JP2004153228A - Method for analyzing defect information and its apparatus - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an apparatus for effectively extracting equipment in which a trouble has occurred, in a manufacturing process of thin film devices. <P>SOLUTION: In the manufacturing process of thin film devices, the apparatus extracts the candidates of manufacturing equipment in which a trouble has occurred, by evaluating the relationship between data obtained in relation to product inspection for a product, and data indicating the state of manufacturing equipment, for effectively extracting equipment in which a trouble has occurred. <P>COPYRIGHT: (C)2004,JPO

Description

【０１４８】
ここで、ｉは工程、ｊは装置番号、ｋはチャンバ番号、Ｐｎ（ｉ，ｊ，ｋ）は各評価項目ごとの評価値、ｚは評価項目数、Ａｎは評価項目ごとの重み係数である。このように、各評価項目ごとの評価値を各装置・チャンバごとに重み係数をかけて足し合わせ、関連度評価値の大きさを判定することにより、異常発生ウェハに関連する装置を抽出することができる。
【０１４９】
この算出手法は開示した手法に限定するものではなく、関連度を評価できる手法であれば構わない。ここで、重み係数はユーザが任意に設定してもよく、あるルールに従って自動的に配分されるようにしてもよい。ルールの設定方法として、例えば、ある装置にのみ用いられている元素が欠陥に含まれていたり、ある装置にのみ現れる分布のパタンが検出された時に、それぞれ元素情報、分布情報の重みを増すようにしてもよい。
【０１５０】
そして、Ｓｔｅｐ５０３において、この結果に応じて、図１７に示した表示画面にて、該当する装置の文字を色を変えて表示するなど、他の装置と異なることがわかるように表示すればよい。また、図示しないメールシステム等により、関連するスタッフに抽出された装置の情報を通知するようにしてもよい。また、関連度に応じて順位付けをし、複数の加工装置・チャンバを表示するようにしてもよい。
【０１５１】
本実施例では、関連度評価エンジン１７がデータ解析装置７に搭載されている例を示したが、前記説明した情報を用いて計算処理を行うことができれば、独立して存在してもよく、他の別の装置に含まれていても構わない。
【０１５２】
【発明の効果】
本発明によれば、検査により異常と判定されたウェハに関与した加工装置を容易に特定でき、合わせて、該ウェハが着工された期間付近での加工装置ごとの欠陥の発生頻度といった状態を表示できるため、問題の発生した装置の推定が容易になった。
【０１５３】
また、本発明によれば、装置において発生する欠陥に関連して得られるデータと異常となった製品ウェハの欠陥から得られるデータを自動的に比較、評価し、製品ウェハの欠陥と関連の高い装置の候補を抽出できるため、問題の発生した装置を特定するのに要する工数を大幅に削減できるようになった。
【図面の簡単な説明】
【図１】本発明の概念を示す図である。
【図２】本発明の構成の一例を示す図である。
【図３】本発明に用いる製造経路情報の一例を示す図である。
【図４】本発明に用いる製造経路情報の一例を示す図である。
【図５】本発明のデータベースに記録するデータ取得方法の一実施例を示すフローチャートである。
【図６】本発明の適用手順の一実施例を示すフローチャートである。
【図７】本発明のデータ解析・表示方法の一実施例を示す図である。
【図８】本発明のデータ解析・表示方法の一実施例を示す図である。
【図９】本発明のデータ解析方法の一実施例を示すフローチャートである。
【図１０】本発明のデータ解析結果表示方法の一実施例を示す図である。
【図１１】本発明のデータ解析方法の一実施例を示すフローチャートである。
【図１２】本発明のデータ解析結果表示方法の一実施例を示す図である。
【図１３】本発明のデータ解析結果表示方法の一実施例を示す図である。
【図１４】本発明のデータ解析結果表示方法の一実施例を示す図である。
【図１５】本発明のデータ解析結果表示方法の一実施例を示す図である。
【図１６】本発明のデータ解析結果表示方法の一実施例を示す図である。
【図１７】本発明のデータ解析結果表示画面の一実施例を示す図である。
【図１８】本発明のデータ解析結果表示画面の一実施例を示す図である。
【図１９】本発明のデータ解析結果表示画面の一実施例を示す図である。
【図２０】本発明のデータ解析結果表示画面の一実施例を示す図である。
【図２１】本発明のデータ解析結果表示画面の一実施例を示す図である。
【図２２】本発明のデータ解析結果表示画面の一実施例を示す図である。
【図２３】本発明の構成の一例を示す図である。
【図２４】本発明のデータ解析方法の一実施例を示すフローチャートである。
【図２５】本発明のデータ解析方法の一実施例を示す説明図である。
【図２６】本発明のデータ解析方法の一実施例を示す説明図である。
【図２７】本発明のデータ解析方法の一実施例を示すフローチャートである。
【図２８】本発明のデータ解析方法の一実施例を示す説明図である。
【図２９】本発明のデータ解析方法の一実施例を示す説明図である。
【図３０】本発明のデータ解析方法の一実施例を示す説明図である。
【符号の説明】
１…製造装置 ２…検査装置 ３…詳細解析装置 ４…工程管理データベース
５…装置状況データベース ６…検査・解析データベース ７…データ解析装置 ８…表示装置 ９…ネットワーク １０…結果表示画面 １１…製品ウェハ情報表示エリア １２…装置情報表示エリア １３…ウェハ識別情報表示エリア １４…ウェハの検査情報・解析情報表示エリア １５…装置経路情報表示エリア １６…着工日時表示バー １７…関連度評価エンジン[0001]
TECHNICAL FIELD OF THE INVENTION
The present invention relates to a method for specifying a defective process and an apparatus in a thin film device manufacturing process based on inspection information.
[0002]
[Prior art]
The manufacture of thin-film devices such as semiconductors, liquid crystal displays, hard disk magnetic heads, etc., consists of a number of processes.
[0003]
The number of such processing steps sometimes reaches several hundred. When an abnormal appearance such as a foreign substance or a broken wire occurs on a thin film device due to an insufficient manufacturing condition or an abnormality of a processing apparatus, the probability of occurrence of a defect in a product increases, and the yield decreases. Therefore, it is important to identify a device in which a problem has occurred and to take a countermeasure for maintaining and improving the yield. For this reason, inspections such as a foreign substance inspection and an appearance inspection are performed for each main process, and whether or not the processing is performed normally is monitored. At this time, since it is impossible to perform inspection of all the substrates to be processed for each processing process due to time and labor constraints, it is usually necessary to perform lots or substrates to be processed for each of a series of processes. Inspection is performed on the substrate to be processed sampled by the above or a combination thereof. Here, the substrate to be processed means a minimum unit for processing a product, and in the case of a semiconductor, indicates one wafer.
[0004]
When an abnormality such as frequent occurrence of foreign matter / appearance abnormality occurs, a detailed analysis for specifying a generator is performed for the corresponding individual. For example, the occurrence process is estimated by performing detailed observation of the singular point position output by the inspection device and observing the shape of the foreign matter or appearance abnormality. In addition, a cross section that crosses a part of the foreign matter / appearance abnormal region is created, which layer among a plurality of films is estimated, and an EPMA (Electron Probe Micro Analysis: Electron Probe Micro Analysis) or AES (Electron Probe Micro Analysis) is used. Auger Electron Spectroscopy (Auger Electron Spectroscopy) or micro-Raman spectroscopy or micro-fluorescence spectroscopy, etc., obtains spectra related to elements and elemental information contained in foreign matter and defects, and analyzes the spectra to obtain the names of substances that constitute foreign matter and defects. And estimate which manufacturing process is most likely to have occurred. Then, countermeasures have been taken by performing maintenance such as cleaning a device having a high possibility.
[0005]
Here, the “singular point” refers to a point that is output as an abnormality is found by the inspection of the inspection apparatus. The foreign matter and the appearance abnormality are combined and hereinafter referred to as “defect”.
[0006]
Estimating the generation process from the defect appearance information is based on the advanced judgment of the analyst, and poses a problem of individual differences and time required for the judgment. Therefore, Patent Document 1 describes a method of automatically classifying defects according to a specific rule.
In addition, specifying a substance name from the spectrum has a problem in that, similarly to the determination based on appearance information, a large part of the determination by the analyst is large, it takes time, and the determination result may differ depending on the analyst. Was. Therefore, Patent Literature 2 and Patent Literature 3 describe a method of automatically specifying a substance name from spectrum data. Patent Literature 4 describes a method of identifying a processing apparatus that is a problem from a correlation between a database on defects generated in the processing apparatus and data on defects obtained by an inspection apparatus.
[0007]
[Patent Document 1]
JP-A-7-201946
[Patent Document 2]
JP-A-63-108253
[Patent Document 3]
JP-A-8-124982
[Patent Document 4]
JP-A-2000-22203
[Non-patent document 1]
JSME 6th Intelligent Mechatronics Workshop-Mechatronics Technology for Human Support-Lecture Paper “Examination of Defect Distribution Pattern Identification Method” pp. 279-284 (August 2001)
[Non-patent document 2]
Kaoru Nakano, "Associantron" Shokodo (1979)
[0008]
[Problems to be solved by the invention]
In order to specify the processing apparatus in question with high accuracy and in a short time, if each processing apparatus has a plurality of processing units (hereinafter referred to as chambers) that perform the same processing, Efficient analysis by using multiple pieces of information such as foreign matter / defect generation pattern, appearance, element information, construction history, and processing log for each chamber, and by limiting the target time for equipment abnormality analysis Need to be done.
[0009]
The conventional methods of automatically classifying defects from appearance information and the method of automatically specifying a substance name from spectrum data do not describe the use of a plurality of pieces of information. Further, in the method of specifying a processing device, there is no description about limiting a device to be searched and a timing to improve efficiency.
