JP3635762B2 - Inspection method of semiconductor substrate surface defects - Google Patents
Inspection method of semiconductor substrate surface defects Download PDFInfo
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- JP3635762B2 JP3635762B2 JP02324896A JP2324896A JP3635762B2 JP 3635762 B2 JP3635762 B2 JP 3635762B2 JP 02324896 A JP02324896 A JP 02324896A JP 2324896 A JP2324896 A JP 2324896A JP 3635762 B2 JP3635762 B2 JP 3635762B2
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Description
【0001】
【発明の属する技術分野】
この発明は、LSI等の基板として用いられる半導体基板の特性を劣化させる表面欠陥を自動検査する検査方法に係り、新たなフローパターンの認識アルゴリズムにより、フローパターン密度とスモールピット密度を個別に認識、測定し、基板面内全体にわたって表面欠陥の分別評価やその密度分布の測定を可能にした半導体基板表面欠陥の検査方法に関する。
【0002】
【従来の技術】
各種デバイスの基板として用いられる半導体基板、例えば、シリコンウェーハの表面をas−grown状態で欠陥選択エッチングすると、表面欠陥が顕在化される。従来は、光学顕微鏡を用いた目視検査によって各種表面欠陥の密度計測が行われてきた。
【0003】
例えば、表1に示すごとく、観察される欠陥には種々の特徴がある。しかし、従来、ウェーハ全面にわたって詳細な表面欠陥密度分布を計測することは非常に困難であり、その結果、重要な情報が見落とされていた。
【0004】
【表1】
【0005】
【発明が解決しようとする課題】
表面欠陥はデバイス特性を劣化させるが、各種表面欠陥がウェーハ特性に与える影響は、現在のところ明確ではない。そのため、単純な表面欠陥だけでなく、フローパターンのような複雑な形状の表面欠陥に対しても、分別評価や密度分布の測定をウェーハ面内にわたって、詳細に行える画像処理装置が望まれている。
【0006】
最近、欠陥の自動計測や特徴量の抽出を定量的に行う方法が提案されてきている。例えば、カメラから入力された検査画像と参照画像との間で差演算を行って、画像拡大処理および濃淡変換処理の後に疑似欠陥を排除して、真の欠陥のみを計測する方法(特許開4−16752号)が提案されている。ところが、欠陥の分別評価を行っていないという欠点がある。
【0007】
また、2値化された欠陥像について個々の面積、凹凸を特徴量として抽出し、基準となる画像と比較して、欠陥数を計測する方法(特許開2−177548号)が提案されているが、欠陥の特徴量の抽出は行えるものの単純なピットのみに限られるといった欠点があった。
【0008】
この発明は、光学顕微鏡を用いた各種表面欠陥の密度計測において、表面欠陥密度の面内分布測定を可能となし、また、画像より面積と形状を測定して、表面欠陥の分別評価を可能とする半導体基板表面欠陥の検査方法の提供を目的としている。
【0009】
【課題を解決するための手段】
発明者は、光学顕微鏡とCCDカメラを用いて撮影した被測定半導体基板の測定用画像を2値化した画像より、欠陥像の面積及び形状を抽出して欠陥数を計測する表面欠陥の検査方法において、表面欠陥の分別評価が可能な方法を目的に種々検討した結果、読み込みして処理した2値化画像より微小粒子画像をノイズとして除去した計測用画面を作成し、これを特定方法でスキャンすると、本来のフローパターンの場合には、増加した後パターン長さの減少はあまり見られないが、付随パターンの場合には増加後にパターン長さの大きな減少が見られることから、正確なフローパターンの認識、計測が可能なことを知見し、さらに、フローパターン密度とスモールピット密度との間には違いがあることを知見し、この発明を完成した。
【0010】
この発明は、表面欠陥を顕在化させた被測定半導体基板を光学顕微鏡とCCDカメラを用いて撮影した測定画像をコンピュータにて画像処理することにより、上記基板表面の欠陥数を計測する半導体基板表面欠陥の検査方法である。
