(s) or the non-defect(s), for an arbitrary number of defect candidate(s).
Upon basis of the teaching data, calculation is automatically executed on such discriminant function that it does not detect the non-defect (step 75), and with using the discriminant function calculated, the defect candidate detection process (e.g., the test inspection) shown in
Since it is high in possibility that almost of the defect candidates to be detected are the non-defects, if executing the defect candidate detection process with using appropriate parameters, then basically, only the non-defect are designated; however, if including the defect (s) therein, it is also possible to teach the defect. Upon the defect candidate (s) to be detected, by repeating the steps 73 to 76 until when no non-defect is detected as the defect candidate, and thereby renewing the parameters, automatically, the parameters of the discriminant function are determined. And, with using the discriminant function formed upon basis of the parameters determined, the defect candidate detection process is conducted on the total chips (i.e., the entire surface of the semiconductor wafer (step 77).
The screen 81 is constructed, appropriately, so as to have a defect map 82 for showing positions of the defect candidates on the semiconductor wafer, a defect list for displaying all feature quantities or the like, such as, sizes of the defect candidates detected, etc., for example, an individual defect displays screen 84 for displaying a defect candidate image (e.g., a defect portion, a reference portion, a difference image, etc.), which is selected from the defect map 82 or the defect list 83, and the feature quantity thereof, an observation image display screen 85 through other lighting optic system, a teaching button 86 for teaching of whether each defect candidate is the non-defect (normal) or the defect, and a discriminate function setup button 87 for renewing the discriminate function, automatically.
On the defect map 82 is displayed an inspection object of the test inspection, brightly (herein, central five (5) chips), and the detected detect(s) is/are plotted thereon. On the individual defect display screen 84 are displayed the defect candidate image (e.g., a defect portion, a reference portion, a difference image, etc.) and the feature quantity thereof, by pointing out the defect on the defect map 82 or the defect list 83, individually, by a mouse. The teaching button 86 is used when the user teaches of whether the defect candidate is the defect or the non-defect, while observing the observation image display screen 85. Also, the screen 88 is used when the user makes the teaching of whether the defect candidate is the defect or the non-defect, sequentially, while designating several or several-tens numbers of the detect candidates, after selecting the teaching button 86 by the mouse. When the discriminate function setup button 87 is selected, at the time-point when the teaching is completed, then calculation is executed on the discriminant function.
Next, explanation will be made about an example of the discriminant function. According to the present invention, upon an assumption that it is not easy to find out the defect, which is needed by the user, truly, from the defect candidates detected, in particular, if a number of the defect candidates to be detected increases accompanying with an advancement of the high-sensitivity of the device, the teaching is made of only the non-defects, which can be found out easily, and thereby to enable calculation of the discriminant function for discriminating between the defect and the non-defect. As a method thereof, it is treated as a discrimination problem of 1st class, in general, and there are various kinds thereof. As one example thereof, assuming that the feature of a non-defect pixel, upon which the teaching is made, has a normal distribution, there is a method for discrimination, with obtaining a probability that the pixel, being the object to be inspected, is non-defect pixel. Assuming that “d” pieces of the feature quantities of “n” pieces of non-defect pixels, upon which the teaching is made, x1, x2, . . . , xn, then a discrimination function φ for detecting a pixel having the feature quantity x as the defect candidate can be given by the following equations, Eq. 5 and Eq. 6:
Probability density function of x
where μ is an average of the teaching pixels
Σis covariance Σ=Σi=1n(xi−μ)(xi−μ)′
Discrimination Function
φ(x)=1(if p(x)≧th then non-defect)
0(if p(x)<th then defect) (Eq. 6)
Also, as an example of the case, where the features of the non-defect pixel cannot be presumed in the form of the parametric distribution model can be applied a method, such as, a 1st class SVM (Supper Vector Machine), etc. This maps the feature space made up with the non-defect pixels, upon which the teaching is made, into a density space. And then, the discrimination function φ is calculated, while letting a hyperplane having a maximum margin for separating an original point of the density space and the distribution of the non-defect pixel to be the discriminant function (but, the equation thereof is omitted herein). As was explained in
Next, when new data is inputted, as is in the processing flow of the defect candidate detector portion 18-2 shown in
Further according to the present invention, within the defect candidate detector portion 18-2 shown in
The processes for extraction of the defect will be explained by referring to
The user observes image(s) of the detected defect candidate(s) by an arbitrary number of pieces (or, of points) thereof, and makes the teaching of the non-defect pixel (step 92). A manner of the teaching is as follows. Herein, the teaching may be made on both the defect and the non-defect. In the defect extractor portion 18-3, from the images of the defect candidates, which are taught to be the non-defects is determined the discriminant function for not detecting that/those (step 93). And then, upon all of the defect candidates, which are detected, the feature quantities are calculated (step 904), and then determination is made for each defect candidate on whether it lies in an inside of the calculated discriminant function (i.e., being the non-defect), or in an outside of the discriminant function (i.e., being the defect) on the feature space (step 95), thereby extracting only that lying in the outside, to be outputted to the total controller portion 19, and then to be displayed in the form of the map, as a final inspection result (step 96).
