JPS61256237A - Defect inspection for cyclic pattern - Google Patents
Defect inspection for cyclic patternInfo
- Publication number
- JPS61256237A JPS61256237A JP60096593A JP9659385A JPS61256237A JP S61256237 A JPS61256237 A JP S61256237A JP 60096593 A JP60096593 A JP 60096593A JP 9659385 A JP9659385 A JP 9659385A JP S61256237 A JPS61256237 A JP S61256237A
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- Prior art keywords
- pattern
- image data
- periodic pattern
- defect
- pixel
- Prior art date
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Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
- G01N21/956—Inspecting patterns on the surface of objects
- G01N21/95607—Inspecting patterns on the surface of objects using a comparative method
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- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Exposure And Positioning Against Photoresist Photosensitive Materials (AREA)
- Testing Or Measuring Of Semiconductors Or The Like (AREA)
- Closed-Circuit Television Systems (AREA)
- Length Measuring Devices By Optical Means (AREA)
- Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
- Testing Of Optical Devices Or Fibers (AREA)
- Preparing Plates And Mask In Photomechanical Process (AREA)
Abstract
Description
【発明の詳細な説明】
〔発明の利用分野〕
本発明は、カラーテレビ用ブラウン管に用いられるシャ
ドウマスク、カラー撮像装置用色分解フィルタ、液晶表
示パネル用カラーフィルタ、電子管に用いられるメツシ
ュ状電極、ＶＤＴフィルタ、ｍ過装を用メツシュフィル
タ、ロータリーエンコーダ、リニアエンコーダ、ＩＣ用
７オトマスク、フレネルレンズ、レンチキュラーレンス
ナト一定の光学的性質、形状をもつ単位（以下単位パタ
ーン）が−次元方向、或いは二次元方向に規則的に繰り
返し配列されている工業製品、あるいは単位パターンが
、その光学的性質、形状及び１次元方向、２次元方向の
配列ピッチが除々に変化しながら繰り返し、配列されて
いる工業製品のキズ、ピンホール、黒点、ゴミなどの欠
陥を自動的に検査する方法に関するものである。DETAILED DESCRIPTION OF THE INVENTION [Field of Application of the Invention] The present invention relates to shadow masks used in cathode ray tubes for color televisions, color separation filters for color imaging devices, color filters for liquid crystal display panels, mesh-like electrodes used in electron tubes, For VDT filters, mesh filters, rotary encoders, linear encoders, 7-otomasks for IC, Fresnel lenses, lenticular lenses Units with certain optical properties and shapes (hereinafter referred to as unit patterns) are arranged in the -dimensional direction, or Industrial products that are regularly and repeatedly arranged in two dimensions, or industries in which unit patterns are repeatedly arranged while their optical properties, shapes, and arrangement pitches in one and two dimensions gradually change. It relates to a method for automatically inspecting products for defects such as scratches, pinholes, black spots, and dust.
従来、上記の様な工業製品の欠陥検査は、裸眼又は顕微
鏡を用いて眼視的に行なわれているのが通例であるが、
多数の製品を検査するためには多大の人手を必要とし、
また官能検査であるために検査精度及び信頼性に欠ける
という問題があった。Conventionally, defect inspections of industrial products such as those mentioned above have been carried out visually using the naked eye or a microscope.
Inspecting a large number of products requires a large amount of manpower,
Furthermore, since it is a sensory test, there is a problem in that it lacks test accuracy and reliability.
この様な問題を解決するために、等ピッチ配列の周期性
パターンをもつ工業製品に関しては、配列単位及び欠陥
の形状を十分に解儂する様な顕微鏡的撮影手段によって
得られたビデオ信号を調べてパターン認識を行なうか、
あるいは欠陥のないパターンを同様に撮影して得られた
信号と比較する等の手段により欠陥を検出して検査する
方法などが提案され、一部実施されている例もあるが、
この様な方法では検出しようとする欠陥の大きさに応じ
た機械的精度が必要となり、微細な欠陥を検出するため
には高精度の装置が必要となるため、装置が高価となり
、また顕微鏡的な撮影であるために一度に処理できる画
面の大きさが小さくなり、検査すべきパターン全体を検
査するのに多大の時間を要するなどの問題があった。In order to solve such problems, for industrial products with periodic patterns of equal pitch arrays, we investigated video signals obtained by microscopic imaging methods that sufficiently reveal the array units and defect shapes. Perform pattern recognition using
Alternatively, methods of detecting and inspecting defects by comparing signals obtained by similarly photographing a defect-free pattern have been proposed, and some examples have been implemented.
This method requires mechanical precision according to the size of the defect to be detected, and high-precision equipment is required to detect minute defects, which makes the equipment expensive and requires microscopic precision. Due to the large size of the image, the size of the screen that can be processed at one time is small, and it takes a lot of time to inspect the entire pattern to be inspected.
また周期的開口をもつ製品、たとえば電子管用メツシュ
状電極などについては、コヒーレント光を照射したとき
の周期性パターンによる光の回折現象を利用した光学的
７−リエ変換空間フィルタリング法が提案されているが
、この方法では検査速度、検出感度には優れているもの
の被検査パターン毎に空間フィルターを作成しなければ
ならず、かつ、精密な光学系が必要となるために装置が
高価となり、さらに、欠陥は検出できるが、その欠陥開
口の基準値に対する大小関係が判別できないなど問題が
多く、新たな検査方法が望まれていた。For products with periodic apertures, such as mesh electrodes for electron tubes, an optical 7-Lier transform spatial filtering method has been proposed that utilizes the diffraction phenomenon of light due to periodic patterns when irradiated with coherent light. However, although this method has excellent inspection speed and detection sensitivity, it requires creating a spatial filter for each pattern to be inspected, and requires a precise optical system, making the equipment expensive. Although defects can be detected, there are many problems such as the inability to determine the size relationship of the defect aperture with respect to a reference value, and a new inspection method has been desired.
本発明は、上記の様な問題を解決し、周期性パターンを
能率良く、高精度に検査でき、かつ欠陥の白黒判別がで
きる検査方法の提供を目的としたものである。SUMMARY OF THE INVENTION The present invention aims to solve the above-mentioned problems, and to provide an inspection method that can efficiently and accurately inspect periodic patterns, and can discriminate between black and white defects.