その特徴ある点は、上記コンピュータによる画像処理が、(1)表面欠陥を含む前記測定画像から背景画像を除去するステップと、(2)背景画像を除去した前記測定画像を強調処理するステップと、(3)強調処理された前記測定画像を2値化処理するステップと、(4)2値化処理された前記測定画像から所定のサイズ以下の微小粒子画像をノイズとして除去して計測用画面とするステップとを含み、前記欠陥数の計測が、(5)前記ステップ(4)で得られた計測用画面を所定の横長測定枠で下から上方向にスキャニングして測定枠内に入ったパターンの長さを計測し、前記パターンの長さに基づいてフローパターンと付随パターンを分別することによりフローパターンを測定するステップと、(6)前記ステップ(4)で得られた計測用画面において欠陥の特徴量計測手法を用いてスモールピットを測定するステップとからなることにある。
【0011】
【発明の実施の形態】
この発明による半導体基板表面欠陥の自動検査装置の概略図を図1に示す。この自動検査装置は、平面2軸方向に移動可能にしたXYステージに被測定半導体基板を載置し、自動焦点装置並びにCCDカメラを有する半導体基板検査用光学顕微鏡にて、被測定半導体基板を観察する。XYステージ並びに自動焦点装置はホストコンピュータ(ホストPC)にて制御され、CCDカメラからの画像入力信号は画像処理用コンピュータで処理されて欠陥の抽出と面積、密度を求められ、その計測データー等はホストコンピュータでデーター処理され、画像データーは画像記憶装置に記録される構成からなる。
【0012】
画像処理による検査方法のフローチャートを図2に示す。CCDカメラからの測定画像は背景画像を除いた後、輪郭強調処理され、強調処理された画像に対して2値化処理を行う。続いて、微小粒子をノイズとして除去する。この段階で、表1に示す表面欠陥の混在した画像が得られるが、両欠陥の認識アルゴリズムが異なるため分離して考える。
【0013】
詳述すると、まず、全く欠陥のない背景画像(B)を例えば256階調のグレースケールで取り込み、同様に欠陥を含む測定画像(I)を取り込み、次いで画像の差演算処理(I−B)を行い、欠陥測定画像からほぼ背景を除去する。続いて、輪郭強調処理する。これは、強調処理を行わないときは、背景を除去した欠陥測定画像と画面上の画像の明るさは比例関係にあるものを、元の画像の暗いところはさらに暗く、明るいところはさらに明るくする強調処理によって、差演算処理された画像のコントラストをより鮮明にする。
【0014】
取り込み後に背景除去して強調処理を行った画像は、取込み時の階調のままであるが、この画像に対して、例えば20の明るさを閾値として2値化処理を行う。この2値化処理においてほぼ欠陥のみが分離された画像が得られるが、閾値より僅かに明るい明るさをもった背景がノイズとして2値化画像に取り込まれてしまう。これらは本来の欠陥と比べて明らかに小さいので、ある閾値以下のサイズの微小粒子をノイズとして除去することによって、欠陥のみの2値化像を得ることができる。
【0015】
フローパターンは、図3に示すように本来のフローパターンとは異なる、付随パターンを伴うことがあるため、それらの分離を行って計測する必要がある。そこで、この発明では新たにフローパターンの認識アルゴリズムを考案した。フローパターン認識アルゴリズムを図4に示す。このアルゴリズムでは、2値化画像の中で横長の窓、すなわち測定画面一杯の横幅で縦長さを測定時間とフローパターン寸法とを考慮した画面高さの数十分の1の長さに設定した横長測定枠を用いて画面を下から上へとスキャンさせる。そのとき、枠内に入ったパターンの長さを計測する。
【0016】
図3に見られるフローパターンを測定したときの結果を図5に示す。本来のフローパターンの場合には、増加した後パターン長さの減少はあまり見られないが、付随パターンの場合には増加後にパターン長さの大きな減少が見られるという特徴がある。両者の間にはこの様な顕著な違いが見られるため、減少の度合いの閾値を設定することによって、図示のごとく分別を行うことができる。
【0017】
一方、スモールピットについては、画像処理コンピュータにより以下の欠陥の特徴量計測手法を用いて、面積、形状の測定を行った。すなわち、画像において、各々のスモールピットは多数のピクセルの塊として認識されており、面積とはピットを構成するピクセル数、形状は例えば凹凸の度合いというパラメーターを用いてピットの面積と形状の測定を行う。これはビットの外周長と面積からなるパラメータでピットが円形になる程1に近づき、円形からずれるに従って大きくなる。各種欠陥でこのパラメータは大きく異なるため、欠陥の分離を行うことができる。
【0018】
【実施例】
<実施例1>
試料に引き上げ速度1.1mm/minのas−grownウェーハを用い、このウェーハを15分間、およそ10μm、欠陥選択エッチングしてas−grown状態での表面欠陥を顕在化させた後、図1の自動検査装置を用いて図2及び図4に示すフローでこの発明による検査を実施した。
【0019】