[0010]
SUMMARY OF THE INVENTION An object of the present invention is to solve the above-mentioned problems and to provide a method and an apparatus for providing information that enables early identification of an apparatus in question from product inspection information in which an abnormality is detected.
[0011]
[Means for Solving the Problems]
In order to achieve the above object, the present invention provides a method for analyzing defect information obtained by inspecting a workpiece in a thin film device manufacturing process, the method comprising the steps of: Comparing the data obtained by inspecting the substrate to be processed before performing the processing in the processing step with the data obtained by inspecting the substrate to be processed after performing the processing in the predetermined processing step; A defect generated on the substrate to be processed by performing the processing in the processing step is extracted, and when the number of the extracted defects is larger than a preset number, a detailed analysis of the defect is performed. And information indicating the process history of the substrate to be processed in a plurality of processing apparatuses constituting the predetermined processing step and information on the status of the processing apparatus that has processed the substrate in the predetermined processing step are displayed on a screen. It was way.
[0012]
In addition, a computer in which a problem has occurred is extracted by evaluating the relevance of the detailed analysis information, the information indicating the construction history, and the information on the status of the processing device by a computer.
[0013]
BEST MODE FOR CARRYING OUT THE INVENTION
Hereinafter, embodiments of the present invention will be described using a semiconductor as an example with reference to the drawings.
[0014]
An outline of the present invention is shown in FIG. As described above, in the manufacturing process, the inspection is performed for each of a series of steps. FIG. 1 shows the steps between the inspection 1 and the inspection 2 cut out.
[0015]
Inspection 1 and Inspection 2 can obtain the position information of each detected defect. However, if the defect position detected in Inspection 1 is subtracted from the defect position of Inspection 1 from the defect position of Inspection 2, The position information of a net defect generated in the processing steps A, B, and C shown in FIG. The present invention is directed to such net defects.
[0016]
In the inspection 2, information on a defect obtained in connection with the inspection for the product wafer is acquired. Examples of the information include defect distribution, appearance, element information, and the like. Defect distribution information can be obtained from defect position information output from the inspection apparatus. The external appearance image is obtained by observing and acquiring a defect image at a position corresponding to defect position information of the inspection device using a review device such as an optical microscope or an electron microscope. The contained element information can be obtained by performing element analysis such as EPMA on a defect recognized at a position corresponding to the defect position information. Such defect information obtained in connection with inspection on a product wafer is hereinafter referred to as “product QC (Quality Control) information”.
[0017]
On the other hand, information on which apparatus has been started on the product wafer for each process is acquired. Here, the number of processing apparatuses in each step is not limited to one, and a plurality of processing apparatuses may be present. Further, each apparatus may include a plurality of chambers. Specify up to the unit chamber. This is hereinafter referred to as “manufacturing route information”. FIG. 1 shows an example in which the work is started at the third unit of the process A, the first unit of the process B, and the second unit of the process C.
[0018]
Further, information on a defect (foreign matter) generated in the processing apparatus is acquired for each apparatus and each chamber. This means that, for example, a dummy wafer that has not been subjected to processing such as pattern creation is processed and then taken out in a device or a chamber for which information is to be obtained, and a wafer that has been started only in the device for which information is to be obtained is created. It is obtained by performing the same information acquisition as when acquiring the product QC information. The information is obtained independently of the product wafer information, and is obtained and stored according to a predetermined rule. This is hereinafter referred to as “device QC information”.
[0019]
By evaluating the relevance of the product QC information to, for example, the manufacturing path information or the device QC information, a candidate of a device or a chamber having a problem is extracted.
[0020]
One of the relevance evaluation methods is a defect distribution cycle analysis of product QC information. For example, if the defect distribution of each wafer in a lot has a characteristic every other wafer, it can be determined that the relevance is high for an apparatus that starts the same chamber every other wafer, that is, an apparatus having two chambers. Further, as another example of the evaluation method, there is a comparison between the device QC information and the product QC information with respect to the distribution of defects, the appearance of the defects, or the contained elements. It can be determined that there is a high possibility that a problem has occurred in a device having similarity.
[0021]
As information to be compared with the product QC information, information on the timing and content of maintenance such as device replacement and cleaning, and processing log information when a product wafer of interest is started can be combined.
[0022]
FIG. 2 is a diagram showing a first embodiment of the device configuration of the present invention. 1 is a manufacturing apparatus such as a film forming apparatus, an exposure apparatus, an etching apparatus, etc., 2 is an inspection apparatus such as a foreign substance inspection apparatus or a visual inspection apparatus, and 3 is an image near a corresponding coordinate based on defect detection information from the inspection apparatus. Detailed analysis device for acquiring detailed information such as an optical review device, an image acquisition device such as an SEM (Scanning Electron Microscope: electron microscope), and an analyzer for analyzing elemental information such as EPMA and AES. A process management database that holds manufacturing route information such as which processing machine and which chamber has been processed, 5 is a device status database that stores manufacturing logs and maintenance information of each manufacturing device and device QC information, and 6 is product wafer inspection. Inspection / analysis database that stores product QC information as results and detailed analysis results, 7 A data analyzer for analyzing and evaluating whether or not the inspection information from the inspection device is in a normal range or which device is likely to be a problem; and 8, a display device for displaying analysis results and the like , 9 are networks. The devices 1 to 7 can transmit and receive data via the network 9.
[0023]
In the process management database 4, when a product is processed by a certain processing apparatus, the lot number and the wafer number are used to determine which processing apparatus has been processed, and if there are a plurality of chambers, which chamber has been processed. Alternatively, data for specifying the start time and the like is stored.
[0024]
In the device status database 5, a log of processing conditions acquired for each processing device, data relating to a defect generated for each device, and data calculated by processing the data are accumulated in a time series. These are added or updated each time new data is acquired.
[0025]
The product wafer is inspected by the inspection device 2, and a detailed analysis device 3 acquires an image of the defect and element information thereof according to the inspection result. The obtained data is sent to the inspection / analysis database 6.
[0026]
The data analysis device 7 communicates with various databases connected to the network as shown in 4 to 6 to determine whether the information is in a normal range, or may have a problem. Analyze and evaluate the equipment. The determination result and the analysis result are displayed on the display device 8.
[0027]
The data contents of each database will be described.
[0028]
The contents of the process management database 4 will be described below.
[0029]
The process management database 4 records manufacturing route information. The manufacturing route information will be described with reference to FIG.
[0030]
As described above, a semiconductor is manufactured through a plurality of steps. FIG. 3 shows an example in which three processes A, B, and C are performed, and then inspection D is performed. The process A has two processing devices, and the process C has three processing devices. In each process, a wafer is started in a processing apparatus and processing is performed. Here, in a process requiring processing time, a plurality of processing apparatuses are used in parallel to improve the throughput of the time required for manufacturing in many cases. In such a case, even if the lots have gone through the same processing steps, the actually started apparatuses are not always the same. For example, in FIG. 3, the history of the start equipment of lot A is indicated by a solid arrow, and the history of the start equipment of lot B is indicated by a dotted arrow. At this time, history information indicating which manufacturing apparatus has processed a certain wafer for each processing step is apparatus path information.
[0031]
If the manufacturing apparatus has a plurality of processing units such as chambers, the apparatus path information includes the chamber information. In this case, as in the case of the wafer A and the wafer B shown in FIG. 4, the chambers started by the wafers may be different even in the same lot, so that the apparatus route information is different for each wafer.
[0032]
First, as the production route information, first, a product code such as a lot code and a wafer code which can specify a minimum unit in which the product is started in the apparatus is recorded. These may be stored in the same column for each minimum unit, or may be stored hierarchically, as long as the construction history for each minimum unit can be specified. A name or code that can identify the machine that has been processed for each unit is recorded together with the product code, linked to the product code. At this time, when there are a plurality of devices that perform the same processing, a name or a code is determined so that the device can be identified.
[0033]
Processing equipment here refers to equipment that may cause a change in the state of the wafer before and after passing through the equipment, including cleaning equipment, wafer sorters, wafer inspection equipment, wafer transfer equipment, etc. You may.
[0034]
When the minimum unit of a product has a processing area that can be processed in parallel in the same apparatus, such as a processing apparatus having a plurality of chambers, a name or code is recorded so that the processing area can be specified. At the same time, the database records the time at which the processing apparatus is carried in and / or the time at which the processing apparatus is carried out, or both.
[0035]
The contents of the device status database 5 will be described below.
[0036]
The device status database 5 stores device QC information for each processing device and information on a processing status and a maintenance status.
[0037]
The device QC information includes, for example, the number of defects, the coordinates of the defects, the distribution of the defects on the wafer, the appearance image of the defects, the element information of the defects, the attribute information for each defect given according to the appearance or distribution of the defects, And numerical values calculated from the data such as the ratio of the number of defects for each attribute.
[0038]
The number of defects, the coordinates of the defects, and the distribution of defects on the wafer are obtained as the output of the inspection device. However, these include the possibility of false information (output as a singular point but no defect found in the review).
[0039]
The appearance image of a defect refers to an image obtained by acquiring an area near a position corresponding to the defect coordinates with an image acquisition device such as an optical microscope or an SEM.
[0040]
The element information of a defect refers to element information data obtained by an element information analyzer such as EPMA or AES for a defect confirmed by an optical review device or an SEM. The recording form may be spectral data of elemental information, a waveform image, a representative element name, or any data representing the characteristics of the elemental information of a defect.
