The electrical conductivity of lithium niobate thin film capacitor structures depends on the density of conducting 180[Formula: see text] domain walls, which traverse the interelectrode gap, and on their inclination angle with respect to the polarization axis. Both microstructural characteristics can be altered by applying electric fields, but changes are time-dependent and relax, upon field removal, into a diverse range of remanent states. As a result, the measured conductance is a complex history-dependent function of electric field and time. Here, we show that complexity in the kinetics of microstructural change, in this ferroelectric system, can generate transport behavior that is strongly reminiscent of that seen in key neurological building blocks, such as synapses. Successive voltage pulses, of positive and negative polarity, progressively enhance or suppress domain wall related conductance (analogous to synaptic potentiation and depression), in a way that depends on both the pulse voltage magnitude and frequency. Synaptic spike-rate-dependent plasticity and even Ebbinghaus forgetting behavior, characteristic of learning and memory in the brain, can be emulated as a result. Conductance can also be changed according to the time difference between designed identical voltage pulse waveforms, applied to top and bottom contact electrodes, in a way that can mimic both Hebbian and anti-Hebbian spike-timing-dependent plasticity in synapses. While such features have been seen in, and developed for, other kinds of memristors, few have previously been realized through the manipulation of conducting ferroelectric domain walls.

Fundamentally, lithium niobate is a good electrical insulator. However, this can change dramatically when 180°domain walls are present, as they are often found to be strongly conducting. Conductivities depend on the inclination angles of walls with respect to the polarization axis and so, if these angles can be altered, then electrical conduction can be tuned, or toggled on and off. In ≈500 nm thick z-cut ion-sliced thin films, localized wall angle variations can be controlled by both the sense and magnitude of applied electrical bias. It is shown that this results in diode-like behaviour, allowing half-wave rectification at modest frequencies. Importantly, it is experimentally demonstrated that these domain wall diodes can be used to construct "AND" and inclusive "OR" logic gates, where "0" and "1" output states are clearly distinguishable. Extrapolation to more complex arrangements shows that output states can still be distinguished in two-level cascade logic. Insights show that simple logic circuits can be realized by localized manipulation of domain wall conductivity. Our research complements that by Jie Sun et al. (Adv. Funct. Mater. 2207418 (2022)), where NOT, NOR, and NAND gates are realized by moving conducting domain walls to make or break electrical contacts.

The increasing volume of smart edge devices, like smart cameras, and the growing amount of data to treat incited the development of light edge Artificial Intelligence (AI) solutions with neuromorphic computing. Oscillatory Neural Network (ONN) is a promising neuromorphic computing approach which uses networks of coupled oscillators, and their inherent parallel synchronization to compute. Also, ONN phase computing allows to limit voltage amplitude and reduce power consumption. Low-power, fast, and parallel computation properties make ONN attractive for edge AI. In state-of-theart, ONN is built with a fully-connected architecture, with coupling defined from unsupervised learning to perform autoassociative memory tasks, like with Hopfield Networks. However, to allow ONN to solve beyond associative memory applications, there is a need to explore further ONN architectures. In this work, we propose a novel architecture of cascaded analog fully-connected ONNs interconnected with an analog feedforward majority gate layer. In particular, we show this architecture can solve image edge detection task using two fully-connected ONN layers. This is, to our best knowledge, a first analog-based solution to cascade two fully-connected ONNs.