Ferroelectric HfZrO2 With 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. S. Chang, M. H. Lee*

*此作品的通信作者

研究成果: Article同行評審

12 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 vertical bar alpha(p) - alpha(d)vertical bar = 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 vertical bar alpha(p) - alpha(d)vertical bar = 0.56, and G(max)/G(min) > 10x are delivered for FeFET.

原文English
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Published - 10月 2020

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