この目的を達成するため、本発明は、周期性パターンの
単位パターンを十分に解像しない、又は全く解像しない
様な大面積の撮影視野であっても。In order to achieve this objective, the present invention provides an imaging field of view of a large area in which the unit pattern of the periodic pattern is not sufficiently resolved or not resolved at all.
得られるビデオ信号には欠陥情報が含まれ、画像処理の
手法である画面加算処理によるビデオ信号のランダムノ
イズ低減効果と、被検査パターンを所定の方法で移動さ
せたときに欠陥のない部分のビデオ信号がパターンの移
動の前後でほとんど変化せず、移動の前後の画像データ
を減算すると。The resulting video signal includes defect information, and the random noise reduction effect of the video signal is achieved by screen addition processing, which is an image processing method, and when the pattern to be inspected is moved in a predetermined manner, the video of the defect-free portion is The signal changes little before and after the pattern movement, and when you subtract the image data before and after the movement.
欠陥のない部分の単位パターンによるビデ第１ｇ号の変
化と撮像系のシ二一ディング及び光学系のゴミなどによ
るビデオ信号の変化が消去され、欠陥のない部分の画像
データはほぼＯとなり、欠陥部のみ局部的に値が変化す
る事を見い出し、これにより欠陥の検出を行なうように
した点を特徴とする。Changes in video signal No. 1g due to the unit pattern in the defect-free area and changes in the video signal due to shinning in the imaging system and dust in the optical system are erased, and the image data in the defect-free area becomes almost O, indicating that there is no defect. This method is characterized by the fact that it has been found that the value changes locally only in the area, and defects can be detected based on this.
以下、実施例にもとづき本発明の詳細な説明する。 Hereinafter, the present invention will be described in detail based on Examples.
第１図は本発明による周期性パターンの検査方法の一実
施例で、第２図示の様に周期的な開口な単位パターン１
１として持つパターンの開ロ面積ノ異常を検査する例に
適用した場合の方法について示したもので、直流電源９
で点灯される白熱ラング８と拡散板７で構成される透過
照明部により被検査パターン６を照明し、ＴＶ左カメラ
で検査領域を撮影する。画像処理装置２はＴＶ左カメラ
出力信号なＡ／Ｄ変換してデジタル画像データとし、フ
レームメモリ及び演算器により画面の加算、減算を含む
各種の画像処理を高速で行うものである。FIG. 1 shows an embodiment of the periodic pattern inspection method according to the present invention, in which a periodic opening unit pattern 1 as shown in FIG.
This shows the method applied to an example of inspecting abnormalities in the open area of a pattern with a DC power supply 9.
The pattern to be inspected 6 is illuminated by a transmissive illumination unit composed of an incandescent lamp 8 and a diffuser plate 7, and the inspection area is photographed by the left camera of the TV. The image processing device 2 A/D converts the TV left camera output signal into digital image data, and performs various image processing including addition and subtraction on the screen at high speed using a frame memory and an arithmetic unit.
３は制御部で画像処理装置２及びＸＹステージ１０と駆
動部５で構成されるパターン移動機構を制御するもので
ある。なお、第２図で、１２ 、１３は欠陥をもった単
位パターンを表わす。Reference numeral 3 denotes a control section that controls the pattern moving mechanism composed of the image processing device 2, the XY stage 10, and the drive section 5. In FIG. 2, 12 and 13 represent unit patterns having defects.
次に、この実施例によりパターン８の欠陥を検出する動
作について説明する。なお、ここでは、説明を簡単にす
るため、ＴＶ左カメラによるビデオ信号の単位開口によ
る変化が無視できる撮影条件１例えば１画素に対応する
パターン面積に単位開口１１が１０個程度入る様な撮影
条件とし、パターンを移動させて、変位させる方向がＴ
Ｖカメラ１１の走査線方向で、パターンの変位距離が画
素ピッチの整数倍となっている場合について説明する。Next, the operation of detecting defects in pattern 8 according to this embodiment will be explained. In order to simplify the explanation, here, we will use a shooting condition 1 in which changes due to unit apertures in the video signal from the TV left camera can be ignored. and the direction of displacement is T
A case will be described in which the displacement distance of the pattern is an integral multiple of the pixel pitch in the scanning line direction of the V camera 11.
まず、第３図（α）はパターンの欠陥がある所を通る直
線上の光透過重分布を示す図で１４は第２図のパターン
１３に示す様に開口面積が正常なパターン１１よりも大
きい欠陥（以下白点という）による光透過率の変化を示
し、１５は第２図のパターン１２）Ｃ示す様に開口面積
が正常なパターン１１よりも小さい欠陥（以下黒点）に
よる光透過率の変化を示す。First, Fig. 3 (α) is a diagram showing the light transmission weight distribution on a straight line passing through a defective part of the pattern, and 14 is a diagram whose opening area is larger than that of the normal pattern 11, as shown in pattern 13 of Fig. 2. 15 shows a change in light transmittance due to a defect (hereinafter referred to as a white dot), and 15 shows a change in light transmittance due to a defect (hereinafter referred to as a black dot) whose opening area is smaller than that of the normal pattern 11, as shown in pattern 12)C in Figure 2. shows.
次に、第３図（ｂ）は第３図（α）と同じ線上を走査し
たビデオ信号を示す図で、パターンの照明ムラ、撮像面
の感度ムラ等によるゆるやかな信号変化（シェーディン
グ）とビデオ信号処理回路で発生するランダムノイズ、
及び光学系に付着したゴミなどによる信号の局部的変化
１６とが示されている。Next, FIG. 3(b) is a diagram showing a video signal scanned on the same line as FIG. Random noise generated in signal processing circuits,
and a local change 16 in the signal due to dust or the like attached to the optical system.
また、第３図（Ｃ）は画像処理装置２により画面加算処
理した結果を示す図で、箒３図（勾のランダムノイズ成
分の比率が、加算回数をＮとしたときに、、１／ｖ／Ｔ
にまで減少していることを示す。In addition, FIG. 3(C) is a diagram showing the result of screen addition processing performed by the image processing device 2. /T
This shows that it has decreased to .