[0041]
The attribute information given according to the appearance refers to, for example, a classification code automatically given by the method disclosed in Patent Document 1 or a classification code given manually by an operator. By providing "false alarms" in the classification code and ignoring the defects with the classification codes, eliminating the influence of the "false alarms" where no defects exist from the number of defects, the coordinates of the defects, and the distribution of defects on the wafer. Can be.
[0042]
The attribute information given according to the distribution status is calculated by a method of identifying a point group distribution shape disclosed in Non-Patent Document 1, a certain point group is densely distributed, or The distribution density indicates that the distribution is coarse. In the case of dense distribution, information on what pattern the shape of the dense part is similar to may be included. The pattern indicates, for example, a concentric distribution, a linear distribution, a distribution on the orientation flat or the notch side, a shape, a distribution position, or both. Further, an occurrence ratio for each classification may be recorded.
[0043]
The processing status information refers to processing log information in which logs of various processing conditions during processing are recorded, deviation information from set values of the processing conditions, or both. For example, in the case of an etching apparatus, the processing condition log is time-series information such as the temperature, pressure, gas flow rate, and applied high-frequency power in the chamber. Further, as the deviation amount information from the set value, a statistical value such as a maximum value, an average value, a median value, or the like of the deviation from the set value in the series of processing may be recorded as a representative value.
[0044]
The information on the maintenance status is information on the time of replacement of parts of the processing apparatus, the time of maintenance such as disassembly and cleaning, and the like.
[0045]
In order to obtain data for each processing apparatus, inspection is performed by the inspection apparatus 2 before being carried into the processing apparatus, and inspection is performed again by the inspection apparatus 2 after processing by the manufacturing apparatus 1, and the detected specific Point coordinates are compared, and only data that is determined to have newly occurred may be recorded.
These are obtained continuously according to a certain set rule, and are sequentially added or updated.
[0046]
The contents of the inspection / analysis database 6 will be described below.
[0047]
Inspection / analysis database 6 records inspection / analysis data on product wafers. As the data items, the number of singularities detected by the inspection device, the coordinates of the singularities, the distribution of the singularities on the wafer, the image of the defect, the element information of the defect, the appearance of the defect, There are attribute information for each defect given according to the distribution status, and numerical values calculated from the data such as the ratio of the number of defects for each attribute.
[0048]
Next, a procedure for acquiring data stored in the device status database 5 will be described.
[0049]
FIG. 5 is a flowchart showing the acquisition procedure. A case where data for a certain process A is acquired will be described. First, in Step 101, a defect inspection 1 is performed before starting the manufacturing apparatus for performing the processing A on the wafer. Subsequently, processing A is performed in Step 102. Next, inspection 2 is performed in Step 103. Here, it is desirable that the inspection 1 in Step 101 and the inspection 2 in Step 103 are set so that the defect detection sensitivity is the same.
[0050]
Next, in Step 104, a defect which is not output in the inspection 1 of the Step 101 and is newly output in the inspection 2 of the Step 103 is extracted as a difference detection defect. This is because, for example, for each of the defect coordinates output in the inspection 2 of Step 103, the distance from the defect coordinate output in the inspection 1 of Step 101 is calculated, and if the minimum value of the calculated distance is equal to or less than a certain threshold value This can be realized by ignoring the defects that are output in common and extracting only those defects whose calculated distance is equal to or greater than the threshold value. The method for extracting the difference defect is not limited to the above method, and any method may be used as long as information on a newly generated defect can be extracted by performing processing in the processing apparatus A. In Step 105, the ID number of the defect extracted in Step 104 is transmitted to the database 5. At this time, attribute information given according to the distribution status may be transmitted to the device status database 5 together.
[0051]
Next, it is determined whether the number of defects extracted in Step 106 is equal to or smaller than a preset threshold. If the number of defect points is equal to or greater than the threshold value, sampling of coordinate points is performed in step 107 according to a predetermined sampling rule. The sampling rule may be, for example, to randomly extract only a predetermined number, based on the attribute information given according to the distribution situation, defects that are densely distributed, defects that are coarsely distributed, A fixed number may be extracted from each. Further, the number of sampling points may be determined in proportion to the number of detection points. In this Step 107, the number of coordinate points after sampling should just be smaller than a predetermined threshold value, and the sampling rule is not limited to the above.
[0052]
Next, in Step 108, a review, which is one of the detailed analysis, is performed and recorded in the device status database 5. This review can be realized by an inspection device having an optical review function such as I-890 manufactured by Hitachi, Ltd., or a review SEM such as RS-3000 manufactured by Hitachi, Ltd. The review is not limited to the above as long as an image near the coordinate point can be acquired based on the coordinates output from the inspection device.
[0053]
Next, in Step 109, the obtained images are classified. The classification result may be recorded in the apparatus status database 5. The classification may be performed manually by an operator, or may be performed automatically using a method according to Patent Document 1.
[0054]
Next, it is determined whether the number of defects reviewed in Step 110 is equal to or less than a preset threshold value. If the number of review points is equal to or greater than the threshold value, sampling of coordinate points is performed according to a predetermined rule in Step 111. The sampling method may be the same as that in Step 107, or a different method may be used. Next, in Step 112, data relating to element information is acquired using the detailed analysis device 3 capable of acquiring element information such as EPMA and AES for the defect confirmed by the review, and transmitted to the apparatus status database 5.
[0055]
Here, the determination of the necessity of target sampling in the next step of Steps 106 and 107 is not limited to the method described in the embodiment, as long as a rule that does not permit increase without an upper limit according to the number of defects is set.
[0056]
The data of the apparatus status database 5 is desirably held for each single processing apparatus, but may be recorded and managed as data of a processing block including a plurality of processing apparatuses. This is because, if a processing block that causes a problem can be specified, narrowing down of problem device candidates becomes easy, which can contribute to problem device specification.
[0057]
The information accumulated in the apparatus status database 5 may be collected using a dummy wafer, or may be collected at a specific frequency by applying the procedure shown in FIG. 5 to a product wafer. The frequency at which data is stored in the apparatus status database 5 can be arbitrarily set by the user, but the reference may be based on time or the number of wafers started in the apparatus or the chamber.
[0058]
Next, a procedure for applying the first embodiment to a product wafer will be described.
[0059]
FIG. 6 is a flowchart showing the application procedure. As described above, the inspection is usually performed every certain steps in the manufacturing process. In Step 201, a defect inspection 1 after a processing step (not shown) performed before Step 201 is performed. In Step 202, a series of processing is performed. Although the figure shows an example via three processing devices, the number of steps is not limited to three. Inspection 2 is performed in Step 203. It is desirable that the inspection 1 in Step 201 and the inspection 2 in Step 203 are set so that the defect detection sensitivities are the same.
[0060]
Next, in Step 204, a defect which is not output in the inspection 1 of the Step 201 and is newly output in the inspection 2 of the Step 203 is extracted as a difference detection defect. This is because, for example, for each of the defect coordinates output in the inspection 2 of Step 203, the distance from the defect coordinate output in the inspection 1 of Step 201 is calculated, and if the minimum value of the calculated distance is equal to or less than a certain threshold value This can be realized by ignoring the defects that are output in common and extracting only those defects whose calculated distance is equal to or greater than the threshold value. The method for extracting the difference defect is not limited to the above method, and any method may be used as long as information on a newly generated defect can be extracted by performing processing in the processing apparatus A.
[0061]
In Step 205, the ID number of the extracted defect, the number of defects, a defect map, or attribute information given according to the distribution status is transmitted to the inspection / analysis database 6. In Step 206, the number of extracted defects is compared with a predetermined threshold value. If the number of defects is smaller than a predetermined threshold value, it is determined that no abnormality has occurred, and the product wafer is sent to the next step in Step 207. If the number of defects is larger than a predetermined threshold value, it is determined that an abnormality has occurred, and the process proceeds to next Step 208. Although it is desirable to use the number of difference detection defects as the number of defects used for the determination, the number of defects detected by the inspection 2 may be simply used.
[0062]
Next, in Step 208, it is determined whether or not the review target is sampled according to a predetermined rule. For example, it is determined whether or not the number of extracted defects is equal to or less than a preset threshold. If the number of defect points is equal to or larger than the threshold value, sampling of coordinate points is performed in step 209 according to a predetermined rule. The rule for judging the necessity of sampling is not limited to the above, as long as it does not permit the review target point to increase without an upper limit according to the number of defects. Sampling can be realized, for example, by randomly extracting a predetermined number. In Step 209, the number of coordinate points after sampling may be smaller than a predetermined threshold value, and the sampling rule is not limited to the above. Further, information indicating which of the sampled coordinate points may be transmitted to the database so that it can be referred to separately.
[0063]
Next, in Step 210, a review, which is one of the detailed analysis, is performed. This review can be realized by a device having an optical review function such as Hitachi I-890 or a review SEM such as Hitachi RS-3000. The review is not limited to the above as long as an image near the coordinate point can be acquired based on the coordinates output from the inspection device. Next, in Step 211, the images obtained in Step 210 are classified and recorded. The classification may be performed manually by an operator, or may be performed automatically using a method according to Patent Document 1.
[0064]
Next, in Step 212, it is determined whether or not the analysis target is sampled according to a predetermined rule. For example, it is determined whether the number of reviewed defects is equal to or less than a preset threshold value. If the number of defect points is equal to or greater than the threshold value, sampling of coordinate points is performed in step 213 according to a predetermined rule. The rule of the sampling necessity determination is not limited to the above, as long as it does not permit the analysis target point to increase without an upper limit according to the number of defects. The sampling method may be the same as in Step 209, or a different method may be used. Further, information indicating which of the sampled coordinate points may be transmitted to the inspection / analysis database 6 so that it can be referred to separately.
