반도체디스플레이기술학회지 (Journal of the Semiconductor & Display Technology)

한국반도체디스플레이기술학회 (The Korean Society Of Semiconductor & Display Technology)
권호 표지

LTPO 소자의 머신 러닝 모델 개발

Development of Machine Learning Model of LTPO Devices

  • 은정수 (상명대학교 시스템반도체공학부) ;
  • 안진수 (상명대학교 시스템반도체공학부) ;
  • 이민석 (상명대학교 시스템반도체공학부) ;
  • 곽우석 (상명대학교 시스템반도체공학부) ;
  • 이종환 (상명대학교 시스템반도체공학부)
  • Jungsoo Eun (Department of System Semiconductor Engineering, Sangmyung University) ;
  • Jinsoo Ahn (Department of System Semiconductor Engineering, Sangmyung University) ;
  • Minseok Lee (Department of System Semiconductor Engineering, Sangmyung University) ;
  • Wooseok Kwak (Department of System Semiconductor Engineering, Sangmyung University) ;
  • Jonghwan Lee (Department of System Semiconductor Engineering, Sangmyung University)
  • 투고 : 2023.12.08
  • 심사 : 2023.12.19
  • 발행 : 2023.12.31
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초록

We propose the modeling methodology of CMOS inverter made of LTPO TFT using a machine learning. LTPO can achieve advantages of LTPS TFT with high electron mobility as a driving TFT and IGZO TFT with low off-current as a switching TFT. However, since the unified model of both LTPS and IGZO TFTs is still lacking, it is necessary to develop a SPICE-compatible compact model to simulate the LTPO current-voltage characteristics. In this work, a generic framework for combining the existing formula of I-V characteristics with artificial neural network is presented. The weight and bias values of ANN for LTPS and IGZO TFTs is obtained and implemented into PSPICE circuit simulator to predict CMOS inverter. This methodology enables efficient modeling for predicting LTPO TFT circuit characteristics.

키워드

과제정보

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2022R1I1A3064285).

참고문헌

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