さらに、第３図（ｄ）はパターンを変位させて画面加算
処理をした結果を示し、パターンの移動と共にパターン
上の欠陥による信号も移動しているが、撮像系のシェー
ディング及び光学系のゴミなどによる信号１６の位置は
変化していないことを示している。Furthermore, Fig. 3(d) shows the result of screen addition processing by displacing the pattern, and as the pattern moves, signals due to defects on the pattern also move, but shading in the imaging system and dust in the optical system also move. This shows that the position of signal 16 has not changed.
そして、第３図（−）は、第３図（Ｃ）から第３図（ｄ
）の画像データを減算した結果を示したもので、第３図
（Ｃ１、（ｄ）のデータに含まれるシェーディングや、
１６に示す様なパターンの移動によって変化しない成分
が消去されて、パターンの光透通事変化による信号と、
低減されたランダムノイズ成分だけが残り、この結果、
欠陥による信号はパターンの移動量に応じた画素数離れ
た位置で、その近傍の平均値に対する値の差がほぼ同じ
で、符号が反転して現われ、その反転する順序は欠陥の
種類（白点。Figure 3 (-) is from Figure 3 (C) to Figure 3 (d).
) shows the result of subtracting the image data in Figure 3 (C1, (d)) and the shading included in the data in Figure 3 (C1, (d))
The components that do not change due to the movement of the pattern as shown in 16 are erased, and the signal due to the change in light transmission of the pattern and
Only the reduced random noise component remains, resulting in
Signals due to defects appear at positions separated by a number of pixels according to the amount of movement of the pattern, with almost the same difference in value from the average value in the vicinity, and with reversed signs, and the order in which the signals are reversed depends on the type of defect (white dots). .
黒点）によって逆転していることが判る。It can be seen from the black dots that it is reversed.
以上はパターンの変位前及び変位後、各々画面加算処理
を行った２画面画像データ間で減算する例を説明したも
のであるが、移動前の加算データから移動後の画面デー
タを加算と同一フレーム数減算しても結果は全く同じで
ありフレームメモリも１面のみで処理可能である。The above describes an example of subtracting between two screen image data that have been subjected to screen addition processing before and after pattern displacement. Even if the number is subtracted, the result is exactly the same, and the processing can be performed using only one side of the frame memory.
したがって、以上の処理をした画像データをＴＶモニタ
４で観察すれば、欠陥部のみ明るさが局部的に変化して
いるため、容易に欠陥を認識でき、さらに欠陥部での周
囲に対する明暗の反転の順序で欠陥の種類（白点、黒点
）が識別できる。Therefore, if you observe the image data processed above on the TV monitor 4, you can easily recognize the defect because the brightness changes locally in the defective area, and furthermore, the brightness of the defective area is reversed with respect to the surrounding area. Defect types (white dots, black dots) can be identified in this order.
また、この１惨データから、近傍平均値の減算、あるい
は微分処理、特にパターン移動が平行移動である場合に
はパターン上の一点が移動の前後で各々対応する画素間
の差を演算する画像処理を行うと、第３図Ｖ）に示す様
に、前記画像データのゆるやかな変化成分が除去され、
所定の閾値との比較により自動的に欠陥を検出できる。In addition, from this one-shot data, subtraction of neighborhood average values or differential processing, especially when the pattern movement is parallel movement, image processing that calculates the difference between corresponding pixels before and after the movement of one point on the pattern. When performing this, as shown in FIG. 3 V), the gradual change component of the image data is removed,
Defects can be automatically detected by comparison with a predetermined threshold.
ところで、以上は画面データの加算、パターン移動、減
算によって欠陥による信号以外を消去する方法を説明し
たものであるが、撮影条件によっては単位パターンによ
るビデオ信号の変化が無視できない場合があり、この場
合には前記説明の様に、画素ピッチの整数倍の移動では
単位パターン信号が第４図（ｂ） Ｋ示す様に卯減算後
の画像データに残り、微小な欠陥が検出できなくなる。By the way, the above describes a method for erasing signals other than those caused by defects by addition, pattern movement, and subtraction of screen data, but depending on the shooting conditions, changes in the video signal due to unit patterns may not be ignored, and in this case As explained above, when the pixel pitch is moved by an integral multiple, the unit pattern signal remains in the image data after the subtraction, as shown in FIG. 4(b), and minute defects cannot be detected.
なお、第４図（α）は減算前のデータである。Note that FIG. 4 (α) is data before subtraction.
しかし、この様な場合には、例えば等ピッチ配列の周期
パターンでは配列ピッチの整数倍、ストライプ状パター
ンではストライプの方向へ、同心円パターンではその中
心で回転、単位パターンを回転させ、円周上に配列した
パターンでは、その配列の中心を回転中心として配列角
度の整数倍の回転を各々のパターン移動の条件として設
定すれば、各々欠陥のないパターンによる画像データは
移動の前後で変化せず、したがって、第５図（α）。However, in such a case, for example, for a periodic pattern with an equal pitch, rotate in an integral multiple of the array pitch, for a striped pattern, rotate in the stripe direction, for a concentric pattern, rotate at its center, rotate the unit pattern, and rotate the unit pattern on the circumference. For arrayed patterns, if a rotation of an integral multiple of the array angle is set as a condition for each pattern movement with the center of the array as the rotation center, the image data of each defect-free pattern will not change before and after the movement. , Figure 5 (α).
（，６＋に示す様に、加減算後の画像データは第３図（
−）で示すものと同様な結果が得られ、微小な欠陥の検
出が可能となる。(,6+, the image data after addition and subtraction is shown in Figure 3 (
Results similar to those shown in -) are obtained, making it possible to detect minute defects.
次にパターンの移動距離について説明する。第６図は欠
陥パターン１７ 、１８の光学像と、画素Ｐの関係、加
減算後の画像データＤの関係を説明する図で、第６図（
α）はパターン移動量が画素ピッチの整数倍、第６図（
ｂ）は整数倍でないときの例を示す。Next, the movement distance of the pattern will be explained. FIG. 6 is a diagram explaining the relationship between the optical images of the defect patterns 17 and 18, the pixels P, and the relationship between the image data D after addition and subtraction.