図6にフローパターン密度、図7にスモールピット密度の面内密度分布を示す。今回の測定は3mm間隔で行った。なお、図6、図7は本来、コンピューターに接続されたカラープリンターにて出力されたカラー画像であるが、10色の区分を10種の領域表示に変換図示してある。この発明による自動検査装置並びに検査方法によって、1.1mm/minウェーハではフローパターン密度とスモールピット密度との間には違いがあることが明確になった。
【0020】
従来の目視検査は、図8に示すような、1cm間隔の測定でしかも直径方向の1方向計測で行われることが多かった。そのため、欠陥の面内分布の測定は不可能であった。ところがこの発明により、従来法では不可能であった、表面欠陥の分別評価が可能になったといえる。
【0021】
【発明の効果】
この発明による半導体基板表面欠陥の検査方法は、2値化された画像より欠陥像の面積及び形状を抽出して欠陥数を計測する検査方法において、新たなフローパターンの認識アルゴリズムにより、フローパターン密度とスモールピット密度を個別に認識、測定し、基板面内全体にわたって表面欠陥の分別評価やその密度分布の測定を可能にしたことよって、従来不可能であったシリコンウェーハ表面欠陥の分別評価が可能となり、LSI等の基板として用いられるシリコンウェーハの高品質化に寄与できる。
【図面の簡単な説明】
【図1】この発明による半導体基板表面欠陥の自動検査装置の構成を示す概略図である。
【図2】この発明による半導体基板表面欠陥の検査方法のステップを示すフローチャート図である。
【図3】この発明方法によりフローパターンを測定したときの計測用画面を示す説明図である。
【図4】この発明方法によるフローパターンの認識アルゴリズムを示す説明図である。
【図5】フローパターン長さと位置との関係を示すグラフである。
【図6】Aはこの発明方法によりコンピューターで出力したフローパターン密度分布を示す説明図であり、Bは測定位置を示す説明図である。
【図7】Aはこの発明方法によりコンピューターで出力したスモールピット密度の面内密度分布を示す説明図であり、Bは測定位置を示す説明図である。
【図8】従来方法の測定結果を示す、ウェーハ中心からの距離と欠陥数との関係を示すグラフである。[0001]
BACKGROUND OF THE INVENTION
This invention relates to surface defects that degrade the characteristics of the semiconductor substrate used as a substrate such as an LSI to test how the automatic inspection, the recognition algorithm of the new flow pattern, individually recognized the flow pattern density and small pit density , measured relates to the inspection how the semiconductor substrate surface defects that enables measurement of the separation evaluation and its density distribution of surface defects across the substrate surface.
[0002]
[Prior art]
When defects are selectively etched in the as-grown state of a semiconductor substrate used as a substrate for various devices, for example, the surface of a silicon wafer, surface defects become apparent. Conventionally, density measurement of various surface defects has been performed by visual inspection using an optical microscope.
[0003]
For example, as shown in Table 1, the observed defects have various characteristics. Conventionally, however, it has been very difficult to measure a detailed surface defect density distribution over the entire wafer surface, and as a result, important information has been overlooked.
