JP7257511B2 - 画像フレームに基づくアルゴリズムセレクタ - Google Patents
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- G01N2021/889—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges based on image processing techniques providing a bare video image, i.e. without visual measurement aids
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Description
本出願は、2018年10月26日に出願され、米国出願第62/751,077号に割当てられた米国仮特許出願の優先権を主張するものであり、その開示は参照により本明細書に組み込まれる。
Claims (20)
- 光ビームをウェーハに向けるように構成される光源と、
前記ウェーハから反射された前記光ビームを集める検出器と、
前記検出器と電子通信するプロセッサであって、
複数の光モードについてウェーハ上の検出したい欠陥及びニューサンス事象の画像フレームを受信し、
複数の検出アルゴリズムを使用して前記複数の光モードの差分画像及び属性を決定し、
前記複数の光モードのそれぞれの組み合わせを前記複数の検出アルゴリズムのそれぞれと、ゼロのオフセットで検出されるすべての前記検出したい欠陥について比較し、前記ゼロのオフセットでは、前記検出アルゴリズムが実行されて、可能な限り多くの欠陥が検出され、
前記組み合わせのそれぞれにおいて前記属性のうちの1つについて捕捉率対ニューサンス率を決定する、ように構成されるプロセッサと、を備える、
システム。 - 前記プロセッサは、前記検出したい欠陥と前記ニューサンス事象を識別するように更に構成される、請求項1に記載のシステム。
- 前記プロセッサは、前記比較から閾値領域内の前記検出したい欠陥のうちの少なくとも1つをフィルタリングするように更に構成される、請求項1に記載のシステム。
- 前記プロセッサは、前記比較から属性値を超える前記検出したい欠陥のうちの少なくとも1つをフィルタリングするように更に構成される、請求項1に記載のシステム。
- 前記プロセッサは、前記組み合わせを前記属性によって分類するように更に構成される、請求項1に記載のシステム。
- 前記プロセッサは、選択されたニューサンス率又は選択された検出したい欠陥の捕捉率について、前記光モードの1つと前記検出アルゴリズムの1つとの最適な組み合わせを選択するように更に構成される、請求項1に記載のシステム。
- 前記最適な組み合わせは受信動作曲線プロットを用いて選択される、請求項6に記載のシステム。
- プロセッサで、複数の光モードについてウェーハ上の検出したい欠陥及びニューサンス事象の画像フレームを受信し、
前記プロセッサを用いて、複数の検出アルゴリズムを使用して前記複数の光モードの差分画像及び属性を決定し、
前記プロセッサを用いて、前記複数の光モードのそれぞれの組み合わせを前記複数の検出アルゴリズムのそれぞれと、ゼロのオフセットで検出されるすべての前記検出したい欠陥について比較し、前記ゼロのオフセットでは、前記検出アルゴリズムが実行されて、可能な限り多くの欠陥が検出され、
前記プロセッサを用いて、前記組み合わせのそれぞれにおいて前記属性のうちの1つについて捕捉率対ニューサンス率を決定する、ことを含む、
方法。 - 前記検出したい欠陥と前記ニューサンス事象を、前記プロセッサを使用して識別することを更に含む、請求項8に記載の方法。
- 前記プロセッサを使用して、前記比較から閾値領域内の前記検出したい欠陥のうちの少なくとも1つをフィルタリングすることを更に含む、請求項8に記載の方法。
- 前記プロセッサを使用して、前記比較から属性値を超える前記検出したい欠陥のうちの少なくとも1つをフィルタリングすることを更に含む、請求項8に記載の方法。
- 前記プロセッサを使用して、前記組み合わせを前記属性によって分類することを更に含む、請求項8に記載の方法。
- 前記プロセッサを使用して、選択されたニューサンス率又は選択された検出したい欠陥の捕捉率について、前記光モードの1つと前記検出アルゴリズムの1つとの最適な組み合わせを選択することを更に含む、請求項8に記載の方法。
- 前記最適な組み合わせは受信動作曲線プロットを用いて選択される、請求項13に記載の方法。
- 1つ以上のコンピューティングデバイス上で以下のステップを実行するための1つ以上のプログラムを含む、非一時的コンピュータ可読記憶媒体であって、前記ステップは、
複数の光モードの差分画像及び属性を複数の検出アルゴリズムを使用して、前記複数の光モードについてウェーハ上の検出したい欠陥及びニューサンス事象の画像フレームに基づき決定するステップ、
前記複数の光モードのそれぞれの組み合わせを前記複数の検出アルゴリズムのそれぞれと、ゼロのオフセットで検出されるすべての前記検出したい欠陥について比較するステップであり、前記ゼロのオフセットでは、前記検出アルゴリズムが実行されて、可能な限り多くの欠陥が検出され、及び、
前記組み合わせのそれぞれにおいて前記属性のうちの1つについて捕捉率対ニューサンス率を決定するステップである、
非一時的コンピュータ可読記憶媒体。 - 前記ステップは、前記検出したい欠陥と前記ニューサンス事象を識別することを更に含む、請求項15に記載の非一時的コンピュータ可読記憶媒体。
- 前記ステップは、前記比較から閾値領域内の前記検出したい欠陥のうちの少なくとも1つをフィルタリングすることを更に含む、請求項15に記載の非一時的コンピュータ可読記憶媒体。
- 前記ステップは、前記比較から属性値を超える前記検出したい欠陥のうちの少なくとも1つをフィルタリングすることを更に含む、請求項15に記載の非一時的コンピュータ可読記憶媒体。
- 前記ステップは、前記組み合わせを前記属性によって分類することを更に含む、請求項15に記載の非一時的コンピュータ可読記憶媒体。
- 前記ステップは、選択されたニューサンス率又は選択された検出したい欠陥の捕捉率について、前記光モードの1つと前記検出アルゴリズムの1つとの最適な組み合わせを、受信動作曲線プロットを用いて選択することを更に含む、請求項15に記載の非一時的コンピュータ可読記憶媒体。
Applications Claiming Priority (5)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US201862751077P | 2018-10-26 | 2018-10-26 | |
US62/751,077 | 2018-10-26 | ||
US16/389,442 US10801968B2 (en) | 2018-10-26 | 2019-04-19 | Algorithm selector based on image frames |
US16/389,442 | 2019-04-19 | ||
PCT/US2019/058145 WO2020087004A1 (en) | 2018-10-26 | 2019-10-25 | Algorithm selector based on image frames |
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JP2022505801A JP2022505801A (ja) | 2022-01-14 |
JP7257511B2 true JP7257511B2 (ja) | 2023-04-13 |
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Country | Link |
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US (1) | US10801968B2 (ja) |
JP (1) | JP7257511B2 (ja) |
KR (1) | KR102576877B1 (ja) |
CN (1) | CN112997069B (ja) |
IL (1) | IL282335B2 (ja) |
TW (1) | TWI816922B (ja) |
WO (1) | WO2020087004A1 (ja) |
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US11769242B2 (en) | 2020-05-21 | 2023-09-26 | Kla Corporation | Mode selection and defect detection training |
CN111640112B (zh) * | 2020-06-11 | 2021-04-16 | 云从科技集团股份有限公司 | 图像检测方法、***、平台、设备及介质、图像处理装置 |
US11803960B2 (en) * | 2020-08-12 | 2023-10-31 | Kla Corporation | Optical image contrast metric for optical target search |
US11922619B2 (en) | 2022-03-31 | 2024-03-05 | Kla Corporation | Context-based defect inspection |
US20230314336A1 (en) | 2022-03-31 | 2023-10-05 | Kla Corporation | Multi-mode optical inspection |
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