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Author: search.filters.author.Ruangphanit, AnuchaSubject: search.filters.subject.BSIM3

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    Effects of Temperature and Channel Doping on the BSIM3 Threshold Voltage Model of NMOSFET form Substrate Bias Dependent Methodology
    (2016-01-01)
    Muanghlua, Rangson
    ;
    Niemcharoen, Surasak
    ;
    Poyai, Amporn
    ;
    Ruangphanit, Anucha
    The effects of channel doping and temperature dependence on the BSIM3 threshold voltage model of NMOSFET form substrate bias dependent methodology is proposed. The I<inf>DS</inf> -V<inf>GS</inf> in linear region with different substrate bias condition of a big size of NMOSFET was used. The threshold voltage parameters extraction procedure is based on the measurement of the transconductance characteristics of MOSFET in linear region. The electrical parameters γ, N<inf>CH</inf> and N<inf>SUB</inf> also the BSIM3 model parameter K<inf>1</inf> and K<inf>2</inf> at different channel implanted dose and different operating temperature are extracted. The model can be implemented in simulation tools with the error is less than 5%.
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    A new mismatch model of temperature and narrow channel width dependence on threshold voltage of MOSFETs
    (2014-01-01)
    Ruangphanit, Anucha
    ;
    Sakuna, Natthaphon
    ;
    Niemcharoen, Surasak
    ;
    Muanghlua, Rangson
    A new mismatch model of temperature and narrow channel dependence on threshold voltage of MOSFETs and the parameter extraction method are proposed. A new model is developed from spice level3 and BSIM3 model. The IDS -VGS in linear region was used with a different of channel width. The threshold voltage parameters extraction procedure is based on the measurement of the tranconductance characteristics of MOSFET in linear region. In this predicted model, the temperature coefficient for threshold voltage and the body-bias coefficient of threshold voltage of a big MOSFET at various narrow channel width of MOSFET are determined. The results show that the deviations of experimentally measured threshold voltages of both devices from the predicted model are around 3%. © (2014) Trans Tech Publications, Switzerland.