JP2001305073A - Method and device for processing inspection data - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a method and a device for processing inspection data, allowing the accurate selection of a critical defect on the basis of the inspection data detected by an appearance inspecting device. SOLUTION: This method for processing inspection data comprising position coordinate data and feature quantity data on a defect generated on an inspection object detected by an appearance inspecting device has a preparation process of previously storing a first criticalness determining data group corresponding to the sort of a region on the inspected object and a second criticalness determining data group corresponding to the category of the defect; a first criticalness determining process of determining the first critical level of the defect according to the sort of the region where the defect exists; a category imparting process of imparting a category to the defect in the inspection data; and a second criticalness determining process of determining the second criticalness of the defect on the basis of the second criticalness determining data selected according to the imparted category on the basis of the first critical level determined in the first criticalness determining process.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an inspection data processing technique capable of determining the fatality of a defect generated in a circuit pattern of a semiconductor wafer, a mask, a liquid crystal, or the like. The present invention relates to a technique suitable for judging the fatality of a defect detected by an inspection device or the like and selecting a defect to be reviewed / analyzed, and a technique suitable for investigating a cause of occurrence of a fatal defect or taking measures.

[0002]

2. Description of the Related Art In a production line for semiconductors and the like, it is essential to find and detect the cause of a defect at an early stage and feed it back to a process and a manufacturing apparatus so as to remove the cause of the defect in order to maintain and improve the yield. To this end, it is important to find defects using a visual inspection device and analyze the inspection data.

As described above, as a prior art of a defect analysis system for determining the cause of a defect at an early stage, for example, Japanese Unexamined Patent Application Publication No.
No. 0-107102 is known. This conventional defect analysis system includes a visual inspection device for detecting a defect on a semiconductor wafer, and a group of defects detected from the visual inspection device that are in a dense state are extracted as a defect group. An analysis station for extracting one or more arbitrary defects from the group of defects as a representative defect, and observing only the representative defects extracted at the analysis station to determine the type (category) thereof, And a review station for assigning the same type determined as described above to the defects constituting the defect group.

That is, from the appearance inspection apparatus, for example, position data and size on the semiconductor wafer regarding defects of a circuit pattern such as a short circuit or disconnection or a defect such as a foreign substance generated in a wafer process are stored. Sent to the analysis system. Then, the inspected wafer is
The stage is moved to the review station, the stage is moved to the position of the coordinate data of the detected defect, and the type of the defect is classified based on the enlarged image of the defect.

[0005] Further, the prior art Technical
Proceedings SEMICON / Japan
n 1996 pp. 2-19-24 “An Advanced
Yield Learning System for 64M DRAM Production and
Beyond "describes that in a visual inspection apparatus, a threshold value for determining whether or not a defect is present from a difference image between a detected image and a reference image is changed according to the brightness distribution of the background.

[0006]

However, in the above-mentioned prior art, in a review / analysis work, a critical / invalid data is detected based on inspection data of a defect detected from a visual inspection device.
No mention is made of classification as non-fatal. Here, the fatal defect means a defect that is highly likely to cause an electrical operation failure of the circuit pattern.

However, since the review operation is a manual operation, it takes a long time, and is a constraint on improving the throughput of the inspection process. In addition, regarding the determination of fatality, not only the appearance of the defect itself but also knowledge of the function and structure of the circuit pattern in the area where the defect exists is required, and it is left to a specific expert with specialized knowledge. Was. Furthermore, even among these specialists, there is a problem that the judgment result varies from person to person because the criteria for determining the lethality are different.

[0008] A first object of the present invention is to solve the above-mentioned problems, based on inspection data detected from a visual inspection apparatus.
It is an object of the present invention to provide an inspection data processing method and an inspection data processing apparatus capable of selecting a fatal defect with high accuracy and accuracy.

A second object of the present invention is to provide a review apparatus / analyzer with which a fatal defect can be accurately detected.
It is an object of the present invention to provide an inspection data processing method and an inspection data processing method capable of accurately selecting a target that must be truly reviewed or analyzed and enabling a high throughput of a review operation and an analysis operation. .

A third object of the present invention is to provide a review /
An object of the present invention is to provide an inspection data processing method and an apparatus for automatically selecting an analysis target.

[0011] A fourth object of the present invention is to edit, search, and edit inspection data by using the inspection and analysis result information.
It is an object of the present invention to provide a method and apparatus for displaying and outputting to the outside, and to provide a method and apparatus for efficiently performing analysis and analysis such as cross-sectional observation of a defective portion.

[0012]

SUMMARY OF THE INVENTION In order to achieve the above object, the present invention provides inspection data (defect position coordinates and defect coordinates) for a defect generated on an object to be inspected detected by a visual inspection apparatus. Based on the characteristic data of the inspection target, the determination of the fatality is performed according to the type of the area where the defect exists on the inspection object and the category of the defect.

That is, the determination of the fatality is prepared by storing first fatality determination data according to the type of the area on the inspection object and second fatality determination data according to the category of the defect. It is characterized by performing by doing.

Further, the present invention is characterized in that the production line is managed based on the number of the second fatal defects determined.

Further, according to the present invention, the inspection object is inspected based on inspection data (defect position coordinates and defect characteristic data) on a defect generated on the inspection object detected by the visual inspection apparatus. The method is characterized in that the first fatality of a defect is determined according to the type of the area where the defect exists on the object, and the defect to which a category is assigned is selected based on the first fatality determination.

Further, according to the present invention, the inspection object is inspected based on inspection data (position coordinates of the defect and characteristic data on the defect) on the defect generated on the inspection object detected by the visual inspection apparatus. Calculating a first fatality level of the defect according to the type of the area where the defect exists on the object;
A defect to which a category is assigned is selected in accordance with the calculated first fatality level, a category is assigned to the selected defect using a review device, and a first defect is assigned to the calculated defect. The second fatality level is calculated by performing weighting (second fatality determination data) on the fatality level according to the assigned category.

Further, the present invention is characterized in that a defect to be analyzed by the analyzer is selected according to the second level of fatality.

Further, the present invention provides a method for inspecting an object to be inspected based on inspection data (position coordinates of the defect and characteristic data on the defect) on the defect generated on the object to be inspected detected by the visual inspection apparatus. For each of the different types of areas above,
The number of occurrences for each category is calculated according to the area of the defect, and the calculated number is output.

[0019]

Embodiments of the present invention will be described with reference to the drawings.

A system using the inspection data processing device (inspection data analysis device) according to the present invention is disclosed in Japanese Patent Application No. 10-10 / 1998.
No. 456 is an improved invention.

First, a first embodiment of a system using a test data processing device (test data analysis device) according to the present invention will be described with reference to FIG. Inspection data processing device (inspection data analysis device) 1 of the first embodiment
Is a visual inspection device 30 that inspects various defects (such as pattern defects, foreign matter, film thickness defect defects, and scratch defects) on a circuit pattern formed on a semiconductor wafer to be inspected and outputs an inspection data ID. And an ADC (Automat) that automatically assigns categories to defects
A review device 31 called an ic Defect Classification is connected via a network Nt to an analysis device 40 for analyzing a material, a property, a cross-sectional shape, and the like for investigating a cause of a fatal defect. The visual inspection device 30 includes an optical visual inspection device or an SEM visual inspection device including a detection system 30a and an image processing unit 30b having a display unit, and a semiconductor wafer W to be inspected is formed. Film process P1, exposure process P
2. In the course of processing to the etching step P3, for example, various defects generated on the semiconductor wafer W at the end of the etching are inspected in wafer units or lot units. Then, in the case of the in-line inspection, after the inspection is completed, the semiconductor wafer W is returned to the process process starting from the film forming process P4 again. Actually, the appearance inspection device 30 inspects the semiconductor wafer W sampled from an arbitrary manufacturing process in a manufacturing line having a number of manufacturing processes.

