Abstract
The interface engineering is always an important way to modulate the behavior of the ferroelectric tunnel memristor (FTM), which directly affects its biological synaptic properties. Here, to investigate the effect of interface on bio-synapse performance, FTMs with the structure of Pt/ BaTiO3/La0.67Sr0.33MnO3 were studied, of which the interfaces of the device can be controlled through tailoring the termination of the SrTiO3 substrate and the unit-cell of BaTiO3 thin film by its growth mode. FTMs with different interfaces present opposite resistive switching behaviors due to the contrary polarization directions and the different band alignments in the BaTiO3 film. More importantly, the synaptic learning properties of FTMs can also be tuned by tailoring the interface. FTMs with different interface terminations could modulate different properties of long-term potentiation (LTP), long-term depression (LTD), spike-timing-dependent plasticity (STDP), and paired-pulse facilitation (PPF). Based on the synaptic behaviors of these two interface-engineered FTMs, the artificial neural network (ANN) system could be constructed to complete a handwritten digital image recognition process, and the accuracies of both are close to 90%. Our results provide a useful reference for tuning the functionalities of memristors through nanoscale interface engineering.
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