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Title: Highly Controllable and Silicon-Compatible Ferroelectric Photovoltaic Synapses for Neuromorphic Computing
Authors: Cheng, S.
Fan, Z.
Rao, J.
Hong, L. 
Huang, Q.
Tao, R.
Hou, Z.
Qin, M.
Zeng, M.
Lu, X.
Zhou, G.
Yuan, G.
Gao, X.
Liu, J.-M.
Keywords: Circuit Systems
Electrical Engineering
Materials Science
Semiconductor Manufacturing
Issue Date: 2020
Publisher: Elsevier Inc.
Citation: Cheng, S., Fan, Z., Rao, J., Hong, L., Huang, Q., Tao, R., Hou, Z., Qin, M., Zeng, M., Lu, X., Zhou, G., Yuan, G., Gao, X., Liu, J.-M. (2020). Highly Controllable and Silicon-Compatible Ferroelectric Photovoltaic Synapses for Neuromorphic Computing. iScience 23 (12) : 101874. ScholarBank@NUS Repository.
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Abstract: Circuit Systems; Electrical Engineering; Semiconductor Manufacturing; Materials Science; Devices © 2020 The Author(s)Ferroelectric synapses using polarization switching (a purely electronic switching process) to induce analog conductance change have attracted considerable interest. Here, we propose ferroelectric photovoltaic (FePV) synapses that use polarization-controlled photocurrent as the readout and thus have no limitations on the forms and thicknesses of the constituent ferroelectric and electrode materials. This not only makes FePV synapses easy to fabricate but also reduces the depolarization effect and hence enhances the polarization controllability. As a proof-of-concept implementation, a Pt/Pb(Zr0.2Ti0.8)O3/LaNiO3 FePV synapse is facilely grown on a silicon substrate, which demonstrates continuous photovoltaic response modulation with good controllability (small nonlinearity and write noise) enabled by gradual polarization switching. Using photovoltaic response as synaptic weight, this device exhibits versatile synaptic functions including long-term potentiation/depression and spike-timing-dependent plasticity. A simulated FePV synapse-based neural network achieves high accuracies (>93%) for image recognition. This study paves a new way toward highly controllable and silicon-compatible synapses for neuromorphic computing. © 2020 The Author(s)
Source Title: iScience
ISSN: 2589-0042
DOI: 10.1016/j.isci.2020.101874
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
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