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Silica thin films with vertical nanopores are useful to control access to electrode surfaces and may act as templates for growth of nanomaterials. The most effective method to produce these films, electrochemically assisted surfactant assembly, also produces aggregates of silica particles. This paper shows that growth with an AC signal superimposed onto the potential avoids the aggregates and only very small numbers of single particles are found. This finding is linked to better control of the diffusion field of hydroxide ions that are responsible for particle growth. The resultant films are smooth, with very well-ordered hexagonal pore structures.\ud \ud

The square pyramidal WSeCl4 monomer and its complexes with neutral donor ligands are described, including [WSeCl4(SenBu2)], which is a single source CVD precursor for highly oriented WSe2 thin films.

Memristors are emerging as promising candidates for practical application in reservoir computing systems that are capable of temporal information processing. Here, we experimentally implement a physical reservoir computing system using resistive memristors based on three-dimensional (3D)-structured mesoporous silica (mSiO2) thin films fabricated by a low cost, fast and vacuum-free sol–gel technique. The in situ learning capability and a classification accuracy of 100% on a standard machine learning dataset are experimentally demonstrated. The volatile (temporal) resistive switching in diffusive memristors arises from the formation and subsequent spontaneous rupture of conductive filaments via diffusion of Ag species within the 3D-structured nanopores of the mSiO2 thin film. Besides volatile switching, the devices also exhibit a bipolar non-volatile resistive switching behavior when the devices are operated at a higher compliance current level. The implementation of mSiO2 thin films opens the route to fabricate a simple and low cost dynamic memristor with a temporal information process functionality, which is essential for neuromorphic computing applications.

Two‐terminal memristor has emerged as one of the most promising neuromorphic artificial electronic devices for their structural resemblance to biological synapses and ability to emulate many synaptic functions. In this work, a memristor based on the back‐end‐of‐line (BEOL) material silicon carbide (SiC) is developed. The thin film memristors demonstrate excellent binary resistive switching with compliance‐free and self‐rectifying characteristics which are advantageous for the implementation of high‐density 3D crossbar memory architectures. The conductance of this SiC‐based memristor can be modulated gradually through the application of both DC and AC signals. This behavior is demonstrated to further emulate several vital synaptic functions including paired‐pulse facilitation (PPF), post‐tetanic potentiation (PTP), short‐term potentiation (STP), and spike‐rate‐dependent plasticity (SRDP). The synaptic function of learning‐forgetting‐relearning processes is successfully emulated and demonstrated using a 3 × 3 artificial synapse array. This work presents an important advance in SiC‐based memristor and its application in both memory and neuromorphic computing.

We report on the development of hybrid organic−inorganic material-based flexible memristor devices made by a fast and simple electrochemical fabrication method. The devices consist of a bilayer of poly(methyl methacrylate) (PMMA) and Te-rich GeSbTe chalcogenide nanoscale thin films sandwiched between Ag top and TiN bottom electrodes on both Si and flexible polyimide substrates. These hybrid memristors require no electroforming process and exhibit reliable and reproducible bipolar resistive switching at low switching voltages under bothflat and bending conditions. Multistate switching behavior can also be achieved by controlling the compliance current (CC). We attribute the switching between the high resistance state (HRS) and low resistance state (LRS) in the devices to the formation and rupture of conductive Ag filaments within the hybrid PMMA/GeSbTe matrix. This work provides a promising route to fabricate flexible memory devices through an electrodeposition process for application in flexible electronics.

In this paper, an atomic layer deposited memristor based on Al2O3/MoO3 bi-layer structure is reported. Compared with the single layer MoO3 based device, the bi-layer memristor demonstrates the improved bipolar switching behaviors including better endurance, higher ON/OFF ratio, and higher uniformity of the programming voltages. Space-charge-limited current (SCLC) model is used to explain the current conduction in our memristor. The formation and rupture of conductive oxygen vacancy-based filaments are illustrated as the proposed mechanism for the observed resistive switching behavior.

