This review compares organic–inorganic halide perovskites and ferroelectric oxide perovskites, both of which are emerging as new promising photovoltaic materials.

Ferroelectric random access memory (FeRAM) based on conventional ferroelectric perovskites, such as Pb(Zr,Ti)O₃ and SrBi₂Ta₂O₉, has encountered bottlenecks on memory density and cost, because those conventional perovskites suffer from various issues mainly including poor complementary metal-oxide-semiconductor (CMOS)-compatibility and limited scalability. Next-generation cost-efficient, high-density FeRAM shall therefore rely on a material revolution. Since the discovery of ferroelectricity in Si:HfO₂ thin films in 2011, HfO₂-based materials have aroused widespread interest in the field of FeRAM, because they are CMOS-compatible and can exhibit robust ferroelectricity even when the film thickness is scaled down to below 10 nm. A review on this new class of ferroelectric materials is therefore of great interest. In this paper, the most appealing topics about ferroelectric HfO₂-based materials including origins of ferroelectricity, advantageous material properties, and current and potential applications in FeRAM, are briefly reviewed.

Nowadays the development of machine vision is oriented toward real-time applications such as autonomous driving. This demands a hardware solution with low latency, high energy efficiency, and good reliability. Here, we demonstrate a robust and self-powered in-sensor computing paradigm with a ferroelectric photosensor network (FE-PS-NET). The FE-PS-NET, constituted by ferroelectric photosensors (FE-PSs) with tunable photoresponsivities, is capable of simultaneously capturing and processing images. In each FE-PS, self-powered photovoltaic responses, modulated by remanent polarization of an epitaxial ferroelectric Pb(Zr_0.2Ti_0.8)O₃ layer, show not only multiple nonvolatile levels but also sign reversibility, enabling the representation of a signed weight in a single device and hence reducing the hardware overhead for network construction. With multiple FE-PSs wired together, the FE-PS-NET acts on its own as an artificial neural network. In situ multiply-accumulate operation between an input image and a stored photoresponsivity matrix is demonstrated in the FE-PS-NET. Moreover, the FE-PS-NET is faultlessly competent for real-time image processing functionalities, including binary classification between ‘X’ and ‘T’ patterns with 100% accuracy and edge detection for an arrow sign with an F-Measure of 1 (under 365 nm ultraviolet light). This study highlights the great potential of ferroelectric photovoltaics as the hardware basis of real-time machine vision.