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Lead-free antiferroelectric silver niobate AgNbO 3 ceramics have attracted growing interest for their superior energy storage density. The ceramics are commonly prepared by the conventional solid-state reaction method using Ag 2 O and Nb 2 O 5 as the raw materials. An O 2 atmosphere is maintained to prevent the possible decomposition of Ag 2 O during sintering, which increases the complexity of the process. In this work, we prepared successfully the pure phase AgNbO 3 ceramics in air with the hydrothermally synthesized AgNbO 3 powders. In comparison with the ceramics prepared by the conventional solid-state reaction method, the ceramics prepared by the hydrothermally synthesized powders have a broader antiferroelectric phase region and show pinched polarization-electric field hysteresis loops at moderate electric fields. [ABSTRACT FROM AUTHOR]

A neuromorphic visual system integrating optoelectronic synapses to perform the in‐sensor computing is triggering a revolution due to the reduction of latency and energy consumption. Here it is demonstrated that the dwell time of photon‐generated carriers in the space‐charge region can be effectively extended by embedding a potential well on the shoulder of Schottky energy barrier. It permits the nonlinear interaction of photocurrents stimulated by spatiotemporal optical signals, which is necessary for in‐sensor reservoir computing (RC). The machine vision with the sensor reservoir constituted by designed self‐powered Au/P(VDF‐TrFE)/Cs2AgBiBr6/ITO devices is competent for both static and dynamic vision tasks. It shows an accuracy of 99.97% for face classification and 100% for dynamic vehicle flow recognition. The in‐sensor RC system takes advantage of near‐zero energy consumption in the reservoir, resulting in decades‐time lower training costs than a conventional neural network. This work paves the way for ultralow‐power machine vision using photonic devices. [ABSTRACT FROM AUTHOR]

B Ultralow-Power Machine Vision b In article number 2106092, Bobo Tian, Hui Peng, Chungang Duan, and co-workers demonstrate that photovoltaic sensors can be used for in-sensor reservoir computing (RC). Both image processing and dynamic video analysis are energy-efficiently achieved with a high accuracy of 99.67% for face classification and 100% for dynamic vehicle flow recognition. Brain-inspired vision is achieved by in-sensor RC based on self-powered Cs SB 2 sb AgBiBr SB 6 sb photontic devices. [Extracted from the article]