Abstract
SummaryFerroelectric 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.
Highlights
The human brain can outperform the most advanced digital computer in many intellectual tasks, such as image and voice recognition, data classification, and associative learning (Drachman, 2005; Banerjee et al, 2017; Kuzum et al, 2013; Markram, 2012)
Gradual Polarization Switching in Polycrystalline PZT Film The key component of the proposed ferroelectric photovoltaic (FePV) synapse is the polycrystalline PZT film (Figure S1) exhibiting gradual polarization switching, which enables the access to multilevel photovoltaic responses
We previously demonstrated that the switchable photovoltaic response could be observed in an FePV device as small as $1 mm2 (Fan et al, 2017b)
Summary
The human brain can outperform the most advanced digital computer in many intellectual tasks, such as image and voice recognition, data classification, and associative learning (Drachman, 2005; Banerjee et al, 2017; Kuzum et al, 2013; Markram, 2012). Most reported memristors are based on filament-forming oxides (Yan et al, 2018; Guan et al, 2019), electrolyte-gated oxides and polymers (Ge et al, 2019; Gkoupidenis et al, 2015), two-dimensional (2D) nitrides and sulfides (Shi et al, 2017; Wang et al, 2018; Li et al, 2018), and phase change materials (Tuma et al, 2016; Kuzum et al, 2012; Ge et al, 2020). Large device variability and poor reliability are ubiquitous in these memristors
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