Controlling Long-Term Plasticity in Neuromorphic Computing Through Modulation of Ferroelectric Polarization

Electrolyte-gated transistors (EGTs) have significant potential for neuromorphic computing because they can control the number of ions by mimicking neurotransmitters. However, fast depolarization of the electric double layer (EDL) makes it difficult to achieve long-term plasticity (LTP). Additionally, most research utilizing organic ferroelectric materials has been focused on basic biological functions, and the impact on nonvolatile memory properties is still lacking. Herein, we present a polyvinylidene fluoride (PVDF)-based ion-gel synaptic device using PVDF and poly(vinylidene fluoride-<i>co</i>-hexafluoropropylene) (P(VDF-HFP)) to implement LTP through the introduction of ferroelectric materials. The PVDF-based polymer slows the escape rate of TFSI anions from the electrolyte/channel layer through residual polarization. The fabricated synaptic devices successfully demonstrate LTP by controlling ion adsorption under the influence of PVDF-based polymers. Furthermore, it implements synaptic functions including paired pulse facilitation (PPF), high-pass filtering, and neurotransmitter control. To validate the potential of neuromorphic computing, we successfully achieved high recognition rates for artificial/convolutional neural network (A/CNN) simulations via sequential adsorption and desorption under ferroelectric polarization with long-term potentiation/depression (LTP/D). This study provides a rational ion adsorption strategy utilizing the ferroelectric polarization caused by the introduction of a PVDF-based polymer in the dielectric layer.