Wing Hung Ng

We present a resistance switching device that exhibits analogue potentiation and depression of conductance under the same voltage polarity. This contrasts with previously studied devices that potentiate and depress under opposite polarities. We refer to this mode of operation as the subthreshold regime due to it occurring at voltage or current biases that are insufficient to produce discrete or non-volatile switching. This behaviour has the potential to reduce the complexity of neuronal and synaptic circuitry in neuromorphic computing by removing the need for voltage pulses of both positive and negative polarities. The characteristically long timescales may also help replicate bio-realistic timings. In this paper, we detail how to induce this unique behaviour, how to tune its properties to a desired response, and finally, we demonstrate one potential application.

We report that implanting argon ions into a film of uniform atomic layer deposition (ALD)-grown SiO_x enables electroforming and switching within films that previously failed to electroform at voltages 6) cycles is achieved alongside low switching voltages (±1 V). Comparing SiO_x fabricated by this approach with sputtered SiO_x we find similar conduction mechanisms between the two materials. Our results show that intrinsic SiO_x switching can be achieved with defects created solely by argon bombardment; in contrast to defects generated during deposition, implantation generated defects are potentially more controllable. In the future, noble ion implantation into silicon oxide may allow optimization of already excellent resistance switching devices.

Recent years have seen a rapid rise of artificial neural networks being employed in a number of cognitive tasks. The ever‐increasing computing requirements of these structures have contributed to a desire for novel technologies and paradigms, including memristor‐based hardware accelerators. Solutions based on memristive crossbars and analog data processing promise to improve the overall energy efficiency. However, memristor nonidealities can lead to the degradation of neural network accuracy, while the attempts to mitigate these negative effects often introduce design trade‐offs, such as those between power and reliability. In this work, authors design nonideality‐aware training of memristor‐based neural networks capable of dealing with the most common device nonidealities. The feasibility of using high‐resistance devices that exhibit high I‐V nonlinearity is demonstrated—by analyzing experimental data and employing nonideality‐aware training, it is estimated that the energy efficiency of memristive vector‐matrix multipliers is improved by almost three orders of magnitude (0.715 TOPs⁻¹W⁻¹ to 381 TOPs⁻¹W⁻¹) while maintaining similar accuracy. It is shown that associating the parameters of neural networks with individual memristors allows to bias these devices toward less conductive states through regularization of the corresponding optimization problem, while modifying the validation procedure leads to more reliable estimates of performance. The authors demonstrate the universality and robustness of this approach when dealing with a wide range of nonidealities.

Columnar microstructures are critical for obtaining good resistance switching properties in SiO_x resistive random access memory (ReRAM) devices. In this work, the formation and structure of columnar boundaries are studied in sputtered SiO_x layers. Using TEM measurements, we analyze SiO_x layers in Me–SiO_x–Mo heterostructures, where Me = Ti or Au/Ti. We show that the SiO_x layers are templated by the Mo surface roughness, leading to the formation of columnar boundaries protruding from troughs at the SiO_x/Mo interface. Electron energy-loss spectroscopy measurements show that these boundaries are best characterized as voids, which in turn facilitate Ti, Mo, and Au incorporation from the electrodes into SiO_x. Density functional theory calculations of a simple model of the SiO₂ grain boundary and column boundary show that O interstitials preferentially reside at the boundaries rather than in the SiO₂ bulk. The results elucidate the nature of the SiO_x microstructure and the complex interactions between the metal electrodes and the switching oxide, each of which is critically important for further materials engineering and the optimization of ReRAM devices.

The limited sensitivity of existing analysis techniques at the nanometer scale makes it challenging to systematically examine the complex interactions in redox-based resistive random access memory (ReRAM) devices. To test models of oxygen movement in ReRAM devices beyond what has previously been possible, we present a new nanoscale analysis method. Harnessing the power of secondary ion mass spectrometry, the most sensitive surface analysis technique, for the first time, we observe the movement of ¹⁶O across electrically biased SiO_x ReRAM stacks. We can therefore measure bulk concentration changes in a continuous profile with unprecedented sensitivity. This reveals the nanoscale details of the reversible field-driven exchange of oxygen across the ReRAM stack. Both the reservoir-like behavior of a Mo electrode and the injection of oxygen into the surface of SiO_x from the ambient are observed within one profile. The injection of oxygen is controllable through changing the porosity of the SiO_x layer. Modeling of the electric fields in the ReRAM stacks is carried out which, for the first time, uses real measurements of both the interface roughness and electrode porosity. This supports our findings helping to explain how and where oxygen from ambient moisture enters devices during operation.

Artificial neural networks are notoriously power- and time-consuming when implemented on conventional von Neumann computing systems. Consequently, recent years have seen an emergence of research in machine learning hardware that strives to bring memory and computing closer together. A popular approach is to realise artificial neural networks in hardware by implementing their synaptic weights using memristive devices. However, various device- and system-level non-idealities usually prevent these physical implementations from achieving high inference accuracy. We suggest applying a well-known concept in computer science—committee machines—in the context of memristor-based neural networks. Using simulations and experimental data from three different types of memristive devices, we show that committee machines employing ensemble averaging can successfully increase inference accuracy in physically implemented neural networks that suffer from faulty devices, device-to-device variability, random telegraph noise and line resistance. Importantly, we demonstrate that the accuracy can be improved even without increasing the total number of memristors.

We report a study of the relationship between oxide microstructure at the scale of tens of nanometres and resistance switching behaviour in silicon oxide.

Inorganic semiconductors such as III-V materials are very important in our everyday life as they are used for manufacturing optoelectronic and microelectronic components with important applications span from energy harvesting to telecommunications. In some applications, these components are required to operate in harsh environments. In these cases, having waterproofing capability is essential. Here we demonstrate design and control of the wettability of indium phosphide based multilayer material (InP/InGaAs/InP) using re-entrant structures fabricated by a fast electron beam lithography technique. This patterning technique enabled us to fabricate highly uniform nanostructure arrays with at least one order of magnitude shorter patterning times compared to conventional electron beam lithography methods. We reduced the surface contact fraction significantly such that the water droplets may be completely removed from our nanostructured surface. We predicted the wettability of our patterned surface by modelling the adhesion energies between the water droplet and both the patterned surface and the dispensing needle. This is very useful for the development of coating-free waterproof optoelectronic and microelectronic components where the coating may hinder the performance of such devices and cause problems with semiconductor fabrication compatibility.

In this paper, we present high performance resistance switching memory devices (RRAM) with an SiO$_{2}$-like active layer formed from spin-on hydrogen silsesquioxane (HSQ). Our metal-insulator-metal (MIM) devices exhibit switching voltages of less than 1 V, cycling endurances of more than 10$_{7}$ cycles without failure, electroforming below 2 V and retention time of resistance states of more than 10$_{5}$ seconds at room temperature. We also report arrays of nanoscale HSQ-based RRAM devices in the form of multilayer nanopillars with switching performance comparable to that of our thin film devices. We are able to address and program individual RRAM nanopillars using conductive atomic force microscopy. These promising results, coupled with a much easier fabrication method than traditional ultra-high vacuum based deposition techniques, make HSQ a strong candidate material for the next generation memory devices.