Ferroelectric HfZrO2with Electrode Engineering and Stimulation Schemes as Symmetric Analog Synaptic Weight Element for Deep Neural Network Training

K. Y. Hsiang, C. Y. Liao, K. T. Chen, Y. Y. Lin, C. Y. Chueh, C. Chang, Y. J. Tseng, Y. J. Yang, S. T. Chang, M. H. Liao, T. H. Hou, C. H. Wu, C. C. Ho, J. P. Chiu, C. Chang, M. H. Lee

研究成果: 雜誌貢獻期刊論文同行評審

1 引文 斯高帕斯(Scopus)

摘要

Atomic layer deposition (ALD)-based TiN electrode on ferroelectric HfZrO2 metal/ferroelectric/metal (MFM) capacitor and ferroelectric field-effect transistor (FeFET) is demonstrated experimentally with weight transfer, that is, $\Delta {P}$ , per pulse analysis through consecutive alternating potentiation/depression (Pot./Dep.) training pulses. The weight training pulse schemes are studied to have symmetric and linear synapse weight transfer to increase the accuracy and accelerate the deep neural network (DNN) training. With ALD TiN inserted, $\alpha _{p} / \alpha _{d} = -0.63$ / -0.84, asymmetry $\vert \alpha _{p} - \alpha _{d}\vert =0.21$ , and polarization modulation ratio (Pot./Dep.) = 97%/98% are achieved for MFM capacitor, and $\alpha _{p} / \alpha _{d} = -1.32$ / -1.88, asymmetry $\vert \alpha _{p} - \alpha _{d}\vert =0.56$ , and $G_{\text {max}} / G_{\text {min}} > 10\times $ are delivered for FeFET.

原文英語
文章編號9180313
頁(從 - 到)4201-4207
頁數7
期刊IEEE Transactions on Electron Devices
67
發行號10
DOIs
出版狀態已發佈 - 2020 十月

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Electrical and Electronic Engineering

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