α) means that the amount of pattern movement is an integer multiple of the pixel pitch, Figure 6 (
b) shows an example when it is not an integral multiple.
そして、第６図（ａ）では欠陥部の画像データＤが近傍
平均値に対して上下対称となるが、整数倍でない場合は
第６図（Ａ＋の例で示す様に欠陥像による画像データＤ
の変化が隣接画素に振り分けられる割合がパターン移動
の前後で異なるために、近傍平均値に対する上下′の対
称性が失なわれ、自動検出処理を行なう上で誤差の要因
となる。In Fig. 6(a), the image data D of the defective part is vertically symmetrical with respect to the neighborhood average value, but if it is not an integral multiple, then as shown in Fig. 6(a), the image data D of the defective part is
Since the rate at which changes in are distributed to adjacent pixels differs before and after pattern movement, the vertical symmetry with respect to the neighborhood average value is lost, which causes errors in automatic detection processing.
したがって、パターンの移動は、欠陥像とそれを受ける
画素との位置関係がパターンの移動の前後で同一となる
ことが好ましく、例えば画素の配列方向に移動させる場
合には、画素ピッチの整数倍に設定する方が良い納采が
得られる事になる。Therefore, when moving the pattern, it is preferable that the positional relationship between the defective image and the pixel that receives it is the same before and after moving the pattern. For example, when moving in the pixel arrangement direction, You will get better results if you set it up.
そこで、単位バター／によるビデオ信号の変化が無視し
得る場合には画素ピッチの整数倍、無視できない場合に
はパターンの配列ピッチと画素ピッチの公倍数を移動距
離とすれば前述の欠陥のないパターンによる画像データ
の消去と、移動距離を画素ピッチの整数倍にすることの
両条件を満足する事が出来る。Therefore, if the change in the video signal due to unit butter/ can be ignored, the moving distance should be an integral multiple of the pixel pitch, and if it cannot be ignored, then the moving distance should be a common multiple of the pattern arrangement pitch and the pixel pitch. It is possible to satisfy both the conditions of erasing image data and making the moving distance an integral multiple of the pixel pitch.
ところで、以上の本発明の検査方法で必要なバターンの
変位は、加減算後の画像データから欠陥のないパターン
の情報の消去を不可欠の条件として、この条件のもとで
、可能ならば、欠陥像とそれを受ける画素との位置関係
を移動の前後で一致させる方が良好な結果が得られるべ
きことを意味している。したがって、これらの条件を満
足すれば移動の方向は必ずしも画素の配列方向と一致し
ていなくても良い。By the way, the displacement of the pattern required in the above inspection method of the present invention is based on the essential condition of erasing the information of the defect-free pattern from the image data after addition and subtraction, and under this condition, if possible, the defect image This means that a better result should be obtained if the positional relationship between the pixel and the pixel that receives it is the same before and after the movement. Therefore, as long as these conditions are satisfied, the direction of movement does not necessarily have to coincide with the pixel arrangement direction.
次に、本発明の一実施例として、欠陥像と画素の位置関
係によって生ずる欠陥信号レベルの変動を低減する方法
について説明する。Next, as an embodiment of the present invention, a method for reducing fluctuations in defect signal level caused by the positional relationship between a defect image and a pixel will be described.
第７図（α）は欠陥パターン１７の像が一つの画素Ｐの
中央に、第７図（Ａｌは欠陥パターン１７の像が４つの
画素Ｐの接点上にそれぞれある状態を示す。FIG. 7(α) shows a state in which the image of the defect pattern 17 is located at the center of one pixel P, and FIG.
このように、同一の欠陥であっても第７図（α）の様に
欠陥による信号変化の２はとんどが１画素に集中する場
合と、第７図（ｂ）の様に４画素に分散される場合とで
は、欠陥信号レベルにほぼ４倍の差が生じ、この結果、
欠陥検出の再現性が低いという問題を生じる。しかして
、この様な欠陥信号の周囲画素への分散は、欠陥像を中
心とした３×３画素の領域内にその大部分が収まってお
り、その外側への影響は無視できるため、画像処理でよ
く用いられる空間フィルター処理により、最大３×３画
素の近傍画素加算処理を加減算後の画像データに対して
行うと、欠陥部において周囲の画素に分散した欠陥信号
の合計が得られ、欠陥信号レベルの変化を低減する事が
できる。In this way, even if the defect is the same, the signal change due to the defect is mostly concentrated in one pixel as shown in Fig. 7 (α), and in the case of 4 pixels as shown in Fig. 7 (b). There is a difference of almost four times in the defect signal level between the two cases, and as a result,
This causes a problem that the reproducibility of defect detection is low. However, most of the dispersion of such defect signals to surrounding pixels is within a 3 x 3 pixel area centered on the defect image, and the influence on the outside can be ignored, so image processing By performing spatial filtering processing, which is often used in Changes in level can be reduced.
次に、本発明のさらに別の一実施例として、欠陥の検出
と種類の判別を自動的に行う方法について説明する。Next, as yet another embodiment of the present invention, a method for automatically detecting defects and determining types will be described.
第８図（α）はパターンを変位させる方向が画素の配列
方向と同じで、移動距離を画素ピッチの２倍とし、画面
加算処理を行った後、図で右側・＼パターン移動を行い
、加算と同一フレーム数、減算を行った結果の画像デー
タの例を示したもので、２０は白点欠陥、２１は黒点欠
陥による画像データの局部的変化を示し、それ以外の部
分は単位パターンの配列ピッチ、大きさなどのゆるやか
な変化に応じた画像データの変化（低周波変化）を表わ
している。In Figure 8 (α), the direction in which the pattern is displaced is the same as the pixel arrangement direction, the movement distance is twice the pixel pitch, and after performing screen addition processing, the pattern is moved to the right side in the figure, and the addition This shows an example of the image data obtained by subtracting the same number of frames as , where 20 shows a white spot defect, 21 shows a local change in image data due to a black spot defect, and the other parts are the arrangement of unit patterns. It represents changes in image data (low frequency changes) in response to gradual changes in pitch, size, etc.
