Ferroelectric polymer-based artificial synapse for neuromorphic computing

Recently, various efforts have been made to implement synaptic characteristics with a ferroelectric field-effect transistor (FeFET), but in-depth physical analyses have not been reported thus far. Here, we investigated the effects by (i) the formation temperature of the ferroelectric material, poly(vinylidene fluoride-trifluoroethylene) P(VDF-TrFE) and (ii) the nature of the contactmetals (Ti, Cr, Pd) of the FeFET on the operating performance of a FeFET-based artificial synapse in terms of various synaptic performance indices. Excellent ferroelectric properties were induced by maximizing the size and coverage ratio of the b-phase domains by annealing the P(VDF-TrFE) film at 140 degrees C. A metal that forms a relatively high barrier improved the dynamic range and nonlinearity by suppressing the contribution of the tunneling current to the post-synaptic current. Subsequently, we studied the influence of the synaptic characteristics on the training and recognition tasks by using two MNIST datasets (fashion and handwritten digits) and the multi-layer perceptron concept of neural networks.

Automated summary

This study optimized the performance of ferroelectric materials by investigating the effects of different formation temperatures and contact metals on ferroelectric transistors, making breakthroughs in simulating synaptic characteristics. Metals forming relatively high barriers are beneficial for improving dynamic range and nonlinearity.