[0065]
Next, in Step 214, data relating to element information is acquired using a device capable of acquiring element information such as EPMA and AES for the defect confirmed by the review, and transmitted to the database 6. In Step 215, the manufacturing path information on the wafer determined to be abnormal is acquired from the process management database 4. In Step 216, information on the corresponding device and chamber is obtained from the device status database 5.
[0066]
In step 217, the data analyzing device 7 displays information on the device that processed the wafer having the abnormality and the chamber on the display device 8. In addition, information on the processing apparatus that has processed the wafer, which is stored in the apparatus status database 5 and may include the time when the wafer was processed. Further, the inspection / analysis result of the wafer may be displayed together. The display is performed by an inquiry from the user, but a warning may be issued from the data analysis device 7 to prompt the user to make an inquiry about the abnormal data when the abnormality is detected.
[0067]
The inspection frequency of the product wafer may be all or may be sampled according to a certain rule. Also, even when Steps 208 to 214 are not performed, Steps 215 and subsequent steps may be performed. Further, the abnormality determination in Step 206 is not limited to the number of extraction points, and may be determined based on the density of the extraction points normalized by the area of the inspection area. Further, using attribute information given according to the distribution situation, the density of only a dense area portion, the density of only a coarse area, or the like may be used as a criterion value.
[0068]
The data analysis / display method in the data analysis device 7 will be described below.
[0069]
First, FIGS. 7 and 8 show an embodiment of a data analysis method using device route information. For example, when an abnormality is detected in a certain wafer of the lot A in the inspection D of FIG. 7, all of the seven apparatuses existing in the processes A to C are considered as candidates in which the abnormality has occurred. Here, the apparatus route information is acquired from the process management database 4, and as shown in FIG. 7, by presenting the apparatus in which the lot A has been started to the user, the user can select four apparatus candidates in which an abnormality has occurred to four apparatuses. The search can be narrowed down, and the time required for the detailed investigation can be reduced.
[0070]
Also, as shown in FIG. 8, by presenting information on a wafer basis or on a chamber basis, instead of on a lot basis or on a device basis, it is possible to limit the candidates of the apparatus in which an abnormality has occurred on a chamber basis.
[0071]
Further, by using a plurality of wafers in which an abnormality is detected and displaying the path information of the wafers together, it is possible to assist in narrowing down the device candidates in which an abnormality has occurred.
[0072]
FIG. 9 is a flowchart showing an example of a method for narrowing down abnormal device candidates using device route information. First, in Step 301, a plurality of wafers in which an abnormality is detected for use in path superposition are specified. In Step 302, a certain process is set as a first calculation target process in the range of the process for calculating the wafer set as the calculation target. In Step 303, among the wafers specified in Step 301, a certain wafer is set as a first wafer used for calculation. In step 304, the set wafer and the processing apparatus / processing chamber in the process are obtained from the process management database 4, and points are added to the processing apparatus / processing chamber. In Step 305, it is determined whether or not the calculations for all target wafers have been completed in the set process. If the processing has not been completed, the wafer to be calculated is set as the next wafer in Step 306, and Step 304 and Step 305 are repeatedly executed.
[0073]
If the calculation of all the wafers has been completed in Step 305, it is determined in Step 307 whether the calculation of all the processes to be considered has been completed. If not completed, the process to be calculated in Step 308 is set as the next process, and Step 303 to Step 307 are repeatedly executed.
[0074]
If all the calculations for the process to be considered have been completed, the points of all the apparatuses and chambers of the process to be calculated are displayed to the user in Step 309.
[0075]
FIG. 10 shows a display example of the calculation result. This example shows three abnormal wafers. When the abnormal wafers pass the paths indicated by the arrows, points shown in the drawing are given to the respective processing apparatuses and processing chambers. Here, for example, if a device having, for example, two or more points is to be extracted as a point to be extracted as a problem candidate, the device 2 is extracted in process A, the device 1 is extracted in process B, and the device 1 is extracted in process C.
[0076]
As described above, it is possible to extract a device / chamber with a high point as a detailed investigation target as having a high possibility that an abnormality has occurred.
[0077]
Further, by using together the path information of the wafers for which no abnormality has been detected, it is possible to narrow down the apparatuses to be investigated in detail. The execution flow is shown in FIG.
[0078]
First, in Step 401, a plurality of wafers to be used for path overlapping are specified. In Step 402, a certain process is set as a first calculation target process in the range of the process for calculating the wafer set as the calculation target. In Step 403, among the wafers specified in Step 401, the first wafer started in the calculation step in the calculation step is set as the first wafer used for calculation. In Step 404, it is determined whether or not the calculation target wafer is a wafer in which an abnormality is detected. If an abnormal wafer has been detected, in step 405, the set wafer and the processing apparatus / processing chamber in the process are acquired from the process management database, and points are added to the processing apparatus / processing chamber.
[0079]
If it is determined in Step 404 that the wafer to be calculated is not abnormal, in Step 406, the wafer and the processing apparatus / processing chamber in the process are acquired from the process management database. It is determined whether the number of points given to the processing apparatus / processing chamber is 1 or more. If it is 1 or more, in Step 407, the point of the processing apparatus / chamber is subtracted. If the value is less than 1, that is, if the point is 0, the process proceeds to Step 408 without performing the subtraction process of Step 407.
[0080]
In Step 408, it is determined whether or not the calculations for all target wafers have been completed in the set process. If not completed, in Step 409, the wafer to be calculated is set as the wafer that has been started in the next step, and Steps 404 to 408 are repeatedly executed. If the calculation of all the wafers has been completed in Step 408, it is determined in Step 410 whether the calculation of all the processes to be considered has been completed. If not completed, the process to be calculated is set as the next process in Step 411, and Steps 403 to 410 are repeatedly executed. If all the calculations for the process to be considered have been completed, the points of all the apparatuses and chambers of the process to be calculated are displayed to the user in Step 412.
[0081]
Here, in Step 406, the point is subtracted only when the number of points of the started apparatus / chamber is 1 or more for the following reason. When the state of the apparatus is normal, the point of the apparatus / chamber takes a negative value unless the processing of Step 406 is performed. Here, when the state of the apparatus / chamber changes to an abnormal state in the middle, the points of the apparatus / chamber become 0 or less until an abnormal point exceeding the minus point is added.
[0082]
Since there is a possibility that the number of wafers to be started is different for each apparatus / chamber, it is difficult to determine whether or not a point indicating an abnormality is included in the apparatus / chamber based on the magnitude of the minus point. Therefore, a negative value is not allowed for a point for each device / chamber, and a point is always set to a positive value when an abnormality may have occurred.
[0083]
In addition, in Steps 403 and 409, the point addition / subtraction in a certain process is performed in the order of the started wafers for the following reason. For example, suppose that in a certain process, two wafers are started in a state where the apparatus is normal, and then two wafers are started in an abnormal state. Here, when the points are calculated according to the rule shown in FIG. 11 in the construction start order, the number of points of the device is 2, and a point indicating an abnormality of the device is indicated. However, when the calculation of the wafers started in a normal state is performed after the calculation of two wafers started in an abnormal state is performed without considering the start order, for example, when the calculation is performed on the wafers started in a normal state, the point of the installation value becomes 0, You cannot get points reflecting the occurrence of abnormalities. As described above, in order to reflect the time-dependent change in the occurrence of an abnormality in the apparatus, the calculation of the points is performed in the order of the started wafers.
[0084]
FIG. 12 shows a display example of the calculation result. This shows an example in which three abnormal wafers are processed in addition to the three abnormal wafers. The steps, the configuration of the apparatus, and the paths of the three abnormal wafers are the same as those shown in FIG. In each step, the order in which the processes are started is the order of the abnormal wafers 1, 2, 3 and the normal wafers 1, 2, 3. Here, when three normal wafers pass through the path indicated by the arrow in the figure, points are finally given as shown in FIG. Here, for example, when the number of points to be extracted as a problem candidate is two or more, the apparatus 2 is extracted in the process A and the apparatus 1 is extracted in the process C. In this way, it can be used as an index for narrowing down the problem device candidates.
[0085]
The wafers to be superimposed on the paths may be narrowed down according to a preset condition. For example, the density of detected defects may be limited to a value equal to or higher than a predetermined threshold. In addition, the method may be limited to those determined to have a densely distributed region by the method disclosed in Non-Patent Document 1. Further, in addition to the above limitation, the shape of the limited region may be limited to a shape similar to each other. When attribute information is added to a defect, a condition of a wafer to be calculated may be narrowed down using the attribute information. For example, with respect to the number of defects detected by the inspection device, the attribute of the defect is calculated by excluding the defect added with the attribute “false alarm (output as a defect but no defect was found in the review)”, When the value is equal to or more than a predetermined threshold value, the path may be superimposed as an abnormal wafer, and when the value is less than the threshold value, as an abnormal wafer. Further, the wafers used in the superposition processing do not need to be in different lots, and may be applied to wafers in the same lot.
[0086]
Next, an embodiment of a period analysis / display method using defect distribution information included in product QC information will be described below.
[0087]
When an abnormality is detected in a certain wafer, an additional inspection is performed on another uninspected wafer included in a lot including the wafer to acquire inspection data. Then, a graph such as that shown in FIG. 13 is displayed with the wafer number and the number of defects detected on the wafer as axes. The user can read from this graph that a large number of defects are detected every three wafers. Further, the user compares the graph with the starting device obtained from the device route information, and determines that a device having a specific processing area for every three wafers, that is, a device having three chambers is highly likely to be a problem generating device. can do.