A semiconductor wafer W to be inspected by the visual inspection apparatus 30 is formed by arranging a large number of semiconductor chips C as shown in FIG. When the semiconductor chip is a memory, the semiconductor chip C includes a memory cell region A40, a first peripheral circuit region A10 around the memory cell region A40, a second peripheral circuit region A20 including a decoder and the like, a decoder and the like. And a fourth peripheral circuit area A0 formed on the outermost side. When the semiconductor chip C is a system LSI, a logic area exists inside the semiconductor chip C in addition to the memory area.

As described above, in the semiconductor chip C, the memory cell area A40 having the densest circuit pattern, and the second to third, first, and fourth peripheral circuit areas A30 which gradually become ancestors. , A20, A10, and A0. Therefore, the present invention has been made by paying attention to the fact that the fatality with respect to various defects generated in these areas and detected by the visual inspection device 30 also differs for each area.

It should be noted that fatal / non-fatal can be determined by an operation test called a probe test for a semiconductor chip that is finally almost completed.

However, the semiconductor wafer W has a very large number of manufacturing process steps (for example, various film forming steps, resist coating steps, exposure / development steps, etching steps, resist stripping steps, and chemical and mechanical processing of the surface of the insulating film. The semiconductor wafer W is inspected by the visual inspection device 30 in various forms, and is also detected by the visual inspection device 30. There are various types of defects (foreign matter, circuit pattern defects (more specifically, disconnection defects and short-circuit defects), scratch defects such as scratches, and other defects). That is, the surface of the semiconductor wafer W is the surface of an interlayer insulating layer or the surface of a circuit pattern (wiring pattern).

As described above, the appearance inspection apparatus 30 can be provided in various forms.
It is difficult to clearly determine whether various detected defects are fatal or non-fatal at the stage of the semiconductor wafer W inspected. However, if the representative conductor pattern width or conductor pattern space is dense, that is, minute, even a minute defect generated there has a high probability of becoming a fatal defect such as disconnection or short circuit,
On the other hand, if the representative conductor pattern width and conductor pattern space are large, that is, if they become large, even relatively large defects that occur there will be less likely to become fatal defects such as disconnection or short circuit. Become. Further, the level of criticality varies depending on the type (category) of the defect. Therefore, by calculating the criticality level according to the area where the defect exists and the type (category) of the defect, it becomes possible to narrow down the target of the defect to be analyzed by the analysis device 40 to a truly critical one. As a result, it is possible to increase the throughput of the analyzer 40 for determining the cause of the defect.

By the way, from the appearance inspection device 30, information (wafer number, lot number, manufacturing process number, etc.) on the inspected wafer W as the inspection data ID, Position coordinates (for example, barycenter position coordinates) of various defects based on the mark, feature amount data CD (defect size data: specifically, for example, projection lengths on the X axis and the Y axis) The length CD _{L of} a certain defect, the area CD _{S of} the defect, the change in brightness (shading value CD _B, etc.) are output. Then, information on the wafer W (including information on the manufacturing process) output as the inspection data ID from one or a plurality of visual inspection devices 30, position coordinates of the defect on the wafer, defect feature amount data IDC (defect Is the inspection data processing device 1 through the Internet Nt, for example.
Is stored in the inspection data storage unit 21 provided in the storage unit.

The review device 31 is an ADC constituted by a metal microscope, a scanning electron microscope (SEM), or the like. The review device 31 automatically determines the color and shape of a defect designated based on the defect position coordinates. The type (category) of a defect is determined, and the category of the defect as a result of the determination is output. The review device 31 classifies the category of the defect by looking at an enlarged image observed for the specified defect based on the defect position coordinates by a metallurgical microscope, a SEM, or the like, and inputs the classification using the input means. It may be output. Then, the information I of the defect category classified by the review device 31
DK (for example, foreign matter, circuit pattern defect, scratch defect such as scratch, or other defect) is, for example, Internet N
The data is stored in the inspection data storage unit 21 through t. The review device 31 is located inside the visual inspection device 30,
Alternatively, it may be provided in the vicinity.

As the analysis device 40, SEM, FIB
(Focused Ion Beam) Analyzer, SE
Combination of M and FIB processing device, mass spectrometer, X
It is composed of a line spectrometer and the like, and estimates the cause of the defect by analyzing the material, properties, cross-sectional shape, etc. of the defect.

The inspection data processing device 1 includes a communication control unit 3 for controlling communication of data input / output from the visual inspection device 30, the review device 31, and the analysis device 40. A data input / output unit 4 for inputting / outputting data is provided.

Further, the test data processing device 1 includes the test data storage unit 21, the in-chip area coordinate data storage unit 23, and the fatality determination data storage unit (the area-based determination rule 24a and the category-based determination rule 24b are A data storage unit consisting of 24; a search unit 5 for searching each data stored in the data storage unit; and a search unit 5
And a memory 6 for storing the data retrieved by the above.

The in-chip area coordinate data storage unit 23
For example, the data input / output unit 4 previously stores, for example, area coordinate data in a chip shown in FIG. 4 from a CAD system (not shown) of a semiconductor device via a network Nt. Naturally, when the area coordinate data in the chip is input from the CAD system and stored in the visual inspection device IA, the data may be input from the visual inspection device 30 and stored. As described above, the area coordinate data in the chip is, as shown in FIG. For example, the area No. 1 is indicated by the start point coordinates (xs, ys) and the end point coordinates (xe, ye) of the fourth peripheral circuit area A0 indicated by the area class value 0 composed of the coarsest circuit pattern. 6 and no. 7, the start and end point coordinates of the second and third peripheral circuit areas A20 and A30 indicated by the area class values 20 and 30 formed of the next finer circuit patterns. 2-No. 5 is an area class value 1 consisting of the next finer circuit pattern
For example, the start point coordinates and the end point coordinates of the first peripheral circuit area A10 indicated by the area No. 0 are displayed. 8 and thereafter, the area class value 40 consisting of the finest circuit pattern
For example, the start point coordinates and the end point coordinates of the memory cell area A40 are indicated. In the above description, each region is divided and set according to the density of the circuit pattern. However, each region is divided according to the difference in the thickness of the insulating film and the density of the circuit pattern formed thereon. May be set. The reason is that when defects such as foreign matter or scratches occur according to the thickness of the insulating film or the like, the possibility of operation failure such as disconnection or short circuit increases, which is fatal, and This is because the degree of being affected by the defect changes according to the density of the circuit pattern formed on the generated surface, thereby changing the criticality. In other words, even a minute defect, if the circuit pattern formed thereon is minute,
There is a high possibility that a malfunction occurs in the circuit pattern, and the circuit pattern becomes fatal.