ここで、欠陥部の画像データを詳しく見ると、欠陥が画
面加算時に対応する画素Ａ（２２，ｚ２′）、と減算時
に対応する画素Ｂ （２３、２３’）が欠陥情報をもつ
画素として、パターンの変位距離、つまり画素２ピツチ
に対応する画素数（２画素）離れて現われ、各々の画素
データなＩＡ ｐ ＩＢ ｐその中点に対応する画素Ｃ
（２４、２４’）の近傍平均値をＩＣとすると、■＾−
ＩＣ及びＩＢ−ＩＣは符号が反対で絶対値がほぼ同じ値
となり、この符号の順序が欠陥の種類（白点、黒点）に
対応し、Ｉ、 −ＩＢの絶対値が欠陥の大きさに比例す
ることが判る。また、エムとＩＢの平均値とＩＣの差は
、エム−ＩＢの値に対して十分に小さい比率となってい
ることも判る。そこでｓ ＩＡ−ＩＢが得られる様な画
像データ処理、例えば第８図（ｃＬ）に対してＩ（７’
） ＝ＩＣｊ−１’）＝Ｉ（ｊ＋１＞を演算すると第８
図（Ａｌに示す様になり、■（］゛−１）、Ｉ（）°＋
１）が、それぞれ■＾ｐＩＢとなる画素（２５，２５’
）に対しての欠陥の種類と大きさを示すデータとなり、
また前記、低周波変化も低減されるため、一定の閾値と
比較すれば、欠陥の検出と種類の判定が可能となる。Here, if we look at the image data of the defective part in detail, pixel A (22, z2'), which corresponds to the defect during screen addition, and pixel B (23, 23'), which corresponds to the defect during subtraction, are pixels that have defect information. The displacement distance of the pattern, that is, the number of pixels (2 pixels) corresponding to 2 pixels, appears apart, and each pixel data IA p IB p The pixel C corresponding to the midpoint.
If the neighborhood average value of (24, 24') is IC, then ■^-
IC and IB-IC have opposite signs and almost the same absolute value, and the order of the signs corresponds to the type of defect (white dot, black dot), and the absolute value of I, -IB is proportional to the size of the defect. It turns out that it does. It can also be seen that the difference between the average value of M and IB and IC is a sufficiently small ratio to the value of M-IB. Therefore, image data processing that yields s IA-IB, for example, I(7'
) =ICj-1')=I(j+1>, the 8th
As shown in the figure (Al, ■(]゛-1), I()°+
1) are pixels (25, 25') that are respectively ■^pIB
), the data shows the type and size of the defect.
Furthermore, since the aforementioned low frequency changes are also reduced, it becomes possible to detect defects and determine the type by comparing them with a certain threshold value.
しかしながら、このとき、第８図（ｈ） Ｋ示す様に、
欠陥として検出すべき画素の両側に符号が反対で値がＩ
Ａ−ＩＢの半分のデータ（２６、２６’）が発生するた
め、閾値に対して２倍以上の信号レベルをもつ欠陥に対
しては偽欠陥をも検出してしまう事になり、検出と種類
の判定に不都合を生ずる。However, at this time, as shown in Fig. 8 (h) K,
On both sides of the pixel to be detected as a defect, there are values of I with opposite signs.
Since half of the data (26, 26') of A-IB is generated, false defects will also be detected for defects with a signal level that is more than twice the threshold value, making it difficult to detect and type defects. This causes inconvenience in the judgment.
そこで、前記■Ａ ｔ ＩＢ ｐ ＩＣ！に於て（ＩＡ
＋ＩＢ ） ／ ２−ＩＣがＩＡ−ＩＢに対して、一定
の比率以下になる事を利用し、例えば＠８図（α）に対
してＩＣ）”（ｌ（ｊ−１）” ＩＯ’＋ｘ）） ／
２− Ｉｔ＞） （ただし、Ｉ（７’）はＩσ＋の近傍
平均値とする）を演算して第８図（Ｃ１を得、所定の閾
値と比較して偽欠陥を含む欠陥検出画素の内、前記（■
ム＋ＩＢ） ／ ２− Ｉｏが所定の値以下となる画素
を選別してやれば、前記偽欠陥を除外する事が出来、安
定した欠陥検出と種類の判定を行なうことができる。Therefore, the above ■A t IB p IC! In (IA
+IB) / 2-Using the fact that IC is less than a certain ratio with respect to IA-IB, for example, IC)"(l(j-1)"IO'+x) for @8 diagram (α) ) /
2-It>) (where I(7') is the neighborhood average value of Iσ+) to obtain Figure 8 (C1), and compare it with a predetermined threshold to determine which of the defect detection pixels including the false defect. , Said (■
By selecting pixels whose Io is less than or equal to a predetermined value, the false defects can be excluded, and stable defect detection and type determination can be performed.
次に、以上の実施例により周期性パターンの欠陥検査を
実施した例を示すと、単位パターンの直径が１００μｍ
、配列ピッチが３００μ隅で第２図に示す様な配列をも
つ周期性パターンを第１図に示す様に透過光により照明
し、撮偉管を用いたＴＶ右カメラで３００ Ｘ ２２０
ｔｍの領域を撮影し、撮影時間及び画像処理装置の合
計が約５秒で単位パターンの開口径が５μ簿異なる欠陥
を自動的に検出し、周囲に対する開口の大小の判定を行
う事ができた。Next, an example of defect inspection of a periodic pattern according to the above embodiment is shown in which the diameter of the unit pattern is 100 μm.
A periodic pattern with an arrangement pitch of 300μ corners as shown in Fig. 2 was illuminated with transmitted light as shown in Fig. 1, and a TV right camera using a camera tube was used to capture the image at 300 x 220.
The area of tm was photographed, and within a total of approximately 5 seconds of photographing time and image processing equipment, defects in which the aperture diameter of the unit pattern differed by 5 μm were automatically detected, and it was possible to determine the size of the aperture relative to the surroundings. .
