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    Avoiding erroneous analysis of MIM diode current-voltage characteristics through exponential fitting

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    Type
    Article
    Authors
    Pelz, Bradley cc
    Belkadi, Amina cc
    Moddel, Garret
    Date
    2018-02-07
    Online Publication Date
    2018-02-07
    Print Publication Date
    2018-05
    Permanent link to this record
    http://hdl.handle.net/10754/629737
    
    Metadata
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    Abstract
    Accurate fitting of measured current-voltage [I(V)I(V)] data is crucial to the correct analysis and understanding of metal-insulator–metal (MIM) diodes, especially for optical rectennas. With the commonly used polynomial fitting of the I(V)I(V) data, the order of the fit can drastically affect the diode performance metrics such as resistance, responsivity, and asymmetry. Additionally, the resulting fitting coefficients provide no useful parameters. An exponential-based equation can fit the I(V)I(V) data well, can avoid artifacts from the choice of order of the polynomial, and allows for the accurate calculation of diode performance metrics directly from the fitting coefficients. Connecting the performance metrics to fitting coefficients shows a correspondence between zero-bias responsivity and asymmetry at any given voltage.
    Citation
    Pelz B, Belkadi A, Moddel G (2018) Avoiding erroneous analysis of MIM diode current-voltage characteristics through exponential fitting. Measurement 120: 28–33. Available: http://dx.doi.org/10.1016/j.measurement.2018.01.054.
    Sponsors
    The authors would like to thank David Doroski and Brad Herner for their assistance in fabricating the MIM devices and Miena Armanious, Ayendra Weerakkody and John Stearns for their helpful discussions. This work was carried out in part under contract from RedWave Energy Inc. and funded in part by the Advanced Research Projects Agency - Energy (ARPA-E), U.S. Department of Energy, under Award Number DE-AR0000676. Also, research reported in this publication was supported by competitive research funding from King Abdullah University of Science and Technology (KAUST). G. Moddel holds stock in RedWave Energy, Inc.
    Publisher
    Elsevier BV
    Journal
    Measurement
    DOI
    10.1016/j.measurement.2018.01.054
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.measurement.2018.01.054
    Scopus Count
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