[0004]
[Table 1]
[0005]
[Problems to be solved by the invention]
Although surface defects degrade device characteristics, the impact of various surface defects on wafer characteristics is currently unclear. Therefore, there is a demand for an image processing apparatus capable of performing detailed evaluation and measurement of density distribution over the wafer surface not only for simple surface defects but also for surface defects with complicated shapes such as flow patterns. .
[0006]
Recently, methods have been proposed for automatically measuring defects and extracting feature quantities quantitatively. For example, a method of calculating only a true defect by performing a difference operation between an inspection image input from a camera and a reference image, eliminating pseudo defects after image enlargement processing and light / dark conversion processing (Patent Opening 4) -16752) has been proposed. However, there is a drawback in that defect evaluation is not performed.
[0007]
In addition, a method has been proposed in which individual areas and irregularities are extracted as feature quantities from a binarized defect image and the number of defects is measured by comparison with a reference image (Japanese Patent No. 2-177548). However, the defect feature amount can be extracted, but it is limited to only a simple pit.
[0008]
In this invention, in the density measurement of various surface defects using an optical microscope, in-plane distribution measurement of the surface defect density is possible, and the area and shape are measured from the image, and the surface defect can be separately evaluated. An object of the present invention is to provide a method for inspecting semiconductor substrate surface defects .
[0009]
[Means for Solving the Problems]
The inventor extracts a defect image area and shape from an image obtained by binarizing a measurement image of a semiconductor substrate to be measured, which is taken using an optical microscope and a CCD camera, and measures the number of defects by extracting the area and shape of the defect image As a result of various studies for the purpose of the method that enables the evaluation of surface defects, we created a measurement screen that removes the fine particle image as noise from the binarized image that was read and processed, and scanned it with a specific method. Then, in the case of the original flow pattern, there is not much decrease in the pattern length after the increase, but in the case of the accompanying pattern, a large decrease in the pattern length is observed after the increase, so an accurate flow pattern The present invention was completed by knowing that it is possible to recognize and measure the difference between the flow pattern density and the small pit density.
[0010]
The present invention, by image processing the measurement images taken with a surface defect optical microscope and a CCD camera to be measured semiconductor substrate obtained by actualized by a computer, semiconductor base plate for measuring the number of defects in the substrate surface a detection 査方 method table surface defects.
Its distinctive point, the image processing by the computer, comprising the steps of: emphasizing process removing the background image from the measurement image including the (1) surface defects, the measurement image obtained by removing (2) the background image, (3) a step of binarizing the enhanced measurement image ; and (4) a measurement screen by removing, as noise, a fine particle image having a predetermined size or less from the binarized measurement image. And (5) a pattern in which the measurement screen obtained in step (4) is scanned from the bottom to the top in a predetermined horizontally long measurement frame and entered into the measurement frame. the length is measured in the steps of measuring more flow pattern to separate the accompanying pattern and the flow pattern based on the length of the pattern, obtained in (6) step (4) And measuring a small pit using a defect feature amount measurement technique on the measurement screen .
[0011]
DETAILED DESCRIPTION OF THE INVENTION
FIG. 1 shows a schematic view of a semiconductor substrate surface defect automatic inspection apparatus according to the present invention. In this automatic inspection apparatus, a semiconductor substrate to be measured is placed on an XY stage that is movable in two planes on a plane, and the semiconductor substrate to be measured is observed with an optical microscope for semiconductor substrate inspection having an automatic focusing device and a CCD camera. To do. The XY stage and the autofocus device are controlled by a host computer (host PC), and the image input signal from the CCD camera is processed by an image processing computer to obtain defect extraction, area, and density. Data processing is performed by a host computer, and image data is recorded in an image storage device.
[0012]
A flowchart of the inspection method based on image processing is shown in FIG. The measurement image from the CCD camera is subjected to edge enhancement processing after removing the background image, and binarization processing is performed on the enhanced image. Subsequently, the fine particles are removed as noise. At this stage, an image in which surface defects are mixed as shown in Table 1 is obtained. However, since the recognition algorithms for both defects are different, they are considered separately.
