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Ti 1−x O 3(1−x)-xBaTiO 3 ceramics with an excess in Bi 3+ and/or a deficiency in Na + were prepared and investigated. It is found that an antiferroelectric phase can be induced through a modulation of the mole ratio of Na + and Bi 3+. A phase boundary between ferroelectric and antiferroelectric phases can be observed at ambient temperature. A modulated phase, which is the origin of relaxor antiferroelectric behavior, should be attributed to a compositional modulation. The antiferroelectric phase can be induced to the ferroelectric phase by an applied electric field. The stability of the induced ferroelectric phase strongly depends on the mole ratio of Na + and Bi 3+. A recoverable giant strain of 0.48% comparable to PbZrO 3-based antiferroelectrics as well as electrostrictive coefficients (0.026 C 4 m −2) much higher than lead-based relaxor ferroelectrics with low-temperature dependence was achieved in (Na y ,Bi z)Ti 1−x O 3(1−x)-xBaTiO 3 antiferroelectrics. Our results show there is a high possibility that the novel lead-free antiferroelectrics will replace the PbZrO 3-based ones.

Optical machine learning has emerged as an important research area that, by leveraging the advantages inherent to optical signals, such as parallelism and high speed, paves the way for a future where optical hardware can process data at the speed of light. In this work, we present such optical devices for data processing in the form of single-layer nanoscale holographic perceptrons trained to perform optical inference tasks. We experimentally show the functionality of these passive optical devices in the example of decryptors trained to perform optical inference of single or whole classes of keys through symmetric and asymmetric decryption. The decryptors, designed for operation in the near-infrared region, are nanoprinted on complementary metal-oxide–semiconductor chips by galvo-dithered two-photon nanolithography with axial nanostepping of 10 nm1,2, achieving a neuron density of >500 million neurons per square centimetre. This power-efficient commixture of machine learning and on-chip integration may have a transformative impact on optical decryption3, sensing4, medical diagnostics5 and computing6,7.