Further, the area-specific fatality determination data storage unit 2
5A, as shown in FIG. 5, a fatality determination rule for each area corresponding to the accuracy (density) of a circuit pattern formed for each area is input means connected to the data input / output unit 4 for each area. (Not shown) and stored. The area-specific fatality determination rules are set in correspondence with the area class value Ac, as shown in FIG. By the way, a semiconductor wafer is completed by forming active elements on a semiconductor substrate and forming a multilayer wiring layer thereon. Therefore, the criticality judgment threshold value (judgment criterion) R for each region for a defect occurring on the circuit pattern (wiring pattern) of each layer slightly differs. However, the region-based determination criterion R for defects generated on the interlayer insulating film
Is different from the region-based determination criterion R on the circuit pattern, but is also affected by the circuit pattern formed thereon. However, as shown in FIG. 5, the area class value A of each of the areas A0 to A40 is generally used as the area-based determination criterion R for the defect generated on the circuit pattern.
For c = 0 to 40, for example, the defect length _RL indicating the defect feature amount is 2 μm, 1 μm, 1.5 μm, 1.5 μm.
m, 0.5 μm, the defect area R _S is 4 μm ² , 1 μm ² ,
^{2.25μm 2, 2.25μm 2, 0.25μm 2} , ( difference in brightness with respect to the normal portion) brightness of the defect R _B is large (L), large (L), intermediate (M), intermediate (M ) And is set small (S). Naturally, it is necessary to set the determination criteria R for each area by changing according to the surface condition of the semiconductor wafer. Further, it is necessary to change and set the region-specific determination criterion R according to the density of the circuit pattern formed thereon.

Categorical Fatality Determination Data Storage Unit 24
b, a coefficient, a function, or a look-up table K, which is weighted with respect to the above-described region-based determination criterion R in accordance with the category of a defect as a category-based fatality determination rule
Are set and stored. That is, when the defect is a foreign substance, the area or brightness of the defect is more significantly detected from the appearance inspection device 30 than the length of the defect, so that the area or brightness of the defect is greatly weighted. Also,
If the defect is a circuit pattern defect, the visual inspection device 30
For example, since the length or area of the defect is more remarkably detected than the brightness, the length or area of the defect is greatly weighted. If the defect is a flaw, it is weighted differently.

As described above, basically, the criticality determination data R for each area is set for each area in a chip according to the surface condition of the semiconductor wafer W.
In the category-based fatality determination data K, a coefficient, a function, a look-up table, or the like that is differently weighted according to the category of the defect is set.
It should be noted that whether or not these set region-by-category and category-by-category fatality judgment data is appropriate is confirmed by collecting statistics in association with operation test results obtained by a probe test on a semiconductor chip that has been almost completed. be able to.

Then, the search unit 5 is connected to the memory 6,
The memory 6 is connected to the data input / output unit 4. Further, the inspection data processing device 1 includes an inspection data storage unit 2.
1. An input / output interface 7 for adjusting the timing of data exchange with the in-chip area coordinate data storage unit 23 and the criticality determination data storage unit 24 is provided. The input / output interface 7 is connected to a search unit 5, a data input / output unit 4, an inspection data storage unit 21, a chip area coordinate data storage unit 23, and a fatality determination data storage unit 24.

Further, the test data processing apparatus 1 has a program storage unit 8 in which a program which is software for performing necessary processing is stored, and an operation for performing an operation based on the program stored in the program storage unit 8. A unit (which serves as a first and a second arithmetic unit) 9 is provided.
The operation unit 9 is connected to the memory 6.

The test data processing apparatus 1 is provided with a main control section (first and second control sections, etc.) 10 which controls all controls in the test data processing apparatus 1. The control unit 10 is connected to a data input / output unit 4, a search unit 5, a memory 6, a calculation unit 9, and a program storage unit 8.

Further, the display unit 11 is provided with the data input / output unit 4
Is connected to The display unit 11 displays data in the inspection data storage unit 21, the in-chip area coordinate data storage unit 23, the criticality determination data storage unit 24, and the first and second criticality determination results based on the first and second criticality determination results. And displays a map of the fatal defect.

The inspection data storage unit 21, the in-chip area coordinate data storage unit 23, the criticality determination data storage unit 24
May be the same storage medium. further,
The test data processing apparatus 1 may be connected to another system via the network Nt to transmit and receive data of the test data storage unit 21, the in-chip area coordinate data storage unit 23, and the criticality determination data storage unit 24. Further, the inspection data processing apparatus according to the first embodiment may be incorporated into the appearance inspection apparatus 30 without using the network Nt.

The arithmetic unit 9 and the main control unit 10 may be the same CPU. Further, the same semiconductor element in which the memory 6, the data input / output unit 4, and the communication control unit 3 are connected to the CPU such as the arithmetic unit 9, the main control unit 10, and the search unit 5 by a bus or the like may be used.

Next, a data processing method according to the first embodiment will be described with reference to FIG.

First, in step S21, the retrieval unit 5 retrieves information on the wafer W inspected by the visual inspection device 30.
Input means (keyboard,
(A recording medium, a network, or the like) to search for inspection data such as a manufacturing process, a defect position coordinate, and a defect size stored in the inspection data storage unit 21 for the wafer W. Write to.

Incidentally, a semiconductor wafer is formed by film formation, exposure,
It is manufactured by repeating a manufacturing process such as etching, and the inspection by the appearance inspection device 30 is also performed in a plurality of manufacturing processes. Here, FIG. 6 shows the transition of the number of detected defects by the generation process. It can be seen that the defects detected in the previous manufacturing processes A and B are detected again in the manufacturing process C. Therefore, as the manufacturing process progresses, the number of defects already reviewed / analyzed among the number of defects to be reviewed / analyzed increases, resulting in poor time efficiency.

Then, in step S22, as shown in FIG. 7, the arithmetic unit 9 retrieves the coordinate data 211 of the defect on the wafer detected in the current manufacturing process and written into the memory 6 in step S21. And coordinate data 21 of a defect on the wafer detected in the previous manufacturing process
By aligning the coordinate system with 2 and erasing any defects that match or are closer to a preset tolerance, newly found defects likely to have occurred in the current manufacturing process A process of storing the defect coordinate data 213 in the inspection data storage unit 21 (or the review / analysis target data storage units 25a and 25b) may be added. That is, the arithmetic unit 9 can track the manufacturing process in which the defect has occurred in step S22. As a result, it is possible to prevent the defect that has already been reviewed / analyzed in the previous manufacturing process from being reviewed / analyzed again. Of course, in order to investigate what happened in the current manufacturing process, the defect generated in the previous manufacturing process is extracted including the defect generated in the previous manufacturing process, and the criticality judgment and review processing are performed. You may do so. Further, the processing in step S22 may be executed in the appearance inspection device 30.

Further, in step S23, the retrieval unit 5 determines the in-chip area coordinates for each of the chips C arranged on the wafer with reference to the reference mark formed on the wafer based on the information on the wafer. Data storage unit 23
Is retrieved and read into the memory 6.

As shown in the intra-chip layout diagram of FIG. 3, the above-mentioned intra-chip area coordinate data includes a plurality of areas A0, A10 in the chip depending on the type of circuit pattern width (area class value Ac) used, for example. , A20, A3
0, A40, each area is represented by a rectangle,
The lower left coordinates (xs, ys) and the upper right coordinates (x
(e, ye). For example, the area A0 has coordinates C01 and C02, and the area A10 has coordinates C1.
01 and C102, the area A40 has coordinates C401 and C40
It can be represented by 2.

Further, as shown in the chip area coordinate data diagram shown in FIG. 4, the in-chip area coordinate data includes lower left coordinates (xs, ys) and upper right coordinates (xe, ye) of each area.
And an area class value Ac representing the type of area. The area coordinate data in the chip can be manually entered or CAD
Written by reading a file from the data.
Further, since the chips C are arranged at substantially equal pitches on the semiconductor wafer W, the position of the origin (for example, C01) of the in-chip area coordinate data is calculated by the arithmetic unit 9 from the reference position with respect to the semiconductor wafer. It can be easily calculated based on the data.