[0013]
More specifically, first, a background image (B) having no defect is captured in, for example, a gray scale of 256 gradations, similarly, a measurement image (I) including a defect is captured, and then an image difference calculation process (IB). To remove substantially the background from the defect measurement image. Subsequently, contour enhancement processing is performed. This means that when the emphasis process is not performed, the defect measurement image with the background removed and the brightness of the image on the screen are in a proportional relationship, with the darker part of the original image being darker and the brighter part being brighter. The contrast of the image subjected to the difference calculation process is made clearer by the enhancement process.
[0014]
An image that has been subjected to enhancement processing by removing the background after capture remains in the gradation at the time of capture, but binarization processing is performed on this image, for example, with a brightness of 20 as a threshold value. In this binarization process, an image in which only defects are substantially separated is obtained, but a background having a brightness slightly brighter than the threshold is taken into the binarized image as noise. Since these are clearly smaller than the original defect, a binary image of only the defect can be obtained by removing fine particles having a size equal to or smaller than a certain threshold value as noise.
[0015]
Since the flow pattern may be accompanied by an accompanying pattern that is different from the original flow pattern as shown in FIG. 3, it is necessary to measure them by separating them. Therefore, a new flow pattern recognition algorithm has been devised in the present invention. A flow pattern recognition algorithm is shown in FIG. In this algorithm, a horizontal window in the binarized image, that is, the horizontal width of the full measurement screen is set to a length of one tenth of the screen height considering the measurement time and the flow pattern size. The screen is scanned from the bottom to the top using the horizontal measurement frame. At that time, the length of the pattern in the frame is measured.
[0016]
FIG. 5 shows the result when the flow pattern shown in FIG. 3 is measured. In the case of the original flow pattern, the decrease in the pattern length is not so much observed after the increase, but in the case of the accompanying pattern, the pattern length is greatly decreased after the increase. Since such a remarkable difference is seen between the two, by setting a threshold value for the degree of decrease, the separation can be performed as shown in the figure.
[0017]
On the other hand, for the small pit, the area and shape were measured by an image processing computer using the following defect feature amount measurement method. That is, in the image, each small pit is recognized as a cluster of many pixels, and the area is the number of pixels constituting the pit, and the shape is a parameter such as the degree of unevenness, for example, to measure the area and shape of the pit. Do. This is a parameter consisting of the outer circumference length and area of the bit, and approaches 1 as the pit becomes circular, and increases as it deviates from the circle. Since this parameter varies greatly for various defects, it is possible to perform defect separation.
[0018]
【Example】
<Example 1>
As a sample, an as-grown wafer with a pulling speed of 1.1 mm / min was used, and this wafer was subjected to defect selective etching for about 15 μm for about 15 minutes to reveal surface defects in the as-grown state. The inspection according to the present invention was carried out by the flow shown in FIGS. 2 and 4 using the inspection apparatus.
[0019]
FIG. 6 shows the flow pattern density, and FIG. 7 shows the in-plane density distribution of the small pit density. This measurement was performed at intervals of 3 mm. 6 and 7 are originally color images output by a color printer connected to a computer, but 10 color sections are converted into 10 types of area displays. With the automatic inspection apparatus and inspection method according to the present invention, it has become clear that there is a difference between the flow pattern density and the small pit density in the 1.1 mm / min wafer.
[0020]
Conventional visual inspection is often performed by measuring at intervals of 1 cm as shown in FIG. 8 and by measuring in one direction in the diameter direction. Therefore, it was impossible to measure the in-plane distribution of defects. However, it can be said that the present invention has made it possible to evaluate the surface defects, which was impossible with the conventional method.
[0021]
【The invention's effect】
Checking how the semiconductor substrate surface defects according to the invention, in the inspection method of measuring the number of defects by extracting the area and shape of the defect image from the binarized image, the recognition algorithm of the new flow pattern, the flow pattern Recognize and measure the density and small pit density individually, and enable the evaluation of the surface defect classification and the distribution of its density over the entire substrate surface. It becomes possible, and it can contribute to quality improvement of a silicon wafer used as a substrate such as an LSI.
