Search Results - "Nov, Lihour"

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  1. 1
    Characterization of Resistance and Inductance of PIN Diode at mmWave frequency using 7-layer Deep Neural Network

    Characterization of Resistance and Inductance of PIN Diode at mmWave frequency using 7-layer Deep Neural Network by Nov, Lihour, Chrek, Thorn, Chung, Jae-Young

    Published in IEEE access (01-01-2023)
    “…This paper presents a novel technique for extracting the resistance (R) and inductance (L) of an ultra-low capacitance PIN diode, a critical component in…”
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  2. 2
    Broadband Permittivity Characterization of a Substrate Material Using Deep Neural Network Trained With Full-Wave Simulations

    Broadband Permittivity Characterization of a Substrate Material Using Deep Neural Network Trained With Full-Wave Simulations by Nov, Lihour, Chung, Jae-Young, Park, James

    Published in IEEE access (2022)
    “…It is crucial to know the permittivity of dielectric materials used in radio frequency (RF) components and devices because their operation frequency and loss…”
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  3. 3
    Deep neural network for retrieving material's permittivity from S‐parameters

    Deep neural network for retrieving material's permittivity from S‐parameters by Chrek, Thorn, Nov, Lihour, Chung, Jae‐Young

    Published in Microwave and optical technology letters (01-02-2023)
    “…This research proposed a deep neural network (DNN) model employing a multilayer feedforward artificial neural network to characterize the relative permittivity…”
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  4. 4
    Broadband insertion loss measurement of flexible copper clad laminate with direct metallization

    Broadband insertion loss measurement of flexible copper clad laminate with direct metallization by Kamrul, Md. Imtiaz, Nov, Lihour, Chung, Jae-Young, Lee, Byeung-Ju, Kim, Eon-Joong

    Published in Sensors and actuators. A. Physical. (16-06-2023)
    “…The technological trend of replacing the existing ceramic antenna and coaxial cable with the flexible PCB (FPCB) and feed line integrated module has recently…”
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  5. 5
    Precise Modeling of Active Component in Ka-Band Using Deep Neural Network Trained with S21 Data

    Precise Modeling of Active Component in Ka-Band Using Deep Neural Network Trained with S21 Data by Nov, Lihour, Chrek, Thorn, Chung, Jae-Young

    “…This paper proposes a method to precisely determine the R-L-C characteristics of active components, namely PIN diode and varactor diode, with an in-house…”
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