Next, in step S 24, the search unit 5 determines the defect No. generated on the semiconductor wafer and detected by the visual inspection device 30 based on the input information on the semiconductor wafer W. The position coordinates, feature amount data, and the like regarding N are read from the inspection data storage unit 21 and temporarily stored in the memory 6. Then, the calculation unit 9 determines the defect No. From the position coordinate of N, the origin at which chip (for example, C0
1), and compares the converted position coordinates (xN, yN) with the area coordinates read into the memory 6 to compare the defect No. 1 with the position coordinates (xN, yN). N is determined, and the area class value AcN in that area is determined by the defect No. N is written into the memory 6 in correspondence with N. That is,
The above defect No. In the area where N exists, it is determined whether or not the following expression (Equation 1) is satisfied in the order of the area No., and the area class value Ac is overwritten in the order of the area No.

Xs <xN <xe, ys <yN <ye (Equation 1) Therefore, among the regions satisfying the above (Equation 1), for example,
Area showing the largest value of No (area where a defect exists)
Is calculated, and the defect No. is calculated.
N is written into the memory 6 and the test data storage unit 21 in correspondence with N.

In the above description, the area class value Ac is assigned to each area based on the area coordinates in the chip. However, as described in Japanese Patent Application No. 10-10456, the above-described method is applied. The lower left coordinates (xs, ys) and the upper right coordinates (xe, ye) indicating each area are converted into pixel coordinates IP (I, J) by the following equation (Equation 2), and the converted pixels indicating each area are converted. The area class value Ac may be given to the coordinates.

IXs = Int (xs / P), IXe = Int (xe / P) IYs = Int (xe / P), IYe = Int (xe / P) (Equation 2) where P is a preset pixel pitch , Int are functions for rounding down decimal places.

In this case, it is necessary to convert the position coordinates (x, y) of the defect detected by the visual inspection device 30 into the pixel coordinates (KX, KY) according to the following equation (3).

KX = Int (x / P), KY = Int (y / P) (Equation 3) As described above, by using the in-chip area image data composed of the pixel coordinates, the area in the chip where the defect exists can be determined. It has the advantage that it can be identified instantly.

Next, in step S25, the search unit 5 reads the area-specific fatality determination data (the fatality determination rule) corresponding to each of the areas A0 to A40 in the chip on the wafer W in the manufacturing process read in step S23. Data) R is searched from the area-specific fatality determination data storage unit 24a,
Write to. In this manner, the area-specific fatality determination data written in the memory 6 becomes a determination threshold value (determination reference value) R corresponding to the area class value Ac in the manufacturing process, as shown in FIG. The determination threshold value R is set for each region in the manufacturing process. The defect length R _L , the defect area R _S , and the defect brightness value (shade value) R, which are the feature amounts of the defect, are set. _B , for example, a typical pattern width in each region, or a pattern space, or a design value of a pattern width or a pattern space formed thereon in the next manufacturing process, a design value of a film thickness, or a grain. This value corresponds to process information such as local film thickness fluctuation. That is, since the representative pattern width or the design value of the pattern space differs for each region, it is naturally necessary to change the determination threshold value R, which is the criticality determination data, for each region. For this reason, the criticality determination data R for the defect is switched with reference to the area class value Ac where the defect exists. Of course, the area in the chip is further subdivided, for example, subdivided into each pattern section, each space section, and the like, and the area class value Ac is assigned to each of them, and the determination threshold value is set in correspondence with the assigned area class value Ac. R may be set.

Next, in step S26, the arithmetic unit 9 uses the review device 31 to select a defect to which a category is to be assigned from among the defects generated on the wafer.
For all the defects generated on the wafer detected by the visual inspection device 30, the first fatalities are determined based on the criticality determination rule corresponding to the area where the defects exist, which is retrieved in the step S25 and stored in the memory 6. The calculated first fatality level is stored in the inspection data storage unit 21 in association with the defect. This is a review device 3
This is to reduce the number of defects to which the category is assigned as small as possible and prevent useless review work. However, if the review device 31 has a function capable of recognizing the size of a defect, it is possible to select the defect to some extent on the wafer and automatically assign a category to the defect. In this case, step S28 Becomes unnecessary.

Of course, in step S26, the arithmetic unit 9 determines that the detected defect is a fatal defect based on whether the calculated first fatal level exceeds a predetermined first determination reference value. It is possible to classify it into any of the non-fatal defects and store it in the inspection data storage unit 21.

Next, these steps S24 and S26 will be specifically described with reference to FIG. 8 as described in Japanese Patent Application No. 10-10456. That is, the search unit 5
In step S241, the defect No. generated on the semiconductor wafer and detected by the visual inspection device IA based on the input information on the semiconductor wafer W. Search from N = 1. Then, in step S242, the arithmetic unit 9 determines the defect No. The chip in which the defect exists and the area within the chip are determined from the position coordinates of 1, the chip number is assigned, and the area class value Ac is written. Next, the arithmetic unit 9 determines the defect No. written in the memory 6 in step S261. The determination threshold value R1 which is the criticality determination data corresponding to the area class value Ac1 in the area where 1 exists (for example, the defect length R _L1 corresponding to the area class value Ac1, the defect area R _S 1, the defect area R _S1 , brightness etc. R _B 1) reads the defect stored further in the memory 6 No. of 1, the defect feature data CD1 (for example, the defect length CD _L 1 and the defect area CD _S).
1, defect brightness CD _B 1 and the like), a judgment threshold value R1 which is fatality judgment data corresponding to the read area class value Ac1, and a defect No. The first criticality determination is performed on the defect feature amount data CD1 for No. 1 based on the following equation (4). Here, as the determination threshold value R1 for determining the fatality, the defect length R _L1 , the defect area R _S 1, the defect area R _S 1, which is highly likely to cause a fatality such as a disconnection or a short circuit which causes a circuit operation failure. and the like absolute value R _B 1 in the brightness of the difference from the normal portion of the. However, in the equation (4), it is also possible to select which one has priority according to the category of the defect. Further, as the first fatality determination, the first fatality “presence” / “absence” determination and multi-value data FAL1 such as a ratio indicating the first fatality level (degree) are calculated. Do what you want.

First fatality: CD _L 1 / R1 (here, R1 = R _L 1): CD _S 1 / R1 (here, R1 = R _S 1): CD _B 1 / R1 (here, R1 = R _B 1) (Equation 4) The operation unit 9 calculates the defect No. from the above (Equation 4) 1 is the first
If it is determined that there is a fatality of “Yes”, the process proceeds to step S26.
2, the defect No. The first fatal classification data and the multi-value data FAL1 indicating the first criticality level are written in the memory 6 in correspondence with 1. If it is determined that the first fatality is “absent”, the defect No. is determined in step S263. The first non-fatal classification data and the multi-value data FAL1 indicating the first non-fatal level are written in the memory 6 in correspondence with the first data. In addition,
The fatal classification data can be obtained by looking at the multi-value data FAL1 indicating the one fatality level.

The multi-value data FAL1 indicating the first fatality level takes a simple ratio value in the above equation (4). This can be calculated using a function, a look-up table, or the like so that the influence on the circuit pattern is represented with high accuracy.

As described above, the defect No. This means that the criticality determination was made for No. 1.

Next, the arithmetic unit 9 determines in step S264
The defect number is set to the next defect number (N = N + 1), and the above-described steps S242, S261, S262, and S263 are repeated until it is determined in step S265 that all the defects generated on the semiconductor wafer W have been completed. Accordingly, for all defects generated on the semiconductor wafer W, the first fatalities are obtained from the above equation (Equation 4) based on the feature amount CD and the determination threshold value R set in correspondence with the existing area. The sex determination and the calculation of the multi-value data FAL indicating the first fatality level in the case of the presence of the first fatality are performed and stored in the memory 6 in association with the defect No.