[0088]
Further, as shown in FIG. 14, for example, the graph may be displayed with the chamber number of the apparatus started and the number of wafer defects processed in the chamber as axes. By displaying in this way, for example, in a device having two chambers, there is no difference in the distribution of the number of defects for each chamber, but in a device having three chambers, the number of defects is large for a specific chamber. Can be easily determined, and it can be determined that an apparatus having three chambers is highly likely to be a problem occurrence apparatus.
[0089]
Further, as shown in FIG. 15, the information displayed for each wafer may be a map of the wafer. The display allows the user to determine the regularity of the problem occurrence for each wafer in the lot.
[0090]
Further, as shown in FIG. 16, a map may be displayed for each chamber. From the display, the user can grasp the tendency for each chamber.
[0091]
Further, the numerical value of the axis may be the defect density instead of the defect number. At this time, the density of only a dense area portion is calculated using a distribution density calculated by a method of Non-Patent Document 1 or the like, such that a certain point group is densely distributed or coarsely distributed. Alternatively, the density or the like of only a rough region may be used as a value used for analysis and display. Further, a portion determined to be densely distributed in the wafer map display of FIGS. 15 and 16 may be highlighted. The emphasis may be made by changing the color of the plot point or the color of the area including the plot point, as long as the user can distinguish it from other plot points. Alternatively, a wafer determined to have a dense area may be highlighted. This makes it easier for the user to determine the regularity of the distribution pattern fluctuation.
[0092]
In FIG. 16, the distribution of each chamber may be displayed by superimposing the distribution of a plurality of wafers. By doing so, when a dense portion exists for each chamber, the dense portion can be emphasized rather than displayed for each wafer, and the pattern of the map can be easily determined. In this superimposed map, a dense portion may be highlighted in the same manner as described above, or a superposed wafer having a dense portion may be highlighted.
[0093]
Here, an example has been described in which inspection is performed on all uninspected wafers and the inspection data is analyzed, but the wafers to be inspected may be sampled according to a certain rule. As a sampling rule, for example, the wafer to be inspected may be the least common multiple of the number of chambers of the target apparatus. In this case, at least one or more inspections are performed for each chamber of the target apparatus. According to the rule, for example, when a target apparatus includes a two-chamber apparatus and a three-chamber apparatus, if six consecutive wafers are inspected, two chambers out of the six wafers are inspected. Data can be obtained for three wafers started in each chamber of the apparatus and two wafers started in each chamber of the three-chamber apparatus. By comparing the data with the number and density of defects for each chamber as shown in FIG. 14, it can be determined whether or not there is a difference for each chamber.
[0094]
Further, as another sampling rule, the number of sheets may be twice or more the number of apparatuses having the maximum number of chambers. Also in this case, at least two or more wafers are inspected for each chamber of each apparatus. Since the number of samples for each chamber increases as the multiple increases, the accuracy of determining whether or not there is a difference for each chamber is improved. In addition, a sampling rule may be arbitrarily determined so that at least one or more wafers in all chambers are contained.
[0095]
The sampling rule of the inspection wafer may be determined in advance by determining the total number of the inspection target wafers from constraints such as throughput, and may be changed according to the configuration of the number of apparatuses in the range of the processing process to be analyzed and the number of chambers, Further, the number of inspections and the sampling wafer sampling rule may be changed according to the configuration of the number of apparatuses, the number of chambers, and the like in the range of the processing step to be analyzed.
[0096]
Of course, if the inspection of the wafer necessary for the distribution analysis described above is performed as a normal inspection, no additional inspection is necessary when an abnormality is found in the wafer.
[0097]
One embodiment of the output screen is shown in FIG. The screen 10 is called by an inquiry from the user. The present embodiment is characterized in that information about a product wafer and information about a processing device related to the product wafer are displayed together. The display is composed of an area 11 for displaying information on a product wafer and an area 12 for displaying information on a device related to the wafer stored in the device status database 5. The screen 11 further displays an area 13 indicating the unique identification information of the wafer, an area 14 indicating the information obtained by inspection / analysis of the wafer, and a history of the starting apparatus obtained from the manufacturing route information of the process management database 4. Area 15 shown in FIG.
[0098]
An example of the display contents of each area will be described in detail below.
[0099]
The area 13 includes information for uniquely specifying which wafer or inspection process the information shown in the areas 14 and 15 is obtained from, such as a product type, a lot number, a wafer number, and a range of a processing step corresponding to Step 202 in FIG. Is displayed. Here, a function that can display a list of inspection information of wafers inspected in a corresponding lot, instead of information of one wafer, may be provided. For example, the wafer No. A “list” button may be provided beside the display area, and by pressing this button, the screen may be shifted to a screen displaying a list of inspection information of inspected wafers.
[0100]
The area 14 displays a map of the defects detected by the inspection device in the wafer surface and the number of detected defects. Further, when an image at the defect coordinates is obtained, the image information may be displayed together. When element information is obtained for a defect found at the defect coordinates, the element information may be displayed together. Here, when attribute information given according to the appearance is added to the defect, the map may be displayed by changing the color or shape for each attribute so that points on the map can be distinguished. Further, together with the image information and the element information of the defect, the attribute information of the defect given according to the distribution state and the appearance may be displayed together. Further, the map may be divided into a region having a high distribution density and other regions, and the region having a high distribution density may be highlighted so as to be distinguishable from other portions. Further, the ratio of each classification may be displayed. Further, the defect detection density may be displayed instead of the number of defects.
[0101]
The area 15 displays a processing step name corresponding to Step 202 in FIG. 6 and a history of a processing apparatus on which the wafer is started, which is obtained from the manufacturing path information. The example of FIG. 17 shows that there are three processing apparatuses in the sputtering process, and each apparatus has three chambers. Similarly, it shows that there are two processing apparatuses in the coating step, four processing apparatuses in the exposure step, two processing apparatuses in the etching step and two chambers, and two apparatuses in the cleaning step. This indicates that the wafer was processed by the chamber 1 of the sputtering process No. 3, the coating process No. 1, the exposure process No. 2, the etching process No. 1 chamber 1, and the cleaning process No. 1.
[0102]
In the area 12, data on the devices stored in the device status database 5 of the processing devices displayed in the area 15 are displayed in chronological order.
[0103]
The example of FIG. 17 is an example in which the defect density is displayed. In the graph, the horizontal axis indicates the date, and the vertical axis indicates the number of defects per unit area. The vertical line 16 in the graph indicates the date when the wafer was started. In this case, the defect density of the second exposure process increased before and after the start of the construction, while no change was observed in other apparatuses. From this information, the user can judge that there is a high possibility that a problem has occurred in the second exposure process in terms of the defect density. Alternatively, the device name display portion may be a pull-down menu so that data of another unit or chamber can be called up, so that data can be easily compared.
[0104]
The display information in the area 12 can be switched by selecting a tab.
[0105]
FIG. 18 shows an example of a screen displaying a list of inspection information of wafers inspected in a corresponding lot, instead of information of one wafer. The information is displayed in the area 14. The screen can be shifted to this screen by, for example, pressing a “list” button in the area 13. The area 14 shows an example in which the horizontal axis represents the wafer number and the vertical axis represents the defect density. In addition, the number of the started chamber may be displayed by clicking the mark of the device displayed in the area 15. Alternatively, the display items on the horizontal axis may be changed to display for each chamber and displayed in the form shown in FIG. Also, the vertical axis display items may be changed and displayed in the form described with reference to FIG. 15 or FIG.
[0106]
Display examples of other data in the area 12 are shown in FIGS.
[0107]
FIG. 19 is an example showing map data. If there is a characteristic deviation in the distribution of defects detected in the product wafer, the defect occurrence maps in the processing equipment are displayed in chronological order, and the deviation in the defect distribution similar to that of the product wafer starts the product wafer construction. It is easy for the user to determine whether or not it is occurring near the time. That is, it is possible to narrow down the devices having a problem. For example, in FIG. 19, in the exposure apparatus 2, it is possible to observe the same bias of the defect distribution as in the product wafer near the start of the construction. As a result, it is possible to determine that there is a high possibility that a problem has occurred in the exposure apparatus 2 in the same manner as the result determined based on the foreign matter / defect density information.
[0108]
FIG. 20 is an example showing, as image information, a defect generated for each processing apparatus or each chamber. The element information analysis information may be displayed together with the image information. Further, the detected element may be displayed. Further, a substance name may be identified and the substance name may be displayed. The user can compare the defect appearance image or the element information obtained from the product wafer displayed in the area 14 with the defect appearance image or the element information of each device displayed in the area 12 and associate them. It can be used as a judgment material for extracting a device having potential. For example, comparing the appearance image of the product wafer and the appearance image of the apparatus information in FIG. 20, similar defects are observed in the exposure apparatus 2 in both the defect size and the defect shape. From this, the user can determine that the exposure apparatus 2 may be causing a wafer abnormality.
[0109]
FIG. 21 is an example in which, as the processing log information, in the processing log information of various processing conditions obtained for each processing apparatus, an average value of a deviation from a set value is acquired as a representative value, and a transition is displayed. When the abnormality of the processing condition causes the occurrence of the abnormality of the wafer, the user evaluates the degree of coincidence between the start time of the abnormal wafer indicated by 16 and the deviation from the set value of the processing condition, thereby making the relevant operation. It can be used as a judgment material for extracting a possible device.
[0110]
For example, in the graph of FIG. 21, the displacement of the exposure apparatus 2 is large near the start date of the abnormal wafer. Thus, the user can determine that the exposure apparatus 2 may be causing a wafer abnormality. Here, for example, when a certain area on the graph is clicked, a log obtained in a time series during the processing for a certain wafer started during the period is displayed instead of the representative value plotted in the area 12. You may do so.