尚、本発明で用いるＴＶ撮影装置及び照明方法としては
、欠陥情報を含むビデオ信号が得られるものであればど
のようなものでも全て利用でき、例えばＴＶ撮影装置と
しては、撮儂管、固体撮壕素子を用いたＴＶカメラ、イ
メージディセクタやフライングスポット管を用いた撮影
装置、Ｘ＠ＴＶ撮影装置、電子顕微鏡撮偉装置などが使
用でき、また、照明方法としては、透過光照明、透過暗
視野照明、反射暗視野照明、正反射照明などが用いられ
、さらに照明光の性質としては、コヒーレンス、分光特
性、偏光など何れも検出しようとする欠陥による信号の
Ｓ／Ｎが高くなる様に、又は検出する必要のない欠陥に
よる信号レベルが低くなる様に選択していずれＫよって
も実施する事が出来る。Furthermore, as the TV photographing device and illumination method used in the present invention, any device can be used as long as it can obtain a video signal including defect information. TV cameras using trench elements, imaging equipment using image dissectors and flying spot tubes, X@TV imaging equipment, electron microscope imaging equipment, etc. can be used.The illumination methods include transmitted light illumination, transmitted darkness, etc. Field illumination, reflective dark field illumination, specular reflection illumination, etc. are used, and the illumination light properties include coherence, spectral characteristics, and polarization, all of which are designed to increase the S/N of the signal due to the defect to be detected. Alternatively, it can be carried out by selecting K so that the signal level due to defects that do not need to be detected is low.
以上説明した様に、本発明によれば、周期性パターンを
もつ種々の工業製品の微小な欠陥を、その周期性パター
ンを構成する単位パターンが解像されない様な広い撮影
視野で撮影して、その画像データを処理する事により、
自動的に検出し、種類（白点、黒点）の判定を行う事が
可能となり、検査精度、信頼性及び能率の向上などの効
果が得られる。As explained above, according to the present invention, minute defects in various industrial products having periodic patterns are photographed with a wide field of view such that unit patterns constituting the periodic pattern are not resolved. By processing the image data,
It becomes possible to automatically detect and determine the type (white dots, black dots), resulting in improvements in inspection accuracy, reliability, and efficiency.
第１図は本発明による周期性パターンの検査方法の一実
施例を示すブロック図、８２図は周期性パターンの一例
を示す説明図、第３図（α）〜の、第４図（α） 、
（Ａ） 、第５図（αｌ 、 （ｂｌ、第６図（αｌ
、 （ｈ）、第７図（５り 、 （ｂ）、それに第８図
（α）〜（Ｃ）はそれぞれ本発明の動作を示す説明図で
ある。
１・・・・・・ＴＶカメラ、２・・・・・・画像処理装
置、３・・・・・・制御部、４・・・・・・ＴＶモニタ
、５・・・・・・駆動機構、６・・・・・・被検査体、
７・・・・・・拡散板、８・・・・・・ランプ、９・・
・・・・直流電源、１０・・・・・・ステージ、１１・
・・・・・単位パターン、１２ 、１３・・・・・・欠
陥、１４ 、１５・・・・・・欠陥による透過率変化、
１６・・・・・・光学系のゴミ等による信号変化、１７
・・・・・・欠陥像、２７・・・・・・蘭値、２８・・
・・・・近傍平均値。
一′・″へ
＠１図
第２図
−〇 〇〇〇〇−一一
第３図
第４図 第５図
（０） ＣＱ）第６図
（０） （ｂ）第７図
第８図
りｆ
手続補正書（自発）
昭和６０年 ６月り≠日FIG. 1 is a block diagram showing an embodiment of the periodic pattern inspection method according to the present invention, FIG. 82 is an explanatory diagram showing an example of the periodic pattern, and FIGS. 3(α) to 4(α) ,
(A), Figure 5 (αl, (bl), Figure 6 (αl
, (h), FIG. 7 (b), and FIGS. 8 (α) to (C) are explanatory diagrams each showing the operation of the present invention. 1...TV camera, 2... Image processing device, 3... Control unit, 4... TV monitor, 5... Drive mechanism, 6... Test subject body,
7...Diffusion plate, 8...Lamp, 9...
...DC power supply, 10... Stage, 11.
... Unit pattern, 12 , 13 ... Defect, 14 , 15 ... Transmittance change due to defect,
16...Signal change due to dust etc. in optical system, 17
...Defect image, 27...Orchid value, 28...
...Neighborhood average value. To 1'/''@1 Figure 2-〇 〇〇〇-11 Figure 3 Figure 4 Figure 5 (0) CQ) Figure 6 (0) (b) Figure 7 Figure 8 f Procedural amendment (voluntary) June 1985≠date
Claims (11)
ーンの局部的な欠陥を検査する方法において、上記周期
性パターンを画素分解してフレーム単位の画像データを
得るための撮像手段と、上記周期性パターンを所定の方
向に所定の距離だけ変位させる手段とを設け、上記周期
性パターンの変位前の撮像による画撮データから変位後
の撮像による画像データを減算して得た画像データに基
づいて欠陥検査処理を行なうように構成したことを特徴
とする周期性パターンの欠陥検査方法。(1) A method for inspecting local defects in a periodic pattern consisting of a repeating arrangement of unit patterns, comprising: an imaging means for decomposing the periodic pattern into pixels to obtain image data in units of frames; means for displacing the periodic pattern by a predetermined distance in a predetermined direction, and defect inspection based on image data obtained by subtracting image data obtained by imaging after the periodic pattern is captured from image data obtained by imaging before the periodic pattern is displaced. 1. A periodic pattern defect inspection method, characterized in that the periodic pattern defect inspection method is configured to perform processing.
ーンは、その単位パターンの形状、大きさ、配列ピッチ
の少くとも一つが所定の割合で変化して配列されている
ことを特徴とする周期性パターンの欠陥検査方法。(2) In claim 1, the periodic pattern is characterized in that the periodic pattern is arranged such that at least one of the shape, size, and arrangement pitch of the unit pattern changes at a predetermined rate. Defect inspection method for periodic patterns.
周期性パターンの変位前の撮像による画像データと変位
後の撮像による画像データとが、共に複数回の撮像によ
る複数フレームにわたる画像データの各画素データごと
の加算による画像データであることを特徴とする周期性
パターンの検査方法。(3) In claim 1 or 2, the image data obtained by imaging before the periodic pattern is displaced and the image data obtained by imaging after the displacement are both image data spanning multiple frames obtained by imaging multiple times. A periodic pattern inspection method characterized in that the image data is obtained by adding each pixel data.