The calculation unit 9 determines in step S265
When it is determined that the first criticality determination has been completed for all the defects generated on the semiconductor wafer W, the main control unit 10
In step S266, steps S262 and S26
The first fatality determination result written to the memory 6 in the third (first
And the area class value Ac written in the memory 6 in step S242, if necessary, in correspondence with each defect No., for example, in the inspection data storage unit 21. Write to.

As described above, for example, the inspection data storage unit 21
With respect to a certain semiconductor wafer W, the appearance inspection device 30
The inspection data ID and the fatality judgment result output from the main control unit 10 are stored in association with each defect.
And / or the calculation unit 9 calculates the number of first fatal defects generated on the inspection wafer in wafer units or lot units as the management value of the production line shown in FIG.
The review target data can be stored in the review target data storage unit 25a so that the review station 31 can preferentially perform the review in the review station 31 in the order of the first criticality level. Of course, as a management value of the number of fatal defects having a correlation with the yield shown in FIG.
It is better than improving accuracy.

By the way, in the case of a defect or a foreign substance as a defect generated on a wafer, the defect may be generated densely due to a factor for generating the defect. In this case, naturally, the appearance inspection device 30 also detects a dense defect (cluster defect) composed of a large number of defects and stores the defect in the inspection data storage unit 21. However, in the case of a cluster defect, the same factor is generated on the wafer. Therefore, it is not necessary to analyze all the cluster defects generated on the wafer, and it is sufficient to analyze only a few defects in the cluster defect. Become.

Then, in step S27, the arithmetic unit 9 calculates the figure based on the coordinate data (x, y) of the defect newly found on the wafer, which is retrieved from the inspection data storage unit 21 and written into the memory 6. As shown in FIG. 8, it is determined whether or not a pixel belongs to a cluster defect (whether it belongs to a cluster defect or a random defect). If the pixel belongs to a cluster defect, a cluster number is assigned to each defect. It is stored in the inspection data storage unit 21. As described above, in step S27, it is determined whether or not a new defect detected from the wafer belongs to a cluster defect. Therefore, as described later, a review target to be reviewed and assigned a category is further determined. It is possible to narrow down. By the way, the determination as to whether or not a defect belongs to a cluster defect is performed by determining that defects having similar coordinates belong to the same cluster defect, and assigning the same cluster classification number to each defect. Become. Naturally, those belonging to the same cluster defect belong to the same category. Therefore, as shown in FIG. 9, the same category as the category (review classification) given after the review is given. The determination as to whether or not a defect belongs to the above-described cluster defect is performed by associating dense defects with a short distance as one group. However, the size, brightness, color,
If physical characteristics such as shapes are taken into the inspection data storage unit 21, the arithmetic unit 9 performs grouping based on the physical characteristics of these defects (by one of these or any combination of these conditions). It is also possible to do. Also in this case, it is premised that the grouped items may be treated as the same category.

As described above, by providing step S27, defects having the same physical characteristics are grouped as well as cluster defects, as well as cluster defects. It is possible to obtain an analysis result for the entire group simply by performing a review and analysis on one or several defects among them.

In step S 27, the search unit 5 selects a predetermined number of defects from the inspection data storage unit 21 and writes them to the memory 6 for group defects such as cluster defects.

Next, the arithmetic unit 9 determines whether a new defect has occurred on the wafer and has been written into the memory 6.
According to the first fatality level calculated in step S26, a defect to which a category is assigned is selected, its position coordinates are acquired, and stored in the review target data storage unit 25a via the memory 6 or the input / output interface 7. I do. In this selection, not only lethal defects but also non-lethal defects having a high level close to lethal defects are selected. At this time, those belonging to the group defect are further narrowed down as defects to which a category is assigned. By the way, since a group defect such as a cluster defect occurs across different types of regions in a chip, narrowing down is performed after calculating the first criticality level.

Next, in step S 29, the target wafer is put into the review device 31, and the position coordinates of the selected defect are stored in the review target data storage unit 2.
By transmitting the defect to the review device 31 via the network Nt from the device 5a, the review device 31 automatically assigns a category to the selected defect, for example.
A category is assigned to a corresponding defect and stored as inspection data in the inspection data storage unit 21 via the network Nt. Therefore, the search unit 5 can read the category from the test data storage unit 21 and write the category into the memory 6. The inspection data storage unit 21
For example, the review image itself observed or detected by the review device 31 may be stored in association with a defect to which a category is assigned.

As described above, with respect to the defect generated on the wafer detected by the visual inspection device 30, it is possible to determine the final target second criticality with high accuracy for each category and region according to the present invention. You are ready.

Next, in step S30, the retrieval unit 5 determines the criticality determination data (weighting function or the like) for each category corresponding to the defect category (CAn) K (CAn) (for example, K _L (CAn), K _S ). (CAn), K _B a (CAn) and the like.), and searches the category fatality judgment data storage unit 24b, written in the memory 6. In addition,
In the next step S31, it is sufficient that the category-specific fatality determination data K (CAn) is prepared in the memory 6 or the like. Next, in step S31, the arithmetic unit 9 sets the first criticality for each of the various feature amounts (defect length, defect area, defect brightness, etc.) calculated according to the region where the defect exists. level _{FAL (CD L N / R L} N, CD S N / R S N, C
Weighting functions (K _L (CA 1) to D _B N / R _B N) set for each of the categories (CA 1 to CAn) assigned to the defect stored in the memory 6 retrieved in step S 30. _{K L (CAn), K S} (CA1) ~
K _S (CAn), by summing based on the _{K B (CA1) ~K B (} CAn)) or shows a hanging in example then a look-up table (number 5), the second as a multi-level data The fatality level FAH (CAn) is calculated, and the calculated second fatality level FAH (CAn) is stored in the inspection data storage unit 21 in association with the defect. Note that CAn indicates a defective category. And
This multi-valued data indicates the second level of fatality.

Of course, in step S31, the arithmetic unit 9 sets the calculated second fatality level FAH (CA
Depending on whether or not n) exceeds a predetermined second determination reference value, it is possible to classify the detected defect into a fatal defect or a non-fatal defect and store it in the inspection data storage unit 21. It is possible. By the way, even if the detected defect is a group defect such as a cluster defect, the defect is classified into fatal and non-fatal according to each area in the chip where the defect occurred, so that a large number of defects constituting the group defect In contrast, a fatal defect or a non-fatal defect is provided for each region. This makes it possible to calculate the number of true fatal defects generated on the wafer.

[0074] _{FAH (CAn) = (CD L} N / R L N) · K L (CAn) + (CD S N / R S N) · K S (CAn) + (CD B N / R B N) · K _B (cAn) + ... (5) in particular, the calculation of the second fatality level from the probe test result as described later, to represent higher more accurate the influence on the circuit pattern, a function or a look by category It is also possible to calculate using an up table or the like.

Therefore, the main control unit 10 and / or the arithmetic unit 9 sets the management value of the production line shown in FIG.
The analysis target data storage unit 25b is used as the analysis target data so that the number of the fatal defects can be calculated or the analysis device 40 can preferentially analyze the second fatality level in descending order. Can be stored.

Further, in step S32, the calculating section 9 calculates the second fatality level FAH (CAn) calculated in step S31 by weighting for each category.
Are simply rearranged in the descending order, in the order of priority area (area class value Ac), or for each category.

On the other hand, in step S33, the number Nmax of defects to be analyzed, the priority area or category, and the maximum number Icmax analyzed per chip are input from the display unit 11 in advance to the wafer to be analyzed (display unit 11).
Is provided with an input function unit), and is stored and set in the memory 6 via the data input / output unit 4.

