• 제목/요약/키워드: 다이싱 모니터링

검색결과 2건 처리시간 0.016초

신호처리를 이용한 웨이퍼 다이싱 상태 모니터링 (Wafer Dicing State Monitoring by Signal Processing)

  • 고경용;차영엽;최범식
    • 한국정밀공학회지
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    • 제17권5호
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    • pp.70-75
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    • 2000
  • After the patterning and probe process of wafer have been achieved, the dicing process is necessary to separate chips from a wafer. The dicing process cuts a wafer to lengthwise and crosswise direction to make many chips by using narrow circular rotating diamond blade. But inferior goods are made under the influence of complex dicing environment such as blade, wafer, cutting water and cutting conditions. This paper describes a monitoring algorithm using feature extraction in order to find out an instant of vibration signal change when bad dicing appears. The algorithm is composed of two steps: feature extraction and decision. In the feature extraction, two features processed from vibration signal which is acquired by accelerometer attached on blade head are proposed. In the decision. a threshold method is adopted to classify the dicing process into normal and abnormal dicing. Experiment have been performed for GaAs semiconductor wafer. Based upon observation of the experimental results, the proposed scheme shown a good accuracy of classification performance by which the inferior goods decreased from 35.2% to 12.8%.

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역전파 알고리즘을 이용한 웨이퍼의 다이싱 상태 모니터링 (Monitoring of Wafer Dicing State by Using Back Propagation Algorithm)

  • 고경용;차영엽;최범식
    • 제어로봇시스템학회논문지
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    • 제6권6호
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    • pp.486-491
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    • 2000
  • The dicing process cuts a semiconductor wafer to lengthwise and crosswise direction by using a rotating circular diamond blade. But inferior goods are made under the influence of several parameters in dicing such as blade, wafer, cutting water and cutting conditions. This paper describes a monitoring algorithm using neural network in order to find out an instant of vibration signal change when bad dicing appears. The algorithm is composed of two steps: feature extraction and decision. In the feature extraction, five features processed from vibration signal which is acquired by accelerometer attached on blade head are proposed. In the decision, back-propagation neural network is adopted to classify the dicing process into normal and abnormal dicing, and normal and damaged blade. Experiments have been performed for GaAs semiconductor wafer in the case of normal/abnormal dicing and normal/damaged blade. Based upon observation of the experimental results, the proposed scheme shown has a good accuracy of classification performance by which the inferior goods decreased from 35.2% to 6.5%.

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