The “one-to-many” problem is a typical challenge that faced by many machine learning aided inverse nanophotonics designs where one target optical response can be achieved by many solutions (designs). Although novel training approaches, such as tandem network, and network architecture, such as the mixture density model, have been proposed, the critical problem of solution degeneracy still exists where some possible solutions or solution spaces are discarded or unreachable during the network training process. Here, we report a solution to the “one-to-many” problem by employing a conditional generative adversarial network (cGAN) that enables generating sets of multiple solution groups to a design problem. Using the inverse design of a transmissive Fabry–Pérot-cavity-based color filter as an example, our model demonstrates the capability of generating an average number of 3.58 solution groups for each color. These multiple solutions allow the selection of the best design for each color which results in a record high accuracy with an average index color difference ΔE of 0.44. The capability of identifying multiple solution groups can benefit the design manufacturing to allow more viable designs for fabrication. The capability of our cGAN is verified experimentally by inversely designing the RGB color filters. We envisage this cGAN-based design methodology can be applied to other nanophotonic structures or physical science domains where the identification of multi-solution across a vast parameter space is required.

The combination of lithographic methods and sol gel bottom-up techniques is a promising approach for nanopatterning substrates. The integration and scalable fabrication of such substrates are of great interest for the development of nanowire-based materials opening potentialities in new technologies. We demonstrate the deposition of ordered mesoporous silica into nanopatterned silica substrates by dip coating. Using scanning electron microscopy and grazing incidence small angle X-ray scattering, the effect of the sol composition on the pore ordering was probed. Optimising the sol composition using anodic alumina membranes as confined spaces, we showed how the pH controlled the transformation from circular to columnar mesophase. Vertical mesopores were obtained with very good repeatability. The effect of the sol chemistry on the surfactant curvature was then shown to be similar in nanopatterned substrates made by e-beam lithography.

In this work, we report on the fabrication of resistive random-access memory cells based on electrodeposited GeSbTe material between TiN top and bottom electrodes in a crossbar architecture. The cells exhibit asymmetric bipolar resistive switching characteristics under the same SET and RESET compliance current (CC), showing highly uniform and reproducible switching properties. A multi-state switching behavior can be also achieved by varying the sweeping voltage and CC. Unlike phase-change switching, the switching between the high-resistance state and the low-resistance state in these cells can be attributed to the formation and rupture of conductive Te bridge(s) within the Te-rich GeSbTe matrix upon application of a high electric field. The results point toward the usage of the electrodeposition method to fabricate advanced functional device structures for application in non-volatile memory.

Dynamic structural color provides an additional time dimension in contrast to the static one, enriching the information load and functionalities. As a phase change material, vanadium dioxide offers great opportunities to implement dynamic structural color as its insulator-metal transition. Nevertheless, the multiple states also place a barrier to the efficient design of the structure configurations. This work firstly reports the dynamic structural color inverse design of asymmetric Fabry-Pérot cavity through a parameter-based conditional generative adversarial networks approach. The proposed structure attains a gamut as large as 117% of sRGB in the insulator state and can produce a 5% color coverage variation via the phase change of VO2 layer. By using the trained conditional generative adversarial networks, the inverse design accuracy with the average color difference ΔE of 0.98 is achieved. A monochromatic pattern is designed by the trained networks to demonstrate different color dynamics of the various structures.

The ever-increasing demand for renewable energy and zero carbon dioxide emission have been the driving force for the development of thermoelectric generators with better power generation performance. Alongside with the effort to discover thermoelectric materials with higher figure-of-merit, the geometrical and structural optimisation of thermoelectric generators are also essential for maximized power generation and efficiency. This work demonstrates for the first time the application of artificial neural network, a deep learning technique, in forward modelling the maximum power generation and efficiency of a thermoelectric generator and its application in the generator design and optimisation. After training using a dataset containing 5000 3-D finite element method based simulations, the artificial neural networks with 5 layers and 400 neurons per layer demonstrate extremely high prediction accuracy over 98% and are able to operate under both constant temperature difference and heat flux conditions while taking into account of the contact electrical resistance, surface heat transfer and other thermoelectric effects. Coupling with genetic algorithm, the trained artificial neural networks can optimise the leg height, leg width, fill factor and interconnect height of the thermoelectric generator for different operating and contact resistance conditions. With almost identical optimised values obtained, our neural networks can realise geometrical optimisation within 40 s for each operating condition, which is averagely over 1,000 times faster than the optimisation performed by finite element method. The up-front computational time for the neural network can be recovered when more than 2 optimisations are needed. The successful application of this data-driven approach in this work clearly represents a new and cost-effective avenue for conducting system level design and optimisation of thermoelectric generators and other energy harvesting technologies.