The calculation method may follow the Eqs. 5 and 6 mentioned above, or may follow the 1st class SVM, too. Determination of the discriminant function of the defect extractor portion 18-3 may be made at the same timing to that of setting the threshold value in the defect candidate detector portion in accordance with the test inspection shown in
a) to 11(c) show an example of the tuning in the defect extracting process, by an additional teaching of the non-defect or the defect.
Herein, in the defect extractor portion 18-3 shown in
According to the invention mentioned above, although mentioning was made on the example of detecting a small number of the defects desire by the user, being buried within the noises and the large number of the unnecessary defects, by making the teaching that they are the non-defects; however, further effect(s) will be mentioned below.
In the above, although the mentioning was made about the example of detecting a coming out from the distribution of the non-defects, of which the teaching is made on the feature space, as the detect, after calculating the discriminant function only with the teaching of the non-defects; however, according to the present invention, it is also possible to adjust the sensitivity. An envelope surface 1401 shown in
Heretofore, although the mentioning was given on the method for determining the defect with using the image obtained by only one (1) detector; however, the defect inspection method according to the present invention may have a means for detecting plural numbers of images by the detector.
Herein, if the images of the detection optic system 16 and the detection optic system 130 are taken, in time-series, then also the positional shift is calculated between the detection images 161a and 161b (step 1603). And then, by adding the positional relationship between the images of the detection optic system 16 and 130 into the consideration, selection is made for all or a several of the feature quantities of the target pixels, and thereby forming up the feature space (step 1604). The feature quantities are calculated out from the respective sets of the respective images, such as, (1) brightness, (2) contrast, (3) shading or tonic difference, (4) a brightness distribution value of the pixel in vicinity, (5) a correlation coefficient, (6) increase/decrease of brightness comparing to the pixel in vicinity, and (7) a secondary differential value, etc., as was mentioned above. In addition thereto, the brightness of each images themselves (i.e., the detection image 161a, the reference image 161a′, the detection image 161b and the reference image 161b′) are used as the feature quantities. Also, with combination of the images of each detection systems, the feature quantity (1) to (7) may be obtained from, such as, from an average value of the detection images 161a and 161b and the reference images 161a′ and 161b′, etc., for example. Herein, explanation will be made about an example when selecting two (2), i.e., a brightness average Ba calculated from the detection image 161a and the reference image 161a′ and a brightness average Bb calculated from the detection image 161b and the reference image 161b′, as the feature quantity. In case where the position shift of the detection image 161b with respect to the detection image 161a is (x1, y1), then the feature quantity calculated from the detection optic system 130 is Bb (x+x1, y+y1), with respect to the feature quantity Ba (x, y) calculated from the detection optic system 16. For this reason, the feature space is produced by plotting the values of all pixels, the teaching of which are made, in the 2-D space, with setting an X value to Ba (z, Y) and a y value to Bb (x+x1, y;y1). And, within this 2-D space is calculated the discriminant function enclosing the distribution of the teaching data therein (step 605).