撮像手段による撮像条件が、撮像した画像データの各画
素ごとのデータに現われる上記単位パターンごとの変化
による影響が充分に少なく無視可能な状態となるように
定められ、上記所定の方向が上記画素の配列方向と一致
し、かつ上記所定の距離が上記画素ピッチの整数倍とな
るように構成されていることを特徴とする周期性パター
ンの欠陥検査方法。(4) In claim 1 or 2, the imaging conditions by the imaging means are such that the influence of changes in each unit pattern appearing in data for each pixel of the imaged image data is sufficiently small that it can be ignored. periodicity characterized by being configured such that the predetermined direction coincides with the arrangement direction of the pixels, and the predetermined distance is an integral multiple of the pixel pitch. Pattern defect inspection method.
所定の方向が単位パターンの配列方向と一致し、かつ上
記所定の距離が単位パターンの配列ピッチの整数倍とな
るように構成されていることを特徴とする周期性パター
ンの欠陥検査方法。(5) In claim 1 or 2, the predetermined direction is configured such that the predetermined direction coincides with the arrangement direction of the unit patterns, and the predetermined distance is an integral multiple of the arrangement pitch of the unit patterns. A periodic pattern defect inspection method characterized by:
所定の方向が単位パターンの配列方向と一致し、かつ上
記所定の距離が単位パターンの配列ピッチと上記画像デ
ータの画素ピッチの公倍数となるように構成されている
ことを特徴とする周期性パターンの欠陥検査方法。(6) In claim 1 or 2, the predetermined direction coincides with the arrangement direction of the unit patterns, and the predetermined distance is a common multiple of the arrangement pitch of the unit patterns and the pixel pitch of the image data. A periodic pattern defect inspection method characterized by being configured so that:
周期性パターンがストライプ状パターンで、上記所定の
方向がこのストライプ状パターンの各ストライプの配列
方向であり、かつ上記所定の距離が上記画像データの画
素ピッチの整数倍となるように構成されていることを特
徴とする周期性パターンの欠陥検査方法。(7) In claim 1 or 2, the periodic pattern is a striped pattern, the predetermined direction is the arrangement direction of each stripe of the striped pattern, and the predetermined distance is A periodic pattern defect inspection method, characterized in that the periodic pattern is configured to have a pixel pitch that is an integral multiple of the pixel pitch of the image data.
周期性パターンが同心円状パターンで、上記所定の方向
がこの同心円状パターンの中心を回転軸とした回転移動
方向となるように構成されていることを特徴とする周期
性パターンの欠陥検査方法。(8) In claim 1 or 2, the periodic pattern is a concentric pattern, and the predetermined direction is a rotational movement direction with the center of the concentric pattern as a rotation axis. A periodic pattern defect inspection method characterized by:
周期性パターンが、単位パターンを円周上に配列したパ
ターンで、上記所定の方向がこの単位パターンの配列方
向と一致した円周方向であり、かつ上記所定の距離が上
記単位パターンの円周上での配列ピッチの整数倍となる
ように構成されていることを特徴とする周期性パターン
の欠陥検査方法。(9) In claim 1 or 2, the periodic pattern is a pattern in which unit patterns are arranged on a circumference, and the predetermined direction coincides with the arrangement direction of the unit patterns. 1. A method for inspecting defects in periodic patterns, characterized in that the predetermined distance is an integral multiple of an array pitch on a circumference of the unit pattern.
記欠陥検出処理が、最大で３×３画素の近傍画素データ
の加算処理を含むように構成されていることを特徴とす
る周期性パターンの検査方法。(10) The periodicity according to claim 1 or 2, wherein the defect detection process is configured to include an addition process of neighboring pixel data of up to 3 x 3 pixels. How to inspect patterns.
処理が、上記減算して得た画像データに対して、上記周
期性パターン上の一点が画面加算時に対応する画素をＡ
、画面減算時に対応する画素をＢ、それに画素Ａ、Ｂの
中点に対応する画素をＣとしたときに、この画素Ｃの近
傍平均値と、画素ＡとＢのデータの平均値の差が画素Ａ
とＢのデータの差に比して充分に小さく、かつ画素Ａと
Ｂのデータの差が所定のレベル以上である点を欠陥と判
断し、画素ＡとＢのデータの差の符号と絶対値により欠
陥の種類と大きさとを認識するように構成されているこ
とを特徴とする周期性パターンの検査方法。(11) In claim 3, the defect detection process detects, with respect to the image data obtained by the subtraction, a pixel corresponding to one point on the periodic pattern during screen addition.