[0111]
FIG. 22 is an example in which maintenance information of the apparatus is shown as other information. In the manufacturing process, abnormalities may occur suddenly, such as a sudden increase in defects due to maintenance of the device. Therefore, there is a case where the cause of occurrence can be estimated by associating the maintenance time with the abnormality occurrence time of the apparatus. From FIG. 22, it can be seen that in the exposure apparatus 2, parts are replaced immediately before the work on the product wafer in which the abnormality is detected. Thus, in the exposure apparatus 2 in which it is determined that there is a high possibility that a problem has occurred due to the other information described above, the user can determine that there is a possibility that some modulation has occurred due to component replacement.
[0112]
Further, the time series change of the classification ratio of the defect generated for each processing apparatus or each chamber and the start time of the abnormal occurrence wafer may be displayed together. When an abnormality occurs in the apparatus and the ratio of the generated defects changes, by performing the display, the user can use the information as a determination material for extracting the apparatus that may be related to the abnormality.
[0113]
Further, regarding the manufacturing path information displayed in the area 15, a plurality of wafers are designated on a wafer selection screen (not shown), and the manufacturing path information and the number of points are displayed as described with reference to FIGS. Good.
[0114]
The graph in the area 12 is an example in which the horizontal axis is adjusted so that the vertical line 16 indicating the start date of each graph is at the same position, but the date display of each graph is set to the same position. It may be displayed.
[0115]
The display mode is not limited to the embodiment, and any configuration may be used as long as the information on the wafer determined to be abnormal and the information on the processing apparatus can be displayed at any time together with the information on the time when the wafer was started on the processing apparatus. Therefore, in order to realize the present embodiment, it is not always necessary to provide both the process management database 4 and the apparatus status database 5, and it is sufficient to provide at least one or more.
[0116]
The data collected in the apparatus status database 5 and the inspection / analysis database 6 does not necessarily need to be performed for all the items described in the embodiment, and may be one or more of the items described in the embodiment. However, the more types of data to be collected, the more versatile the estimation of the device in which the problem has occurred, and thus the more reliable the estimation can be expected. Further, the databases and the analysis devices shown in 4 to 7 in FIG. 2 may be held in the same device, or may be held by being divided in an arbitrary combination.
[0117]
FIG. 23 is a view showing a second embodiment of the device configuration of the present invention. The difference from the first embodiment is that an association degree evaluation engine 17 is provided. The relevance evaluation engine executes a time-series transition of the processing QC information collected in the processing apparatus or the chamber stored in the apparatus status database 5 and the product QC information obtained from the product wafer or the processing. It has a function of evaluating which processing apparatus or chamber is a candidate for a problem apparatus in consideration of time-series transition fluctuations such as defect density during a given period.
[0118]
An application procedure in the second embodiment for a product wafer will be described.
[0119]
Due to the configuration including the relevance evaluation engine 17, the application procedure of the present technique is replaced with the flowchart shown in FIG. 24 after Step 217 of the flowchart shown in FIG.
[0120]
In Step 501 in FIG. 24, a processing apparatus / chamber common to a wafer having a problem is evaluated based on apparatus path information. In addition, with respect to the pattern fluctuation of the distribution of wafers in a lot, the relevance of the pattern to the apparatus is evaluated by periodic analysis with the number of chambers of the start apparatus. In addition, qualitative data such as a defect map, an image of a defect, and elemental information spectral data obtained from an elemental information analyzer, which is one of the detailed analyzers, and attribute information of the defect are obtained from data obtained from the apparatus status database 5. Evaluate the relevance of data obtained from product wafers. In addition, regarding the change in defect density, the change in the occurrence frequency of each defect attribute, or the change from the setting of the processing log, the relationship between the absolute value or the amount of change and the start time of the wafer determined to be abnormal is evaluated. I do. In addition, the relevance between the apparatus maintenance time and the start time of the wafer determined to be abnormal is evaluated.
[0121]
In Step 502, the evaluation values evaluated in Step 501 are totaled for each device and chamber, and the degree of association with the occurrence of abnormality is evaluated for each device and chamber. In Step 503, a device that is highly relevant to the abnormality that has occurred is displayed.
[0122]
The evaluation method in Step 501 will be described.
[0123]
The related device / chamber evaluation based on the manufacturing path information can be realized by, for example, the method described in FIG. 9 or FIG. That is, the designation of the wafer to be calculated in Step 301 in FIG. 9 or Step 401 in FIG. 11 is automatically set according to a certain rule, and the points for each apparatus / chamber shown in FIG. 10 or FIG. Calculate the number automatically. The rule for specifying the wafer may be specified by specifying the time, for example, within the past several days from the present time, by specifying the number of wafers, such as the number of inspection wafers, or by specifying the lot, such as the number of inspection lots. Then, a certain threshold value is set in advance for the number of points for each device, and for example, an evaluation value 1 is given to a device / chamber exceeding the threshold value, and an evaluation value 0 is given to a device less than the threshold value.
[0124]
Next, an embodiment of evaluating the degree of association between the variation in the lot and the number of chambers is described. The data shown in FIG. 12 is calculated for the wafers in the lot. Then, a certain threshold value is set in advance for the value on the vertical axis in FIG. 12, for example, the number of defects, and it is determined whether or not the wafer numbers exceeding the threshold value are in an arithmetic progression. This can be determined based on whether or not the difference values of the wafer numbers exceeding the threshold are the same. If it is an arithmetic progression, it is determined that the variation within the lot has the same periodicity as the tolerance of the arithmetic progression, and an evaluation value 1, for example, is given to an apparatus having the same number of chambers as the tolerance of the arithmetic progression.
[0125]
Further, as another embodiment of the evaluation, for example, the graph of FIG. 12 is differentiated, and a wafer number having a differentiated value equal to or greater than a predetermined threshold value is set as a peak, and the cycle of the peak wafer number is determined in the same manner as in the above method. The presence or absence of sex may be determined.
[0126]
Further, as another example of the evaluation, a general significance test was performed as a statistical method for the variation in the number of defects for each chamber using a graph shown in FIG. 14, for example, and it was determined that there was a significant difference. For example, the evaluation value 1 may be given to the device. As another example using FIG. 14, an average value is calculated for each chamber number, and the absolute value of the difference between the two chambers is calculated for all combinations of the calculated average value. If the calculated value exceeds a predetermined threshold value, it is determined that there is significance for each chamber, and the number or density of defects among the two chambers used for calculating the calculated value is determined. An evaluation value of 1 is given to a chamber in a poor state such as a large value.
[0127]
Here, the value of the vertical axis in the above evaluation is, instead of the number or density of defects, for example, the density of only the defect dense area for each wafer specified using the method disclosed in Non-Patent Document 1, or the value of the defect. The density of only a region having a coarse distribution, the density of a region excluding a defect dense region, or the like may be used. At this time, the calculation of the region may be performed not on a wafer-by-wafer basis but on a map obtained by superimposing a wafer map for each chamber. Further, a value given in relation to the presence or absence of a densely-defected region may be used as the value on the vertical axis. For example, the above-described evaluation may be performed with the wafer 1 determined to have a dense defect area and the wafer determined to have no defective dense area set to 0.
[0128]
As another example of the evaluation, a method using the wafer map shown in FIG. 15 will be described below. First, using the similarity evaluation method of the map described later, the similarity with each wafer based on the wafer number 1 and the similarity with each wafer based on the wafer number 2 are used as the basis for each wafer. Are calculated, and graphs are created with the wafer number and the similarity as axes, as many as the number of wafers. Then, the variation of the similarity with respect to the wafer number of the graph is evaluated for all the graphs created by the method described as the method using FIG. 12, and if there is any graph that is determined to have any periodicity, For example, an evaluation value 1 is given to the corresponding device.
[0129]
As another example of the evaluation, a method using the wafer map shown in FIG. 16 will be described below. In the wafer map for each chamber, the average value of the similarity is calculated for each chamber by using the method described as the method using FIG. 15 for each chamber. When the average value falls below a predetermined threshold value, for example, an evaluation value 1 is given to the chamber. This is to show that a common pattern appears in the defect distribution of the wafer started in the chamber.
[0130]
Next, an example of the similarity evaluation of a defect image will be described. First, in a defect image for each device / chamber acquired as device QC data, the feature of the defect region is calculated as a quantitative value. As features, for example, brightness, area, aspect ratio, circularity, variation in brightness, and the like of the defect region can be used. The area of the defect may be specified by a person, or an image of the same background where no defect exists may be acquired as a reference image together with the defect image, and the defect area may be specified by an inter-image calculation. Any method may be used as long as the defect area is specified.
[0131]
Next, in the appearance image of the defect acquired as the product QC data, the feature amount is calculated in the same manner. Next, the distance between the point specified by the defect image of the wafer where the abnormality is detected and the point specified by the defect image of each device / chamber is calculated in the feature amount space using each feature amount as a coordinate axis. It can be determined that the smaller the distance is, the higher the similarity of the image is. Then, n devices / chambers are extracted in descending order of similarity, an evaluation value n is given to the device / chamber with the highest similarity, and an evaluation value is given such that the smaller the similarity, the smaller the evaluation value.
[0132]
FIG. 25 shows an example in which two feature values are used in the evaluation value assignment examples. In this case, the feature space is a two-dimensional space defined by the feature amounts 1 and 2. Points specified by the defect images for each device / chamber are plotted with black circles. Here, it is assumed that the characteristic amount of the appearance image of the defect of the product QC wafer is specified at a position plotted by a white circle. If, for example, three devices / chambers are extracted in descending order of similarity, the process 2 device 1 chamber 2, the process 1 device 1 chamber 1, and the process 1 device 1 chamber 1 shown in FIG. 25 are extracted. Evaluation values 3, 2, and 1 are given, respectively. In the case where m feature values are used, the same process may be performed in an m-dimensional feature space.