a) an example of the feature space formed, wherein a broken line 1701 is the calculated discriminant function. And when inspecting all chips, the feature quantities Ba and Bb are calculated, in the similar manner, for all of the pixels of the object (step 1606), and determining the pixel(s) plotted outside of the calculated discriminant function as the defect candidate(s) (step 1607). In
As was mentioned above, according to the present invention, plural numbers of image signals, which are obtained upon receipt of the lights by means of the separate detection optic systems, are inputted into one (1) processor, and where the defect determination processes are executed therein. Since the images of those two (2) separate detection optic systems differ from, of course, in the distribution condition of the scattering lights thereof, and also differ from, in a part of the defect kinds, which are processed and detected, the information obtained from the separate detection optic systems are unified or combined with, for detecting the defects, and therefore it is possible to make more various defect kinds remarkable.
As was mentioned above, according to the check device explained in each of the embodiments of the present invention, the defect determination process by the image processor portion has the defect candidate detection portion and the defect extract portion, appropriately, wherein each of them is constructed with plural numbers of processors and executes the process in parallel. The defect candidate detection portion detects the data, of which the teaching is made, and the pixel, which differs from, characteristically, when the user makes the teaching about the non-defect, which can be obtained in a relatively ease, when conducting the test inspection. With this, it is possible to detect the defect candidate(s) at high accuracy with using the plural numbers of feature quantities, but without complicated setting of the condition. Also, when making the teaching that a part of the memory mat portion, which is formed with the similar repetitive patterns as the normal area, then the detection is made on feature off pixel(s) in the memory mat portion. With this, it is possible to detect the defect (s), which are difficult to be detected through comparison of the chips, such as, the defect(s) existing on the chop at an edge of the wafer, which differs from other chips, largely, in a manner of viewing thereof, due to the difference of film thickness, and/or a systematic defect(s), which generate at the same position of each chip, etc., for example.
Also, when the user makes teaching on the unnecessary detect(s), among from the defect candidates detected, extraction is made only upon the defect candidate(s) differing from the candidate(s), characteristically, of which the teaching is made. With this, it is possible to extract an important defect (s) desired by the user, which is/are buried within the unnecessary defects, without complicated setting of the condition.
Those processes can be also executed, after unifying or combining the images from the plural numbers of separate detection optic systems. With this, it is possible to detect various kinds of detects at high sensitivity. As the defect determination process through comparison of the chips, in the present embodiments, although there are shown only the examples of executing the comparing inspection, assuming the reference image is the image of the neighboring chip (e.g., the area 22 shown in
Also, even if there is a delicate difference in film thickness of the patterns after the process of flattering or planarization process, such as, CMP, etc., or a large difference in the brightness between the chips to be compared with due to short-wavelength of the illumination light; but, according to the present invention, it is possible to detect the defects 20 nm to 90 nm.
Further, even if there is a local difference of brightness due to variation of distribution of refractive index in the films, within an inspection of a “low k” film, such as, an inorganic insulation film, including SiO2, SiOF, SiOB and a porous silica film, or an organic insulation film, including SiO2 containing a methyl group, MSQ, a film of polyimide group, a film of Parellin group, a film of Teflon (®) and a film of amorphous carbon, etc, according to the present invention, it is possible to detect the defects 20 nm to 90 nm.
As was mentioned above, although the explanation was given on the example of the comparison inspection images in the dark-field illumination check device for targeting the semiconductor wafer, as an example of the present invention; however, the present invention can be also applied to a comparison image in an electron-type pattern inspection. Also, it can be applied to a pattern check device of the dark-field illumination.
The object to be inspected should not be limited to the semiconductor wafer, but it is also applicable onto one, the defect of which should be detected with comparison of images, for example, a TFT substrate, a photo mask, a printed board, etc.
| Number | Date | Country | Kind |
|---|---|---|---|
| 2008-214802 | Aug 2008 | JP | national |
| Filing Document | Filing Date | Country | Kind | 371c Date |
|---|---|---|---|---|
| PCT/JP2009/063014 | 7/13/2009 | WO | 00 | 4/15/2011 |