Next, in step S34, the arithmetic unit 9 sets Nmax from the top in the arrangement order after the processing in step S32.
(The defect having the highest lethality occurring on the wafer W and having the maximum Nmax) is selected, and in step 35, the selected defect is stored together with its position coordinates in the analysis target data storage unit 25.
Write to b. In step S34, the calculation unit 9 selects a second fatal defect occurring in the area (area class value Ac) to be preferentially analyzed. In step 35, the selected defect is displayed together with its position coordinates. It is also possible to write in the analysis target data storage unit 25b. Also,
When selecting a defect, if the number of selections per chip exceeds Icmax, the calculation unit 9 may add a process of not selecting a defect exceeding Icmax. By doing so, even when defects are concentrated on the same chip, it is possible to select a defect to be analyzed from the entire wafer.

Further, since the category is assigned to the defect in step S29, the calculating section 9 can select in order from the second one with the highest fatality in each category in step S34. It is also possible to write in the analysis target data storage unit 25b together with the position coordinates.

Further, the defect selected in step S34 may be assigned the category assigned in step S29 and written into the analysis target data storage 25b. By doing so, it becomes possible to select the defects to be analyzed by the analysis device 40 in the second order of higher fatality by category.

As described above, among the defects generated on the wafer W manufactured in a certain manufacturing process, the analysis device 40
The second fatal thing that must be analyzed with
In addition, for the same analysis result expected, the defect to be analyzed is selected by narrowing down the number, and the position coordinates are obtained and stored in the analysis target data storage unit 25b.

On the other hand, in step S36, as shown in FIG. 11 as the selection of an important defect, the arithmetic unit 9 determines the criticality (second criticality level FA) calculated in step S31.
By combining H) mainly with other selection conditions, a defect to be analyzed may be selected and written to the analysis target data storage unit 25b. As other selection conditions, step S24
Area in the chip where the defect obtained from step S22 exists, the size of the defect obtained in step S22 (the length of the defect in the X and Y-axis directions and the area of the defect), and the chip where the defect exists on the wafer obtained in step S24 ( Repeatability between chips), defect categories, and the like can be considered. FIG.
As an example of selection conditions, as an example of a selection condition, in the sense amplifier section (SA) as a region, the criticality is 80 or more (a defect of a certain size or more), the length of the defect in the X direction is 0.4 μm or less, and the Y direction is Is selected to have a length of 0.4 μm or more. In short, in the sense amplifier section, it is necessary to select a long and narrow defect with a high degree of fatality in a defect having a certain size or more. Then, the inspection data processing apparatus 1 provides the analysis target data stored in the analysis target data storage unit 25b to, for example, the appearance inspection apparatus 30 so that the specific mode (area, size) at the time of setting the inspection conditions can be determined. The efficiency of defect confirmation can be improved. In addition, the inspection data processing device 1 includes an analysis device 4
With respect to 0, it is possible to preferentially extract defects having high importance during mass production as analysis points.

Further, the analyzer 40 must analyze information from the analysis target data storage unit 25b of the inspection data processing apparatus 1 by inputting information on the wafer W to be analyzed using input means or the like. Information on a small number of defects to be analyzed can be obtained, and the material, properties, cross-sectional shape, etc. of these defects can be analyzed to estimate the cause of the fatal defect. It is possible to feed back to the manufacturing line management device, the visual inspection device 30, or the inspection data processing device 1 via Nt. Therefore, the production line manager or the like can quickly improve the yield of semiconductor devices by quickly taking measures to remove the cause of the defect estimated based on the analysis result.

In particular, the inspection data storage section 21 stores the inspection data ID for the defect generated on the wafer detected by the visual inspection device 30, the area in the chip where the defect exists, the category of the defect, and the second Information such as a criticality level (including presence / absence) is stored, and defect information to be analyzed by the analysis device 40 is stored in the analysis target data storage unit 25a. It is possible to search and extract useful information on a fatal defect. Accordingly, the main control unit 10 outputs and displays the extracted useful information on, for example, the display unit 11 via the data input / output unit 4 or the network Nt.
, It can be provided to an external manufacturing line management device or the like. Thus, the second in step S31
Is based on the use of defect categories,
Accuracy is improved and useful information can be provided.

FIG. 12 shows fixing of the occurrence of defects detected by the visual inspection device 30 on the semiconductor manufacturing line. That is, a defect generated in a certain manufacturing process is extracted and fixed in step S22. As described above, in the trace inspection / analysis for tracing the manufacturing process in which the defect has occurred, the appearance inspection device 3
Therefore, high sensitivity and high throughput of 0 are required. However, if the first generated defect is, for example, a foreign substance, the disconnection defect such as film peeling or film thinning is affected by the foreign substance defect unless the foreign substance is removed. Will happen. That is, the appearance inspection device 30
Then, in the first step, a foreign substance defect is detected, and in the next step, a category is changed and detected as a circuit pattern defect such as a disconnection. That is, as shown in FIG.
Although only Category 3 was detected in the previous manufacturing process, new Category 1 and 2 defects are detected in the last manufacturing process. However, since the defect is caused by a foreign matter defect, the inspection data processing apparatus 1 must detect the foreign matter defect detected by the visual inspection device 30 in the first step with high fatality. However, since weighting is performed for each category in step S31, it is possible to detect the foreign matter defect detected in the first step with a high second fatality level.
b or by outputting the second highly fatal defect stored in the inspection data storage unit 21, it is possible to know that a foreign matter defect has occurred in the first step. And quickly take countermeasures.

Further, the inspection data processing apparatus 1 checks the number of fatal defects, especially the second fatal defects, generated on the wafer in wafer units or lot units shown in FIG. By providing, it is possible to output a truly abnormal state and issue a warning,
Enables production line management. In particular, as shown in FIG. 10A, between the number of fatal defects generated on the inspection wafer shown in FIG.
By obtaining the correlation, it is possible to take measures for reducing the number of fatal defects on the manufacturing line, and as a result, it is possible to improve the yield of semiconductor chips as products.

In addition, since the inspection data processing apparatus 1 can assign a category to most defects in step S29, the defect size (characteristic) is determined for each region in the chip determined in step S24. It is possible to calculate the number of defects for each category with respect to the area which is one of the (quantity). Therefore, as shown in FIG. 13, the display unit 11 and the display unit of the external manufacturing line management device display the area, which is the feature amount of the defect, and the vertical axis, the number of defects by category, as shown in FIG. The output graph can be displayed. As a result, by finally comparing the result with the operation test result of the probe test, it is possible to find out the defect that lowers the yield, and to take immediate measures.

Further, for example, on the display unit 11, the distribution map information of the second fatal defect and the image data (including the category information) of the defect detected by the review device 31 are selectively displayed. It is possible to do.

When automatically assigning a category to a defect image detected using the review device 31 in step S29 and classifying the defect image, only a more fatal defect image is selected from step S28. And unnecessary review work can be prevented. This leads to a reduction in work time and a reduction in work load in the review work.

Also, useful information on the above defects is
By providing the inspection data processing device 1 to the appearance inspection device 30, it is possible to optimize the inspection conditions so that the appearance inspection device 30 can detect defects with high fatality without fail.

Further, based on the inspection data on the defects detected from the wafers, which are substantially the same inspection object, from each of the plurality of visual inspection apparatuses 30, the second criticality determination is performed as described above. By outputting the distribution map of the second fatality determination result on the wafer as a Venn diagram to the display unit 11, it is possible to visually grasp the machine difference of the visual inspection apparatus. Also,
When a plurality of different types of visual inspection devices, such as a foreign material visual inspection device and a circuit pattern defect visual inspection device, are used as the plurality of visual inspection devices 30, the difference between these performances is represented by a second criticality scale. It is possible to evaluate objectively, and it is possible to appropriately use the appearance inspection device properly.

