JP2020514721A - 深くスタック化された層を有するウェハにおいて欠陥分類器を訓練して適用するためのシステムと方法 - Google Patents
深くスタック化された層を有するウェハにおいて欠陥分類器を訓練して適用するためのシステムと方法 Download PDFInfo
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Abstract
Description
本出願は、2017年1月10日に出願された米国仮特許出願第62/444、690号の利益を主張し、この出願の内容全体が参照により本明細書に組み込まれる。
Claims (19)
- 方法を実行するためにプロセッサによって実行可能なコードを有するコンピュータプログラム製品を記憶する非一過性コンピュータ可読媒体であって、前記方法は、
検査システムによってウェハにおいて検出された欠陥の位置についての、前記検査システムによって生成された複数の画像を取得することであって、前記ウェハにおける前記位置は、複数のスタック化層から構成され、前記複数の画像のそれぞれの画像は、異なる焦点設定を用いて前記位置で前記検査システムによって生成される、ことと、
前記複数の画像を利用して、前記欠陥の分類を決定することと、を備える非一過性コンピュータ可読媒体。 - 前記ウェハは、3次元(3D)NANDウェハである、請求項1に記載の非一過性コンピュータ可読媒体。
- 前記欠陥は、前記複数のスタック化層の基礎層内にある、請求項1に記載の非一過性コンピュータ可読媒体。
- 前記異なる焦点設定のそれぞれは、異なる深さで、前記位置で前記ウェハ中に光を集中させる、請求項1に記載の非一過性コンピュータ可読媒体。
- 前記欠陥の分類は、前記複数の画像から直接決定される、請求項1に記載の非一過性コンピュータ可読媒体。
- 前記欠陥の分類は、1つ又は複数の属性について、前記複数の画像にわたる変化を識別することによって決定されたスルーフォーカス信号プロファイルから決定され、
前記欠陥の分類は、1つ又は複数の属性について識別された前記変化に基づいて決定される、請求項1に記載の非一過性コンピュータ可読媒体。 - 前記1つ又は複数の属性は、輝度を含む、請求項6に記載の非一過性コンピュータ可読媒体。
- 前記1つ又は複数の属性は、強度を含む、請求項6に記載の非一過性コンピュータ可読媒体。
- 前記欠陥の分類は、前記欠陥がそこに存在する、前記複数のスタック化層のうちの1つの層を示す、請求項1に記載の非一過性コンピュータ可読媒体。
- 前記分類を利用して、訓練集合を生成することを更に備える、請求項1に記載の非一過性コンピュータ可読媒体。
- 前記訓練集合を用いて、前記検査システムの分類器を訓練することを更に備える、請求項10に記載の非一過性コンピュータ可読媒体。
- 前記訓練された分類器を用いて、欠陥をビン分割することを更に備える、請求項11に記載の非一過性コンピュータ可読媒体。
- 前記欠陥を同一のビン内の別の欠陥とともにプロットすることを更に備え、それにより、前記ビンについての空間特徴的性質を生成する、請求項12に記載の非一過性コンピュータ可読媒体。
- 前記ビンについて生成された前記空間特徴的性質を用いて、前記検査システムを調整することを更に備える、請求項13に記載の非一過性コンピュータ可読媒体。
- 方法であって、
検査システムによってウェハにおいて検出された欠陥の位置についての前記検査システムによって生成された複数の画像をコンピュータプロセッサによって取得することであって、前記ウェハにおける位置は、複数のスタック化層から構成され、前記複数の画像のそれぞれの画像は、異なる焦点設定を用いて、前記位置で前記検査システムによって生成される、ことと、
前記複数の画像を利用して、前記欠陥の分類を前記コンピュータプロセッサによって決定することと、を備える方法。 - システムであって、
コンピュータコードを記憶するメモリと、
前記メモリに結合され、方法を実行するための前記コンピュータコードを実行するように構成されたプロセッサと、を備え、前記方法は、
検査システムによってウェハにおいて検出された欠陥の位置についての、前記検査システムによって生成された複数の画像を取得することであって、前記ウェハにおける前記位置は、複数のスタック化層から構成され、前記複数の画像のそれぞれの画像は、異なる焦点設定を用いて、前記位置で前記検査システムによって生成される、ことと、
前記複数の画像を利用して、前記欠陥の分類を決定することと、を備えるシステム。 - 前記システムは、前記検査システムを備える、請求項16に記載のシステム。
- 前記方法は、前記分類を利用して、訓練集合を生成することを更に備える、請求項16に記載のシステム。
- 前記方法は、訓練集合を用いて、前記検査システムの分類器を訓練することを更に備える、請求項16に記載のシステム。
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US15/863,753 US10964013B2 (en) | 2017-01-10 | 2018-01-05 | System, method for training and applying defect classifiers in wafers having deeply stacked layers |
PCT/US2018/013178 WO2018132480A1 (en) | 2017-01-10 | 2018-01-10 | System, method for training and applying defect classifiers in wafers having deeply stacked layers |
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