[0133]
Here, the image of the defect in the apparatus and the image obtained at the time of inspection with the actual product may not always be exactly the same. This is because, for example, a defect generated in the process A in FIG. 6 is inspected after the processes B and C. Therefore, as shown in FIG. 26, an appearance image is obtained by defect inspection and review immediately after each processing step, and a defect inspection performed immediately after the processing step in a defect inspection step in which product QC is performed. The defect is reviewed based on the defect coordinate points obtained by the inspection, and an appearance image is obtained.
[0134]
The former appearance image is referred to as “defect image generated in the apparatus”, and the latter appearance image is referred to as “defect image at inspection”. Then, an appearance change database that associates the “defect image generated in the apparatus” with the “defect image at the time of inspection” is created. First, the appearance image acquired by the device QC is evaluated for similarity with the “defect image at the time of inspection” in the database, and an image similar to the defect image is specified. Then, a similar image search is performed from the device status database 5 with the “defect image generated in the device” corresponding to the specified “defect image at the time of inspection” as a search target. In this way, it is possible to perform matching for estimating a generator even for a defect having a different appearance due to a process. It is desirable that this appearance change database is updated periodically.
[0135]
In addition, instead of Step 101 to Step 104 in FIG. 5, an image obtained by performing Step 601 to Step 608 in FIG. 27 may be registered as an image of the device status database 5. That is, inspection 1 of the wafer is performed in Step 601, and processing A is subsequently performed in Step 602. Next, wafer inspection 2 is performed in Step 603, and processing B is subsequently performed in Step 604. Next, the wafer inspection 3 is performed in Step 605, and subsequently, the processing C is performed in Step 606. Next, inspection 4 is performed in Step 607. Then, in Step 608, a difference between the inspection 1 and the inspection 2 is detected, and coordinate points of a defect generated in the processing A are extracted.
[0136]
Similarly, the difference between the inspection 2 and the inspection 3 is detected, and the coordinate point of the defect generated in the processing B is extracted. Similarly, the difference between the inspection 3 and the inspection 4 is detected, and the coordinate point of the defect generated in the processing C is extracted. Subsequent procedures are performed in the same manner as in Step 105 and subsequent steps in FIG.
[0137]
By doing so, the coordinates of the defect that has occurred can be acquired for each processing step, that is, for each processing device / chamber. Further, by acquiring an appearance image in a defect inspection step in which a product QC is performed based on the coordinate points, an appearance image which is a defect generated in each processing apparatus and has been changed through processing is obtained. Can be By using the external appearance image, it is possible to perform matching for estimating a generator even for a defect whose external appearance is different through a process.
[0138]
Alternatively, an associative memory realized by a neural network may be used. Asosiatron disclosed in Non-Patent Document 2 is known as an example of the associative memory model. This is a mutual connection type neural network, and the neuron is a model taking three values of -1, 0 and 1. By learning the image of the defect generated for each device alone, it is possible to search for an image closely related to the image obtained in the inspection process, even if the image does not always completely match.
[0139]
Next, FIG. 28 shows an embodiment of the similarity evaluation of the defect distribution of the wafer map. First, Map 1 and Map 2 to be compared are divided into small areas. Next, according to the density of each small area, binarization is performed using a certain threshold value which is separately determined, and an area having the threshold value or more is extracted as a high-density area. Next, a logical operation of AND and OR of the high-density regions in the map is performed, and an area obtained by the AND operation is obtained as an AND area, and an area obtained by the OR operation is obtained as an OR area. Then, the similarity is defined as: similarity = AND area / OR area.
[0140]
The closer the patterns of Map 1 and Map 2 are, the smaller the difference between the AND area and the OR area becomes, and the similarity approaches 1. Further, the less the patterns of Map 1 and Map 2 are similar, the smaller the AND area becomes, and the similarity approaches 0. Further, after dividing into small regions, a density distribution image in which luminance according to the density is assigned to each small region is created, and a normalized cross-correlation coefficient generally used in image processing may be used as the similarity. . Then, similarly to the image similarity evaluation, the n devices / chambers are extracted in the order of the highest similarity, and the evaluation value n is given to the device / chamber with the highest similarity, and the smaller the similarity, the smaller the evaluation value. Evaluation value may be given.
[0141]
Next, FIG. 29 shows an example of similarity evaluation of element information. For the waveforms 1 and 2 of the elemental information spectrum to be compared, a logical operation of AND and OR is performed on a region where the waveforms overlap, and the area obtained by the AND operation is obtained as the AND area, and the area obtained by the OR operation is obtained as the OR area. The similarity is obtained in the same manner as the similarity evaluation of the map. Then, similarly, n devices / chambers are extracted in descending order of similarity, an evaluation value n is given to the device / chamber with the highest similarity, and the evaluation value is set such that the smaller the similarity, the smaller the evaluation value. give. Alternatively, similarity evaluation may be performed based on the presence or absence of a representative element included in the defect A and the defect B to be compared. For example, the evaluation value may be defined as evaluation value = (number of elements included in both defect A and defect B) / (number of elements included in either defect A or defect B). This evaluation value has the same tendency as the image similarity and the waveform similarity described above.
[0142]
Next, FIG. 30 shows an embodiment of the evaluation of the degree of association between the time of start of construction and the time series data such as the density of defect detection points, the ratio of defect classification, or the processing log. First, an evaluation target period is set near the start time of a product wafer in which an abnormality has occurred in each device / chamber. This may be limited to a period before the start time of the wafer, or may be limited to a period before and after including the start time of the wafer. The rule for limiting the period may be specified by specifying a time, for example, within the past several days from the present time, by specifying the number of processed wafers, or by specifying the number of processed lots. Here, a threshold value for judging normal is determined in advance. When both positive and negative values are taken as in the deviation amount from the set value of the processing log shown in FIG. 21, threshold values may be set for each of the positive and negative values, and the threshold value is set for the absolute value. May be.
[0143]
Next, it is determined whether or not there is a data value exceeding a threshold value in the evaluation period of the data value. If there is a data value exceeding the threshold value, an evaluation value of 1 is given to a device / chamber exceeding the threshold value. Alternatively, as shown in FIG. 30, the differential value of the variation of the data value may be calculated, a threshold value may be provided for the differential value, and the evaluation value may be calculated in the same manner.
[0144]
Next, an embodiment of a method of evaluating the degree of association between the device maintenance time and the start time will be described. First, an evaluation target period is set in the same manner as described with reference to FIG. Then, during the evaluation target period, the presence or absence of maintenance is determined. When the maintenance has been performed, for example, an evaluation value 1 is given to the apparatus / chamber for which the maintenance has been performed.
[0145]
These calculation methods are not limited to the disclosed methods, and may be any methods that can evaluate the degree of association. Also, it is not necessary to perform all of these operations, and it is sufficient to perform the operations on data for which data acquisition is being performed. Further, it may be performed only for the items separately specified by the user.
[0146]
The determination of the degree of association by device and chamber in Step 502 can be performed, for example, as follows. First, the relevance evaluation value for each device / chamber is defined as in the following (Equation 1).
[0147]
(Equation 1)

[0148]
Here, i is a process, j is a device number, k is a chamber number, Pn (i, j, k) is an evaluation value for each evaluation item, z is the number of evaluation items, and An is a weight coefficient for each evaluation item. . As described above, the evaluation value of each evaluation item is multiplied by a weighting factor for each device / chamber and added to determine the magnitude of the relevance evaluation value, thereby extracting the device related to the abnormal wafer. Can be.
[0149]
This calculation method is not limited to the disclosed method, but may be any method that can evaluate the degree of association. Here, the weight coefficient may be arbitrarily set by the user, or may be automatically distributed according to a certain rule. As a rule setting method, for example, when an element used only in a certain device is included in a defect or a distribution pattern that appears only in a certain device is detected, the weight of element information and distribution information is increased, respectively. It may be.
[0150]
Then, in Step 503, according to the result, the display screen shown in FIG. 17 may be displayed in such a manner that the character of the corresponding device is displayed in a different color, so that the character is different from other devices. Further, the information of the extracted apparatus may be notified to the relevant staff by a mail system (not shown) or the like. In addition, ranking may be performed according to the degree of association, and a plurality of processing apparatuses / chambers may be displayed.
[0151]
In the present embodiment, the example in which the relevance evaluation engine 17 is mounted on the data analysis device 7 has been described. However, as long as the calculation processing can be performed using the above-described information, the relevance evaluation engine 17 may exist independently. It may be included in another device.
[0152]
【The invention's effect】
According to the present invention, it is possible to easily specify a processing apparatus related to a wafer determined to be abnormal by inspection, and to display a state such as a frequency of occurrence of a defect for each processing apparatus near a period when the wafer is started. Since it is possible, it is easy to estimate a device in which a problem has occurred.
[0153]
Further, according to the present invention, data obtained in connection with a defect occurring in the apparatus is automatically compared with data obtained from an abnormal product wafer defect, and evaluated. Since the device candidates can be extracted, the man-hour required for specifying the device in which the problem has occurred can be greatly reduced.
[Brief description of the drawings]
FIG. 1 is a diagram showing the concept of the present invention.
FIG. 2 is a diagram showing an example of the configuration of the present invention.
FIG. 3 is a diagram showing an example of manufacturing route information used in the present invention.
FIG. 4 is a diagram showing an example of manufacturing route information used in the present invention.
FIG. 5 is a flowchart illustrating an embodiment of a method for acquiring data to be recorded in a database according to the present invention.
FIG. 6 is a flowchart showing an embodiment of an application procedure of the present invention.