Further, when the wafer W is processed with a film forming apparatus, an exposure apparatus, and an etching apparatus, and when the wafer W is almost completed, an electrical inspection called a probe inspection is performed to determine the electrical characteristics of each chip. become. Therefore, this probe inspection data is transmitted from the probe inspection device to the network N
By inputting the data to the test data processing device 1 via the “t”, the data is compared with the second fatality data stored in the test data storage unit 21, the difference between the two fatalities is grasped, and based on this difference, By modifying the process of deriving the second fatality data (eg, a region-based determination rule or a category-based weighting function), more accurate criticality determination can be performed.

The inspection data processing device 1 also uses the critical defect data relating to the fatality stored in the inspection data storage unit 21 and the analysis result analyzed by the analysis device 40 to determine
The contents can be searched and the data can be edited. By outputting and confirming these results on the display unit 11, it is possible to quickly perform a measure for a process or a manufacturing apparatus which is presumed to have a defect. .

Further, the inspection data processing apparatus 1 for determining the defect fatality, selecting the defect to be reviewed / analyzed, and editing / searching the inspection data may be incorporated in the main body of the visual inspection apparatus 30 without using the network Nt. In this case, it is possible to grasp the details of the defect information on the appearance inspection device 30 with the wafer W mounted thereon.

The inspection data processing apparatus 1 uses the input inspection data having the chip identification coordinates, the coordinates in the chip, the defect coordinates, the defect size, the defect category, and the like to perform the review / analysis order and the review / analysis. You can edit the target. Therefore, a review / analysis system capable of controlling the review / analysis order and the review / analysis target can be constructed. The review station 31 in this review / analysis system may be an SEM. Also,
By providing an editable viewer, such as browsing the images detected by the review station 31 and numbering, the user can freely edit the review results and use it for report creation. Further, even when a cross-sectional photograph of a defective portion is taken by an SEM, data can be more efficiently narrowed down by using the above-described inspection data processing method.

By the way, as the analyzing device 40, for example, in a FIB (Focused Ion Beam) analyzing device, the stage is moved to a defective portion, and the defective portion is halved by irradiating FIB or the like, and a half of the defective portion is photographed by an SEM photograph. Analysis by cross-sectional observation can be performed. Of course, the present invention can be applied to effective use of an analyzer such as an elemental analyzer.

In the above-described embodiment, a semiconductor wafer is described as an example of an object to be inspected. However, the present invention can be applied to inspection of another object to be inspected such as a liquid crystal substrate having a circuit pattern. . It goes without saying that various modifications can be made without departing from the spirit of the present invention.

[0098]

According to the present invention, the fatality of a defect is determined according to the type of the area where the defect exists and the category of the defect based on the inspection data of the defect detected from the visual inspection apparatus. Critical defects can be selected accurately and accurately, and as a result, it is possible to narrow down to those that need to be truly reviewed or analyzed, thereby enabling efficient review and analysis work At the same time, it is possible to manage the production line in correlation with the yield.

[Brief description of the drawings]

FIG. 1 is a configuration diagram showing a first embodiment of a system using an inspection data processing device according to the present invention.

FIG. 2 is a flowchart illustrating an example of a processing flow of inspection data according to the first embodiment of the present invention.

FIG. 3 is an explanatory diagram showing an example of in-chip area data used in the embodiment of the present invention.

FIG. 4 is an explanatory diagram showing an example of in-chip area coordinate data used in the embodiment of the present invention.

FIG. 5 is an explanatory diagram illustrating an example of an area class value Ac and fatality determination data (defect determination threshold value) corresponding thereto used in the embodiment of the present invention.

FIG. 6 is an explanatory diagram showing the transition of the number of defects for each generation step.

FIG. 7 is an explanatory diagram schematically showing a defect in a current step, a defect in a step before the current step, and a state after deleting a defect in a previous step from the defect in the current step.

FIG. 8 is a diagram showing a detailed flow of a first criticality level calculation shown in FIG. 2;

FIG. 9 is an explanatory diagram showing an example of a cluster defect and a defect to be reviewed / analyzed in the cluster.

FIG. 10 is an explanatory diagram showing a state of correlation between the number of fatal defects and the yield according to the present invention, and management based on the number of fatal defects.

FIG. 11 is an explanatory diagram showing one embodiment of a condition for selecting an important defect according to the present invention.

FIG. 12 is a diagram illustrating fixing of a defect generation process detected according to the progress of a manufacturing process.

FIG. 13 is a graph showing the number of defects by category with respect to the defect area calculated for each region in the chip.

[Explanation of symbols]

DESCRIPTION OF SYMBOLS 1 ... Inspection data processing apparatus, 3 ... Communication control part, 4 ... Data input / output part, 5 ... Search part, 6 ... Memory, 7 ... Input / output interface, 8 ... Program storage part, 9 ... Operation part, 10 ...
Main control unit, 11 display unit, 21 inspection data storage unit, 2
3: chip area coordinate data storage unit, 24a: determination rule data storage unit by area, 24b: determination rule data storage unit by category, 25a: review target data storage unit, 2
5b: Analysis target data storage unit, 30: Appearance inspection device, 3
1. Review device, 40: Analysis device, Nt: Network.

 ──────────────────────────────────────────────────続 き Continued on the front page (72) Inventor Kenji Oka 882 Ma, Hitachinaka-shi, Ibaraki Pref.Hitachi, Ltd.Measurement Instruments Group (72) Inventor Atsushi Shimoda 292 Yoshida-cho, Totsuka-ku, Yokohama-shi, Kanagawa Prefecture (72) Inventor Masataka Shiba 292 Yoshida-cho, Totsuka-ku, Yokohama-shi, Kanagawa Prefecture F-term (reference) 2G051 AA51 AA56 AB01 AB07 EC01 EC10 FA10 4M106 CA38 DB21 DJ20 DJ21 DJ27 5B057 AA03 DA03 DA04 DA07 DA08 DC03 DC04 DC22 DC36 DC39

Claims (27)

