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[Keyword] gate driver(2hit)

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  • Computer-Aided Design of Cross-Voltage-Domain Energy-Optimized Tapered Buffers Open Access

    Zhibo CAO  Pengfei HAN  Hongming LYU  

     
    PAPER-Electronic Circuits

      Pubricized:
    2024/04/09
      Vol:
    E107-C No:9
      Page(s):
    245-254

    This paper introduces a computer-aided low-power design method for tapered buffers that address given load capacitances, output transition times, and source impedances. Cross-voltage-domain tapered buffers involving a low-voltage domain in the frontier stages and a high-voltage domain in the posterior stages are further discussed which breaks the trade-off between the energy dissipation and the driving capability in conventional designs. As an essential circuit block, a dedicated analytical model for the level-shifter is proposed. The energy-optimized tapered buffer design is verified for different source and load conditions in a 180-nm CMOS process. The single-VDD buffer model achieves an average inaccuracy of 8.65% on the transition loss compared with Spice simulation results. Cross-voltage tapered buffers can be optimized to further remarkably reduce the energy consumption. The study finds wide applications in energy-efficient switching-mode analog applications.

  • Estimation of Switching Loss and Voltage Overshoot of Active Gate Driver by Neural Network

    Satomu YASUDA  Yukihisa SUZUKI  Keiji WADA  

     
    BRIEF PAPER

      Pubricized:
    2020/05/01
      Vol:
    E103-C No:11
      Page(s):
    609-612

    An active gate driver IC generates arbitrary switching waveform is proposed to reduce the switching loss, the voltage overshoot, and the electromagnetic interference (EMI) by optimizing the switching pattern. However, it is hard to find optimal switching pattern because the switching pattern has huge possible combinations. In this paper, the method to estimate the switching loss and the voltage overshoot from the switching pattern with neural network (NN) is proposed. The implemented NN model obtains reasonable learning results for data-sets.