Hsiang K.-YMING-HAN LIAO2021-08-052021-08-0520200018-9383https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092097938&doi=10.1109%2fTED.2020.3017463&partnerID=40&md5=86b5d704d6c22e0d26d59029904dd06ahttps://scholars.lib.ntu.edu.tw/handle/123456789/576191Atomic 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. ? 1963-2012 IEEE.Atomic layer deposition; Deep neural networks; Electrodes; Ferroelectricity; Field effect transistors; Hafnium compounds; Titanium nitride; Zirconium compounds; Ferroelectric field effect transistors; Neural network training; Polarization modulation; Pulse analysis; Synaptic weight; TiN electrodes; Weight training; Weight transfer; Neural networksjournal article10.1109/TED.2020.30174632-s2.0-85092097938