[Brief description of the drawings]
FIG. 1 is a schematic view showing the configuration of an automatic inspection apparatus for semiconductor substrate surface defects according to the present invention;
FIG. 2 is a flowchart showing steps of a method for inspecting a semiconductor substrate surface defect according to the present invention.
FIG. 3 is an explanatory diagram showing a measurement screen when a flow pattern is measured by the method of the present invention.
FIG. 4 is an explanatory diagram showing a flow pattern recognition algorithm according to the method of the present invention;
FIG. 5 is a graph showing a relationship between a flow pattern length and a position.
6A is an explanatory diagram showing a flow pattern density distribution output by a computer according to the method of the present invention, and B is an explanatory diagram showing a measurement position. FIG.
7A is an explanatory diagram showing an in-plane density distribution of small pit density output by a computer according to the method of the present invention, and B is an explanatory diagram showing a measurement position. FIG.
FIG. 8 is a graph showing the relationship between the distance from the wafer center and the number of defects, showing the measurement results of the conventional method.
Claims (1)
前記コンピュータによる画像処理が、
(1)表面欠陥を含む前記測定画像から背景画像を除去するステップと、
(2)背景画像を除去した前記測定画像を強調処理するステップと、
(3)強調処理された前記測定画像を2値化処理するステップと、
(4)2値化処理された前記測定画像から所定のサイズ以下の微小粒子画像をノイズとして除去して計測用画面とするステップと
を含み、
前記欠陥数の計測が、
(5)前記ステップ(4)で得られた計測用画面を所定の横長測定枠で下から上方向にスキャニングして測定枠内に入ったパターンの長さを計測し、前記パターンの長さに基づいてフローパターンと付随パターンを分別することによりフローパターンを測定するステップと、
(6)前記ステップ(4)で得られた計測用画面において欠陥の特徴量計測手法を用いてスモールピットを測定するステップと
からなることを特徴とする半導体基板表面欠陥の検査方法。 By image processing of the measurement image of the measurement semiconductor substrate were taken using an optical microscope and a CCD camera surface defects were manifested by a computer, semiconductor board table surface defects which measures the number of defects in the substrate surface in the test 査方 method,
Image processing by the computer
Removing the background image from (1) the measurement image containing surface defects,
(2) a step of enhancing the measurement image from which the background image has been removed ;
(3) a step of binarizing the measurement image that has been enhanced ;
(4) a step of removing a fine particle image having a predetermined size or less as noise from the binarized measurement image to obtain a measurement screen ;
Including
Measurement of the number of defects
(5) The measurement screen obtained in the step (4) is scanned from the bottom to the top in a predetermined horizontally long measurement frame to measure the length of the pattern that has entered the measurement frame. measuring a more flow pattern to separate the flow pattern with the accompanying pattern based,
(6) a step of measuring a small pit using a defect feature amount measurement method on the measurement screen obtained in step (4) ;
A method for inspecting a surface defect of a semiconductor substrate, comprising:
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KR100363707B1 (en) * | 1999-11-02 | 2002-12-05 | 이환용 | Semiconductor Element Inspection Method |
JP4031608B2 (en) * | 2000-04-18 | 2008-01-09 | 三菱重工業株式会社 | Material life evaluation method |
KR100445457B1 (en) | 2002-02-25 | 2004-08-21 | 삼성전자주식회사 | Apparatus and method for inspecting wafer backside |
JP4758492B2 (en) | 2009-03-24 | 2011-08-31 | トヨタ自動車株式会社 | Single crystal defect density measurement method |
US9846929B2 (en) | 2016-03-24 | 2017-12-19 | Hong Kong Applied Science and Technology Research Institute Company Limited | Fast density estimation method for defect inspection application |
CN109378277B (en) * | 2018-09-25 | 2020-11-24 | 武汉华星光电半导体显示技术有限公司 | Integrity detection method for substrate pattern array |
CN112525102B (en) * | 2020-12-09 | 2021-07-23 | 吉林大学 | Noise simulator of automobile morphology measurement system based on electrorheological fluid |
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