[Claims] An inspection data processing method for processing inspection data composed of position coordinate data and feature amount data on a defect generated on an inspection object detected by a visual inspection apparatus, the inspection data processing method comprising: A preparatory step of preliminarily storing a criticality determination data group corresponding to each type of a plurality of regions formed on the object; and a case where the defect exists based on position coordinate data on the defect in the inspection data. An area determination step for determining the type of area, and from the lethality determination data group stored in the preparation step, fatality determination data corresponding to the type of the area determined in the area determination step is selected, and in the inspection data, A criticality determining step for determining the criticality of the defect based on the selected criticality determination data based on the characteristic amount data of the defect. Law. 2. An inspection data processing method for processing inspection data consisting of position coordinate data and feature amount data on a defect generated on an inspection object detected by a visual inspection apparatus, the inspection data processing method comprising: A preparatory step of preliminarily storing a first criticality determination data group corresponding to each type of a plurality of regions formed on the target object and a second criticality determination data group corresponding to the defect category; An area determining step of determining a type of an area in which the defect exists based on position coordinate data on the defect in the inspection data; and a first fatality determination data group stored in the preparation step, First fatality judgment data corresponding to the obtained type of region is selected, and a defect is determined based on the selected fatality judgment data based on the defect feature amount data in the inspection data. A first fatality determination step for determining a first fatality level, a category assignment step of assigning a category to a defect in the inspection data, and a second fatality determination data group stored in the preparation step. Selecting second fatality determination data corresponding to the category assigned in the category assignment step, and selecting the second fatality determination data based on the first fatality level of the defect determined in the first fatality determination step. A second criticality determining step of determining a second criticality of the defect based on the performed second criticality determination data. 3. A defect is generated by comparing the inspection data of the inspection object manufactured up to the previous manufacturing process with the inspection data of the inspection object manufactured up to the subsequent manufacturing process. 3. The inspection data processing method according to claim 2, further comprising a tracing step of acquiring said inspection data for a defect traced in a manufacturing process. 4. The inspection data processing method according to claim 2, further comprising an output step of outputting information relating to a second fatality of the defect determined in said second fatality determination step. 5. The inspection data processing method according to claim 2, further comprising an output step of outputting information relating to a first fatality of the defect determined in the first fatality determination step. 6. The method according to claim 2, further comprising a selection step of selecting an analysis target according to a second fatality level of the defect determined in said second fatality determination step. Inspection data processing method. 7. The inspection data processing method according to claim 6, further comprising an analysis step of analyzing a defect selected in said selection step using an analysis device. 8. The inspection data processing method according to claim 2, wherein the second criticality determination data group stored in the preparation step is a group of weighting functions. 9. The inspection data processing method according to claim 2, wherein the category assigning step is performed using a review device. 10. The category assigning step includes a step of assigning a category to a defect selected according to a first fatality level of the defect determined in the first fatality determining step. The inspection data processing method according to claim 2, wherein: 11. The inspection data processing method according to claim 6, further comprising an output step of outputting information on a defect to be analyzed selected in said selecting step. 12. The inspection according to claim 2, wherein the preparing step includes a step of correcting any one of the first and second lethality judgment data groups based on a final probe test result. Data processing method. 13. An inspection data processing method for processing inspection data consisting of position coordinate data and feature amount data on a defect generated on an inspection object detected by a visual inspection apparatus, wherein the defect category comprises: A preparatory step of preliminarily storing a group of lethality judgment data corresponding to the above, a category assigning step of assigning a category to a defect in the inspection data, and A criticality determination data corresponding to the category assigned in the process, and a criticality determination step of determining the criticality of the defect based on the selected criticality determination data. Method. 14. An inspection data processing method for processing inspection data consisting of position coordinate data and feature amount data of a defect generated on an inspection object detected by a visual inspection apparatus, the inspection data processing method comprising: Comparing the inspection data for the inspection object manufactured up to the manufacturing process with the inspection data for the inspection object manufactured up to the subsequent manufacturing process, and tracing the manufacturing process in which the defect has occurred. A tracing step of acquiring data; an area determining step of obtaining a type of an area where the defect exists based on position coordinate data on the defect in the inspection data acquired in the tracing step; and a defect acquired in the tracing step. A category assigning step of assigning a category to the area, and the trace overrun is performed for each area determined in the area determining step. Calculating the number of defects for each category assigned in the category assigning process according to the feature amount of the defect in the inspection data obtained in the step, and the feature amount of the defect for each area calculated in the calculating process An output step of outputting the number of defects for each category according to the inspection data processing method. 15. An inspection data processing apparatus for processing inspection data consisting of position coordinate data and feature amount data on a defect generated on an inspection object detected by a visual inspection apparatus, the inspection data processing apparatus comprising: A storage unit in which a first fatality determination data group corresponding to each type of a plurality of regions formed on the object is stored in advance, and the defect is determined based on position coordinate data on the defect in the inspection data. An area determining unit for determining the type of the area where the area exists, and fatality determination data corresponding to the type of the area determined by the area determining unit selected from the first fatality determination data group stored in the storage unit. And a criticality determining unit that determines the criticality of the defect based on the selected criticality determination data based on the characteristic amount data of the defect in the inspection data. Management apparatus. 16. An inspection data processing apparatus for processing inspection data composed of position coordinate data and feature amount data on a defect generated on an inspection object detected by a visual inspection apparatus, the inspection data processing apparatus comprising: A storage unit that previously stores a first fatality determination data group corresponding to each type of the plurality of regions formed on the target object and a second fatality determination data group corresponding to the defect category; An area determining unit that obtains a type of an area where the defect exists based on position coordinate data on the defect in the inspection data; and a first fatality determination data group stored in the storage unit. First fatality determination data corresponding to the obtained type of region is selected, and a first defect determination data is determined based on the selected criticality determination data based on the defect feature amount data in the inspection data. A first fatality determining unit that determines a level of vitality, a category assigning device that assigns a category to a defect in the inspection data, and a second fatality determination data group stored in the storage unit. The second fatality determination data corresponding to the category given by the giving device is selected, and the selected second fatality determination data is determined based on the first fatality level of the defect determined by the first fatality determination unit. An inspection data processing device comprising: a second fatality determining unit that determines a second fatality of a defect based on the second fatality determination data. 17. A defect is generated by comparing the inspection data of the inspection object manufactured up to the previous manufacturing process with the inspection data of the inspection object manufactured up to the subsequent manufacturing process. 17. The inspection data processing apparatus according to claim 16, further comprising a tracing unit for acquiring the inspection data on a defect traced in a manufacturing process. 18. An inspection data processing apparatus according to claim 16, further comprising an output unit for outputting information on a second fatality of the defect determined by said second fatality determining unit. . 19. An inspection data processing apparatus according to claim 16, further comprising an output unit for outputting information relating to a first fatality of a defect determined by said first fatality determining unit. . 20. The apparatus according to claim 16, wherein said second criticality determining section has a selecting section for selecting an analysis target according to a second criticality level of a defect to be determined. Inspection data processing device. 21. The inspection data processing device according to claim 20, further comprising an analysis device for analyzing a defect selected by said selection unit. 22. The inspection data processing apparatus according to claim 16, wherein the second criticality determination data group stored in the storage unit is a group of weighting functions. 23. The inspection data processing device according to claim 16, wherein said category assigning unit is constituted by a review device. 24. The category assigning section assigns a category to a defect selected according to a first fatality level of a defect determined by the first fatality determining section. 17. The inspection data processing device according to claim 16, wherein: 25. The inspection data processing apparatus according to claim 20, further comprising an output unit for outputting information on a defect to be analyzed selected by said selection unit. 26. An inspection data processing device for processing inspection data consisting of position coordinate data and feature amount data on a defect generated on an inspection object detected by a visual inspection device, comprising: a defect category; A storage unit in which a criticality determination data group corresponding to the above is stored in advance; a category assigning unit that assigns a category to a defect in the inspection data; and a category assignment from the fatality determination data group stored in the storage unit. Inspection data, comprising: a fatality judgment data corresponding to the category given in the process, and a fatality judgment unit for judging the fatality of the defect based on the selected fatality judgment data. Processing equipment. 27. An inspection data processing apparatus for processing inspection data consisting of position coordinate data and feature amount data on a defect generated on an inspection object detected by a visual inspection apparatus, the inspection data processing apparatus comprising: Comparing the inspection data for the inspection object manufactured up to the manufacturing process with the inspection data for the inspection object manufactured up to the subsequent manufacturing process, and tracing the manufacturing process in which the defect has occurred. A tracing unit for acquiring data; an area determining unit for determining a type of an area where the defect exists based on position coordinate data on the defect in the inspection data acquired by the tracing unit; and a defect acquired by the tracing unit. And a category assigning unit that assigns a category to each of the areas determined by the area determining unit. A calculating unit that calculates the number of defects for each category assigned by the category assigning unit according to the feature amount of the defect in the inspection data; and a category that corresponds to the feature amount of the defect for each area calculated by the calculating unit. And an output unit for outputting the number of defects.