DEFECT DETERMINATION DEVICE AND DEFECT DETERMINATION METHOD

• Main Authors: IKEGAWA TAKASHI, ARIKI RYOSUKE, TANAKA SHINICHI, TAKAYA SHUNSUKE, ISHIKAWA AKIHO, MIYOSHI NOBUYA, HONDA FUMIHIRO, YAMAOKA TOSHIHIRO, NISHIO MAMORU
• Format: Patent
• Language: English; Japanese
• Published: 18.05.2022
• Subjects: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIRCHEMICAL OR PHYSICAL PROPERTIES; MEASURING; PHYSICS; TESTING

To provide a defect determination device capable of accurately determining whether or not any defect is included in an object to be inspected.SOLUTION: A defect determination device 20 comprises an inspection data acquisition section 21, a preprocessing section 22, and a determination section 23. The inspection data acquisition section 21 acquires waveform data indicating the amplitude of a reflected wave corresponding to the depth of an object 100 to be inspected. The preprocessing section 22 performs preprocessing on the waveform data to output determination data. The determination section 23 performs determination processing using a determination model for outputting a determination result of whether or not any defect is included in the object 100 to be inspected with the determination data as input. The preprocessing includes processing for extracting a plurality of peaks of the waveform data on the basis of at least the size of the amplitude of the waveform data and processing for outputting the size and depth of the amplitude of the extracted peak. The determination model is constructed by performing machine learning on learning data which is output by performing preprocessing on the waveform data obtained in the previous inspection, and the determination result obtained in the previous inspection.SELECTED DRAWING: Figure 1 【課題】検査対象物に欠陥が含まれるか否かを精度良く判定可能な欠陥判定装置を提供する。【解決手段】欠陥判定装置２０は、検査データ取得部２１と、前処理部２２と、判定部２３と、を備える。検査データ取得部２１は、検査対象物１００の深さに応じた反射波の振幅を示す波形データを取得する。前処理部２２は、波形データに前処理を行って判定用データを出力する。判定部２３は、判定用データを入力として、検査対象物１００に欠陥が含まれるか否かの判定結果を出力する判定モデルを用いて判定処理を行う。前処理には、少なくとも波形データの振幅の大きさに基づいて波形データの複数のピークを抽出する処理と、抽出したピークの振幅の大きさと深さを出力する処理が含まれる。判定モデルは、過去の検査で得られた波形データに前処理を行うことで出力される学習用データと、過去の検査での判定結果と、に対して機械学習を行うことにより構築されている。【選択図】図１