FIG. 7 is a diagram showing one embodiment of a data analysis / display method of the present invention.
FIG. 8 is a diagram showing one embodiment of a data analysis / display method of the present invention.
FIG. 9 is a flowchart showing one embodiment of the data analysis method of the present invention.
FIG. 10 is a diagram showing an embodiment of a data analysis result display method according to the present invention.
FIG. 11 is a flowchart showing one embodiment of the data analysis method of the present invention.
FIG. 12 is a diagram showing an embodiment of a data analysis result display method according to the present invention.
FIG. 13 is a diagram showing one embodiment of a data analysis result display method of the present invention.
FIG. 14 is a diagram showing an embodiment of a data analysis result display method according to the present invention.
FIG. 15 is a diagram showing an embodiment of a data analysis result display method according to the present invention.
FIG. 16 is a diagram showing an embodiment of a data analysis result display method according to the present invention.
FIG. 17 is a diagram showing one embodiment of a data analysis result display screen of the present invention.
FIG. 18 is a diagram showing one embodiment of a data analysis result display screen of the present invention.
FIG. 19 is a diagram showing one embodiment of a data analysis result display screen of the present invention.
FIG. 20 is a diagram showing one embodiment of a data analysis result display screen of the present invention.
FIG. 21 is a diagram showing one embodiment of a data analysis result display screen of the present invention.
FIG. 22 is a diagram showing one embodiment of a data analysis result display screen of the present invention.
FIG. 23 is a diagram showing an example of the configuration of the present invention.
FIG. 24 is a flowchart showing one embodiment of the data analysis method of the present invention.
FIG. 25 is an explanatory diagram showing one embodiment of the data analysis method of the present invention.
FIG. 26 is an explanatory diagram showing one embodiment of the data analysis method of the present invention.
FIG. 27 is a flowchart showing one embodiment of the data analysis method of the present invention.
FIG. 28 is an explanatory diagram showing one embodiment of the data analysis method of the present invention.
FIG. 29 is an explanatory diagram showing one embodiment of the data analysis method of the present invention.
FIG. 30 is an explanatory diagram showing one embodiment of the data analysis method of the present invention.
[Explanation of symbols]
DESCRIPTION OF SYMBOLS 1 ... Manufacturing apparatus 2 ... Inspection apparatus 3 ... Detailed analysis apparatus 4 ... Process management database
5: Device status database 6: Inspection / analysis database 7: Data analyzer 8: Display device 9: Network 10: Result display screen 11: Product wafer information display area 12: Device information display area 13: Wafer identification information display area 14 ... Wafer inspection / analysis information display area 15 ... Device route information display area 16 ... Start date and time display bar 17 ... Relevance evaluation engine

Claims (14)

A method for analyzing defect information obtained by inspecting a workpiece in a thin film device manufacturing process,
In a manufacturing process of a thin film device, data obtained by inspecting a substrate to be processed before performing processing in a predetermined processing step including a plurality of processing apparatuses and the substrate to be processed after performing processing in the predetermined processing step Extracting the defects generated on the substrate to be processed by performing the processing in the predetermined processing step by comparing the data obtained by inspecting;
If the number of the extracted defects is larger than a preset number, the information indicating the process start history of the substrate to be processed in the plurality of processing apparatuses constituting the predetermined processing step and the processing target in the predetermined processing step. A defect information analysis method characterized by displaying information on the status of a processing apparatus that has processed a substrate on a screen. The data obtained by inspecting the substrate to be processed before or after performing the processing in the predetermined processing step or after performing the processing in the predetermined processing step is appearance information of the defect of the processing target machine, containing the defect. 2. The defect information analysis method according to claim 1, further comprising at least one of elemental information, distribution of defects in the device, number of defects, and defect density. A method for analyzing defect information obtained by inspecting a workpiece in a thin film device manufacturing process,
In the manufacturing process of the thin film device, a defect generated on the substrate to be processed by performing the process in a predetermined processing process including a plurality of processing devices is extracted,
When the number of the extracted defects is larger than a preset number, a detailed image of the extracted defect is obtained by imaging the extracted defect,
Classifying the detailed image of the extracted defect,
The detailed image of the classified defects and the distribution information of the extracted defects on the substrate to be processed, the information indicating the work history of the substrate to be processed in a plurality of processing apparatuses constituting the predetermined processing step, and A defect information analysis method characterized by displaying on a screen together with information on the state of a processing apparatus that has processed the substrate to be processed in a predetermined processing step. A method for analyzing defect information obtained by inspecting a workpiece in a thin film device manufacturing process,
In the manufacturing process of the thin film device, a defect generated on the substrate to be processed by performing the process in a predetermined processing process including a plurality of processing devices is extracted,
When the number of the extracted defects is larger than a preset number, the extracted defects are imaged, and a detailed image of the extracted defects is classified, and each of the classified defects is processed on the substrate to be processed. Get distribution information,
The distribution information on the target substrate for each of the classified defects, the information indicating the construction history of the target substrate in a plurality of processing apparatuses constituting the predetermined processing step, and the processing target substrate in the predetermined processing step And displaying on a screen information relating to the status of the processing device that has processed the defect information. If the number of the extracted defects is larger than a preset number, a detailed image of the extracted defect is obtained, and the obtained detailed image of the defect is information indicating the work history of the substrate to be processed. 5. The defect information analysis method according to claim 1, wherein the defect information is displayed on the screen together with information on a status of the processing apparatus. 6. The defect information analysis method according to claim 5, wherein the detailed image of the defect displayed on the screen is classified according to a type of the defect. A method for analyzing a defect removal method obtained by inspecting a workpiece in a thin film device manufacturing process, by performing processing in a predetermined processing step including a plurality of processing devices in a thin film device manufacturing process. Extracting defects generated on the substrate to be processed,
Using the information of the number or density of the extracted defects to determine the presence or absence of the occurrence of defects,
If the above detection of defect occurrence is detected by the determination, the extracted defect is imaged to obtain a detailed image of the extracted defect,
The image is classified to obtain distribution information on the substrate to be processed for each of the classified defects, and the distribution information on the substrate to be processed for each of the classified defects and a plurality of components constituting the predetermined processing step. A defect occurrence abnormality of the plurality of processing apparatuses based on information on a process start history of the processing target substrate in the processing apparatus and information on a state of the processing apparatus processing the processing target substrate in the predetermined processing step; Evaluate
A defect information analysis method, characterized by displaying, on a screen, information on a processing device having a high relevance to the defect occurrence abnormality among the plurality of processing devices. The information indicating the process history of the processing target substrate is information displayed so that a processing device that has actually processed the processing target substrate in a plurality of processing devices configuring the predetermined processing process can be distinguished from other devices. The defect information analysis method according to claim 1, wherein the defect information analysis method comprises: The information on the status of the processing apparatus includes a log of processing conditions of the plurality of processing apparatuses that have processed the substrate in the predetermined processing step, and the plurality of processing when processing the substrate by the plurality of processing apparatuses. 8. The defect information analysis method according to claim 1, wherein the data is data relating to a defect generated on the substrate to be processed for each apparatus. The information on the status of the processing apparatus is information on a time-series transition of processing condition logs of the plurality of processing apparatuses, or a time-series transition of a defect occurring on the substrate to be processed for each of the plurality of processing apparatuses. The defect information analysis method according to claim 1, wherein the defect information analysis method includes any one of information. 5. The display screen further displays a type of the substrate to be processed, a number of a lot as a processing unit, a number of the substrate to be processed, and a range of a processing step. Or the defect information analysis method according to any one of 7. An apparatus for analyzing defect information obtained by inspecting a workpiece in a thin film device manufacturing process,
In a manufacturing process of a thin film device, data obtained by inspecting a substrate to be processed before performing processing in a predetermined processing step including a plurality of processing apparatuses and the substrate to be processed after performing processing in the predetermined processing step Defect extraction means for extracting a defect generated on the substrate to be processed by performing the processing in the predetermined processing step by comparing data obtained by inspecting;
The number of defects extracted by the defect extraction means is compared with a preset number, and when the number of the extracted defects is larger than the preset number, the number of the plurality of processing devices constituting the predetermined processing process is determined. Processing means for processing information indicating the process start history of the substrate to be processed and information relating to the status of the processing apparatus which has processed the substrate to be processed in the predetermined processing step, and displaying the processed information on a screen. Defect information analyzer. The defect extraction means, before performing the processing in the predetermined processing step, or appearance information of the defect on the processing target substrate obtained by inspecting the processing target substrate after performing the processing in the predetermined processing step, A defect generated on the substrate to be processed is extracted by using at least one of data on element content of the defect, distribution of the defect in the device, the number of defects, and the density of the defect. Item 13. The defect information analysis device according to Item 12. An apparatus for analyzing defect information obtained by inspecting a workpiece in a thin film device manufacturing process,
Defect extracting means for extracting a defect generated on the substrate to be processed by performing processing in a predetermined processing step configured by a plurality of processing apparatuses in a thin film device manufacturing process,
Abnormality determining means for determining whether there is an abnormality in defect occurrence using information on the number or density of defects extracted by the defect extracting means,
When the abnormality determining means determines an abnormality of the defect occurrence, distribution information obtained by classifying the detailed image of the extracted defect and the processing target substrate in a plurality of processing apparatuses constituting the predetermined processing step. An evaluation unit that evaluates the association between the information on the construction start history and the defect generation abnormality of the plurality of processing apparatuses based on information on the state of the processing apparatus that has processed the substrate to be processed in the predetermined processing step,
A defect information analyzing apparatus comprising: a display unit that displays, on a screen, information relating to a processing device that is highly relevant to the defect occurrence abnormality among the plurality of processing devices evaluated by the processing unit.

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