대한전자공학회논문지SD (Journal of the Institute of Electronics Engineers of Korea SD)

대한전자공학회 (The Institute of Electronics and Information Engineers)
권호 표지

MFSFET 소자를 이용한 Adaptive Learning Curcuit 의 설계

Design of the Adaptive Learning Circuit by Enploying the MFSFET

  • 이국표 (仁荷大學校 電子材料工學科) ;
  • 강성준 (麗水大學校 半導體 應用物理學科) ;
  • 장동훈 (仁荷大學校 電子材料工學科) ;
  • 윤영섭 (仁荷大學校 電子材料工學科)
  • Lee, Kook-Pyo (Dept. of Electronic Materials & Device Engineering, Inha Univ.) ;
  • Kang, Seong-Jun (Dept. of Semicondutor and Applied Physics, Yosu National University) ;
  • Chang, Dong-Hoon (Dept. of Electronic Materials & Device Engineering, Inha Univ.) ;
  • Yoon, Yung-Sup (Dept. of Electronic Materials & Device Engineering, Inha Univ.)
  • 발행 : 2001.08.01
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초록

본 연구에서는 MFSFET (Metal-Ferroelectric-Semiconductor FET) 소자의 모델링을 바탕으로 adaptive learning 회로를 설계하고, 그 수치적인 결과를 분석하였다. Adaptive learning 회로에서 출력주파수는 MFSFET 소자의 소스-드레인 저항과 캐패시턴스에 반비례하는 특성을 보여주었다. Short pulse 수에 따른 포화드레인 전류곡선은 강유전체의 분극반전 특성과 유사함을 확인할 수 있었고, 이는 강유전체 분극이 MFSFET 소자의 드레인 전류조절에 핵심적인 요소로 작용한다는 사실을 의미한다. 다음으로 MFSFET 소자의 드레인 전류조절에 핵심적인 요소로 작용한다는 사실을 의미한다. 다음으로 MFSFET 소자의 소스-드레인 저항으로부터 dimensionality factor 와 adaptive learning 회로의 펄스 수에 따른 출력주파수 변화를 분석하였다. 이 특성으로부터, adaptive learning 회로의 주파수변조 특성 즉, 입력펄스의 진행에 따라 출력펄스의 점진적인 주파수 변화를 의미하는 adaptive learning 특성을 명화하게 확인할 수 있었고, 뉴럴 네트워크에서 본 회로가 뉴런의 시넵스 부분에 효과적으로 사용될 수 있음을 입증하였다.

The adaptive learning circuit is designed on the basis of modeling of MFSFET (Metal-Ferroelectric-Semiconductor FET) and the numerical results are analyzed. The output frequency of the adaptive learning circuit is inversely proportional to the source-drain resistance of MFSFET and the capacitance of the circuit. The saturated drain current with input pulse number is analogous to the ferroelectric polarization reversal. It indicates that the ferroelectric polarization plays an important role in the drain current control of MFSFET. The output frequency modulation of the adaptive learning circuit is investigated by analyzing the source-drain resistance of MFSFET as functions of input pulse numbers in the adaptive learning circuit and the dimensionality factor of the ferroelectric thin film. From the results, the frequency modulation characteristic of the adaptive learning circuit are confirmed. In other words, adaptive learning characteristics which means a gradual frequency change of output pulse with the progress of input pulse are confirmed. Consequently it is shown that our circuit can be used effectively in the neuron synapses of nueral networks.

키워드

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