Unveiling the origin of unconventional moire ferroelectricity

TL;DR

This paper uncovers that interfacial sliding ferroelectricity, rather than moire bands, causes unconventional ferroelectricity in heterostructures, enabling more reproducible and reliable memory devices for AI applications.

Contribution

It identifies the true origin of moire ferroelectricity as interfacial sliding in heterostructures, moving beyond the correlation-driven charge transfer hypothesis.

Findings

Ferroelectricity arises from BN interfacial sliding, not graphene moire bands.

Multilayer twisted MoS2 can reproduce ferroelectricity.

Screened gate and pinned domain wall lead to anomalous screening.

Abstract

Interfacial ferroelectricity emerges in heterostructures consisting of nonpolar van der Waals (vdW) layers, greatly expanding the scope of two dimensional ferroelectrics. In particular, the unconventional moire ferroelectricity observed in bilayer graphene/boron nitride (BN) heterostructures, exhibits promising functionalities with topological current, superconductivity and synaptic responses. However, the debate about its mechanism - correlation driven charge transfer between two graphene layers - limits device reproducibility and hence large-scale production. Here by designing a single-layer graphene encapsulated by lattice-mismatched WSe2, we identify the ferroelectricity as stemming from - instead of graphene moire bands - the particular BN, where interfacial sliding ferroelectricity must play a role. With similar structures, multilayer twisted MoS2 is found to reproduce the ferroelectricity. The key is a conductive moire ferroelectric, where the screened gate and the pinned domain wall together result in unchanged electronic states, i.e. anomalous screening. The intimate connection to interfacial sliding ferroelectricity thus provides advantages of diverse choices of constituent materials and robust polarization switching while preserving the unique anomalous screening, paving the way to reproducible and reliable memory-based devices in artificial intelligence.