JP2022507543A - 画素レベル画像定量のための深層学習式欠陥検出及び分類方式の使用 - Google Patents
画素レベル画像定量のための深層学習式欠陥検出及び分類方式の使用 Download PDFInfo
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Abstract
Description
本願では、2018年11月15日付印国特許出願第201841042919号に基づく優先権を主張するので、その開示内容を参照により本願に繰り入れるものとする。
Claims (18)
- 電子ビームを生成する電子ビーム源と、
その電子ビームの経路上でウェハを保持するよう構成されたステージと、
そのウェハから返される電子ビームを受け取るよう構成された検出器と、
その検出器と電子通信するプロセッサと、
を備え、そのプロセッサが、
上記検出器から受け取ったデータから生成された画像における欠陥候補のヒートマップを、各画素に対応する欠陥確率指数の行列として表現し、且つ
その画像内の画素のうち上記行列にて対応する閾値を上回るものの個数を定量するよう、
構成されているシステム。 - 請求項1に記載のシステムであって、更に、上記プロセッサにより稼働される深層学習モジュールを備え、その深層学習モジュールが、
上記画像を受け取り、
その画像を対象にして欠陥検出を実行し、且つ
その画像を対象にして欠陥分類を実行するよう、
構成されているシステム。 - 請求項2に記載のシステムであって、上記プロセッサが、更に、上記ヒートマップを決定するよう構成されているシステム。
- 請求項1に記載のシステムであって、上記画素の一員に対応する閾値が、上記画像上で、当該一員の画素と同じ個所にあるシステム。
- 請求項1に記載のシステムであって、上記定量が画素レベル画像定量にて用いられるシステム。
- 請求項1に記載のシステムであって、上記欠陥候補がEUVストキャスティクスであるシステム。
- 請求項1に記載のシステムであって、上記欠陥候補が限界寸法欠陥であるシステム。
- 請求項1に記載のシステムであって、上記画像が走査型電子顕微鏡画像であるシステム。
- 検出器から受け取ったデータから生成された画像における欠陥候補のヒートマップを、プロセッサを用い、各画素に対応する欠陥確率指数の行列として表現し、且つ
その画像内の画素のうち上記行列にて対応する閾値を上回るものの個数を、そのプロセッサを用い定量する方法。 - 請求項9に記載の方法であって、更に、
上記プロセッサにて上記画像を受け取り、
上記プロセッサに備わる深層学習モジュールを用い、その画像を対象にして欠陥検出を実行し、且つ
上記プロセッサに備わる上記深層学習モジュールを用い、その画像を対象にして欠陥分類を実行する方法。 - 請求項10に記載の方法であって、更に、上記プロセッサを用い上記ヒートマップを決定する方法。
- 請求項9に記載の方法であって、上記画素の一員に対応する閾値が、上記画像上で、当該一員の画素と同じ個所にある方法。
- 請求項9に記載の方法であって、上記定量が画素レベル画像定量にて用いられる方法。
- 請求項9に記載の方法であって、上記欠陥候補がEUVストキャスティクスである方法。
- 請求項9に記載の方法であって、上記欠陥候補が限界寸法欠陥である方法。
- 請求項9に記載の方法であって、上記画像が走査型電子顕微鏡画像である方法。
- 請求項9に記載の方法であって、更に、
電子ビームをウェハに差し向け、
そのウェハから返される電子を検出器で以て集め、且つ
上記プロセッサを用いそのウェハの画像を生成する方法。 - 請求項9に記載の方法を実行することをプロセッサに命令するよう構成されたプログラムが格納されている非一時的コンピュータ可読媒体。
Applications Claiming Priority (5)
Application Number | Priority Date | Filing Date | Title |
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IN201841042919 | 2018-11-15 | ||
IN201841042919 | 2018-11-15 | ||
US16/249,337 US10672588B1 (en) | 2018-11-15 | 2019-01-16 | Using deep learning based defect detection and classification schemes for pixel level image quantification |
US16/249,337 | 2019-01-16 | ||
PCT/US2019/061578 WO2020102611A1 (en) | 2018-11-15 | 2019-11-15 | Using deep learning based defect detection and classification schemes for pixel level image quantification |
Publications (3)
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JP2022507543A true JP2022507543A (ja) | 2022-01-18 |
JPWO2020102611A5 JPWO2020102611A5 (ja) | 2022-11-21 |
JP7216822B2 JP7216822B2 (ja) | 2023-02-01 |
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US (1) | US10672588B1 (ja) |
EP (1) | EP3870959A4 (ja) |
JP (1) | JP7216822B2 (ja) |
KR (1) | KR102513717B1 (ja) |
CN (1) | CN112969911B (ja) |
TW (1) | TWI805868B (ja) |
WO (1) | WO2020102611A1 (ja) |
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CN117121064A (zh) * | 2021-03-30 | 2023-11-24 | Asml荷兰有限公司 | 改进的带电粒子图像检测 |
CN112884769B (zh) * | 2021-04-12 | 2021-09-28 | 深圳中科飞测科技股份有限公司 | 图像处理方法、装置、光学系统和计算机可读存储介质 |
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- 2019-11-15 JP JP2021526582A patent/JP7216822B2/ja active Active
- 2019-11-15 EP EP19884619.8A patent/EP3870959A4/en active Pending
- 2019-11-15 TW TW108141684A patent/TWI805868B/zh active
- 2019-11-15 WO PCT/US2019/061578 patent/WO2020102611A1/en unknown
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EP3870959A4 (en) | 2022-07-27 |
KR102513717B1 (ko) | 2023-03-23 |
KR20210080567A (ko) | 2021-06-30 |
US20200161081A1 (en) | 2020-05-21 |
EP3870959A1 (en) | 2021-09-01 |
CN112969911B (zh) | 2022-09-06 |
US10672588B1 (en) | 2020-06-02 |
TW202033954A (zh) | 2020-09-16 |
JP7216822B2 (ja) | 2023-02-01 |
WO2020102611A1 (en) | 2020-05-22 |
TWI805868B (zh) | 2023-06-21 |
CN112969911A (zh) | 2021-06-15 |
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