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Semiconductor chip’s quality analysis based on its high dimensional test data

Author

Listed:
  • Sun Kai

    (Institute of Microelectronics of Chinese Academy of Sciences)

  • Wu Jin

    (Institute of Microelectronics of Chinese Academy of Sciences)

Abstract

A semiconductor chip usually has thousands test parameters in order to guaranteed its quality. Hence, a batch of chips’ test data set include thousands of float data. The primary goal of dealing with this test data is to obtain the fault parameter distribution and judge the chip’s quality. It is a challenge due to the large scale and complex relationship of the test data set. This paper presents a novel method to analyze the test data set by meshing the quality theory and scientific data visualization. First, transfer the test data set to a quality classifier matrix Q: a series of quality region is defined based on quality theory, which is the baseline to classify the test data set into different group and mark them with various number. Second, form a quality-spectrum: define a color rule based on the RGB color model and color the quality classifier matrix Q. Hence chip’s quality distribution could be observed through the quality-spectrum. Furthermore, by analyzing the quality-spectrum, the chip’s quality could be quantitative and fault diagnose has a data basic. One case is included to illustrate appropriateness of the proposed method.

Suggested Citation

  • Sun Kai & Wu Jin, 2022. "Semiconductor chip’s quality analysis based on its high dimensional test data," Annals of Operations Research, Springer, vol. 311(1), pages 183-194, April.
    • Handle: RePEc:spr:annopr:v:311:y:2022:i:1:d:10.1007_s10479-019-03240-z
    DOI: 10.1007/s10479-019-03240-z
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    References listed on IDEAS

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    1. Jiang, Jiancheng & Jiang, Xuejun & Li, Jingzhi & Liu, Yi & Yan, Wanfeng, 2017. "Spatial quantile estimation of multivariate threshold time series models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 772-781.
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