, when the corresponding pixel during screen subtraction is B, and the pixel corresponding to the midpoint of pixels A and B is C, the difference between the neighborhood average value of this pixel C and the average value of the data of pixels A and B is Pixel A
A point that is sufficiently small compared to the difference between the data of pixels A and B, and the difference between the data of pixels A and B is at least a predetermined level is determined to be a defect, and the sign and absolute value of the difference between the data of pixels A and B are determined. A method for inspecting a periodic pattern, characterized in that the method is configured to recognize the type and size of a defect.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP9659385A JPH0713598B2 (en) | 1985-05-09 | 1985-05-09 | Defect inspection method for periodic patterns |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP9659385A JPH0713598B2 (en) | 1985-05-09 | 1985-05-09 | Defect inspection method for periodic patterns |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| JPS61256237A true JPS61256237A (en) | 1986-11-13 |
| JPH0713598B2 JPH0713598B2 (en) | 1995-02-15 |
Family
ID=14169198
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP9659385A Expired - Lifetime JPH0713598B2 (en) | 1985-05-09 | 1985-05-09 | Defect inspection method for periodic patterns |
Country Status (1)
| Country | Link |
|---|---|
| JP (1) | JPH0713598B2 (en) |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS63198854A (en) * | 1987-02-12 | 1988-08-17 | Nippon Sheet Glass Co Ltd | Foreign matter detecting device for template glass |
| JPS6491076A (en) * | 1987-10-02 | 1989-04-10 | Hitachi Ltd | Measuring apparatus for magnetic recording medium |
| JPH01307644A (en) * | 1988-06-03 | 1989-12-12 | Dainippon Printing Co Ltd | Defect detecting method |
| JPH01307647A (en) * | 1988-06-03 | 1989-12-12 | Dainippon Printing Co Ltd | Inspecting method for periodic pattern |
| JPH01307646A (en) * | 1988-06-03 | 1989-12-12 | Dainippon Printing Co Ltd | Inspecting method for periodic pattern |
| JPH01313745A (en) * | 1988-06-13 | 1989-12-19 | Dainippon Printing Co Ltd | Inspecting method for colored periodic pattern |
| JPH02335A (en) * | 1987-11-21 | 1990-01-05 | Dainippon Printing Co Ltd | Defect inspection equipment for surfacing pattern |
| JPH03140848A (en) * | 1989-10-27 | 1991-06-14 | Kubota Corp | Method for detecting flaw |
| JPH04148854A (en) * | 1990-10-12 | 1992-05-21 | Mitsubishi Electric Corp | Surface-defect detecting apparatus |
| JPH04166751A (en) * | 1990-10-31 | 1992-06-12 | Toyo Glass Co Ltd | Method and apparatus for inspecting defect in bottle and the like |
| WO2001073411A1 (en) * | 2000-03-29 | 2001-10-04 | Seiko Epson Corporation | Through hole inspecting method and device |
| WO2001084127A1 (en) * | 2000-04-27 | 2001-11-08 | Seiko Epson Corporation | Method and device for detecting foreign matter in through hole |
| WO2001084128A1 (en) * | 2000-04-27 | 2001-11-08 | Seiko Epson Corporation | Inspection method for foreign matters inside through hole |
| JP2013213676A (en) * | 2012-03-30 | 2013-10-17 | Dainippon Printing Co Ltd | Image processing device, image processing program, and image processing method |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101230203B1 (en) * | 2010-12-28 | 2013-03-18 | 유진인스텍 주식회사 | Detection device of surface defect of wafer polishing head |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS5497085A (en) * | 1978-01-17 | 1979-07-31 | Nippon Steel Corp | Signal processing method of optical detector for the surface flaw of steel band |
| JPS55166839A (en) * | 1979-03-19 | 1980-12-26 | Rca Corp | System for inspecting fault of regular pattern |
| JPS59192943A (en) * | 1983-04-15 | 1984-11-01 | Hitachi Ltd | Defect inspecting device repetitive pattern |
| JPS6176941A (en) * | 1984-09-21 | 1986-04-19 | Nippon Denso Co Ltd | Method and device for inspecting appearance failure of screw |
-
1985
- 1985-05-09 JP JP9659385A patent/JPH0713598B2/en not_active Expired - Lifetime
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS5497085A (en) * | 1978-01-17 | 1979-07-31 | Nippon Steel Corp | Signal processing method of optical detector for the surface flaw of steel band |
| JPS55166839A (en) * | 1979-03-19 | 1980-12-26 | Rca Corp | System for inspecting fault of regular pattern |
| JPS59192943A (en) * | 1983-04-15 | 1984-11-01 | Hitachi Ltd | Defect inspecting device repetitive pattern |
| JPS6176941A (en) * | 1984-09-21 | 1986-04-19 | Nippon Denso Co Ltd | Method and device for inspecting appearance failure of screw |
Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS63198854A (en) * | 1987-02-12 | 1988-08-17 | Nippon Sheet Glass Co Ltd | Foreign matter detecting device for template glass |
| JPS6491076A (en) * | 1987-10-02 | 1989-04-10 | Hitachi Ltd | Measuring apparatus for magnetic recording medium |
| JPH02335A (en) * | 1987-11-21 | 1990-01-05 | Dainippon Printing Co Ltd | Defect inspection equipment for surfacing pattern |
| JPH01307644A (en) * | 1988-06-03 | 1989-12-12 | Dainippon Printing Co Ltd | Defect detecting method |
| JPH01307647A (en) * | 1988-06-03 | 1989-12-12 | Dainippon Printing Co Ltd | Inspecting method for periodic pattern |
| JPH01307646A (en) * | 1988-06-03 | 1989-12-12 | Dainippon Printing Co Ltd | Inspecting method for periodic pattern |
| JPH01313745A (en) * | 1988-06-13 | 1989-12-19 | Dainippon Printing Co Ltd | Inspecting method for colored periodic pattern |
| JPH03140848A (en) * | 1989-10-27 | 1991-06-14 | Kubota Corp | Method for detecting flaw |
| JPH04148854A (en) * | 1990-10-12 | 1992-05-21 | Mitsubishi Electric Corp | Surface-defect detecting apparatus |
| JPH04166751A (en) * | 1990-10-31 | 1992-06-12 | Toyo Glass Co Ltd | Method and apparatus for inspecting defect in bottle and the like |
| WO2001073411A1 (en) * | 2000-03-29 | 2001-10-04 | Seiko Epson Corporation | Through hole inspecting method and device |
| WO2001084127A1 (en) * | 2000-04-27 | 2001-11-08 | Seiko Epson Corporation | Method and device for detecting foreign matter in through hole |
| WO2001084128A1 (en) * | 2000-04-27 | 2001-11-08 | Seiko Epson Corporation | Inspection method for foreign matters inside through hole |
| US6906795B2 (en) | 2000-04-27 | 2005-06-14 | Seiko Epson Corporation | Method and apparatus for examining foreign matters in through holes |
| CN1302280C (en) * | 2000-04-27 | 2007-02-28 | 精工爱普生株式会社 | Inspection method for foreign matters inside through hole |
| CN100387975C (en) * | 2000-04-27 | 2008-05-14 | 精工爱普生株式会社 | Method and device for detecting foreign matter in through hole |
| JP2013213676A (en) * | 2012-03-30 | 2013-10-17 | Dainippon Printing Co Ltd | Image processing device, image processing program, and image processing method |
Also Published As
| Publication number | Publication date |
|---|---|
| JPH0713598B2 (en) | 1995-02-15 |
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