Hole-Doped Nonvolatile and Electrically Controllable Magnetism in van der Waals Ferroelectric Heterostructures

Electrical control of magnetism in van der Waals semiconductors is a promising step towards development of two-dimensional spintronic devices with ultralow power consumption for processing and storing information.Here, we propose a design for two-dimensional van der Waals heterostructures(vdWHs) that can host ferroelectricity and ferromagnetism simultaneously under hole doping. By contacting an In Se monolayer and forming an InSe/In_(2)Se_(3) vd WH, the switchable built-in electric field from the reversible out-of-plane polarization enables robust control of the band alignment. Furthermore, switching between the two ferroelectric states(P_↑ and P_↓)of hole-doped In_(2)Se_(3) with an external electric field can interchange the ON and OFF states of the nonvolatile magnetism. More interestingly, doping concentration and strain can effectively tune the magnetic moment and polarization energy. Therefore, this provides a platform for realizing multiferroics in ferroelectric heterostructures,showing great potential for use in nonvolatile memories and ferroelectric field-effect transistors.

SemID: Blind Image Inpainting with Semantic Inconsistency Detection

Most existing image inpainting methods aim to fill in the missing content in the inside-hole region of the target image. However, the areas to be restored in realistically degraded images are unspecified. Previous studies have failed to recover the degradations due to the absence of the explicit mask indication. Meanwhile, inconsistent patterns are blended complexly with the image content. Therefore, estimating whether certain pixels are out of distribution and considering whether the object is consistent with the context is necessary. Motivated by these observations, a two-stage blind image inpainting network, which utilizes global semantic features of the image to locate semantically inconsistent regions and then generates reasonable content in the areas, is proposed. Specifically, the representation differences between inconsistent and available content are first amplified, iteratively predicting the region to be restored from coarse to fine. A confidence-driven inpainting network based on prediction masks is then used to estimate the information regarding missing regions. Furthermore, a multiscale contextual aggregation module is introduced for spatial feature transfer to refine the generated contents. Extensive experiments over multiple datasets demonstrate that the proposed method can generate visually plausible and structurally complete results that are particularly effective in recovering diverse degraded images.