The interplay between structure and function in redox-based resistance switching.

We report a study of the relationship between oxide microstructure at the scale of tens of nanometres and resistance switching behaviour in silicon oxide. In the case of sputtered amorphous oxides, the presence of columnar structure enables efficient resistance switching by providing an initial structured distribution of defects that can act as precursors for the formation of chains of conductive oxygen vacancies under the application of appropriate electrical bias. Increasing electrode interface roughness decreases electroforming voltages and reduces the distribution of switching voltages. Any contribution to these effects from field enhancement at rough interfaces is secondary to changes in oxide microstructure templated by interface structure.

Flexible and fluorophore-doped luminescent solar concentrators based on polydimethylsiloxane.

We demonstrate a simple and inexpensive method to fabricate flexible and fluorophore-doped luminescent solar concentrators (LSCs). Polydimethylsiloxane (PDMS) serves as a host material which additionally offers the potential to cast LSCs in arbitrary shapes. The laser dye Pyrromethene 567 is used as a prototype fluorophore, and it is shown that it has a high quantum yield of 93% over the concentration range investigated. The optical efficiency and loss channels of the flexible LSCs are investigated; it is also demonstrated that the efficiency remains high while bending the LSC which is essential for flexible LSCs to make an impact on solar energy.

Hardware implementation of memristor-based artificial neural networks.

Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units operating in parallel. The low communication bandwidth between memory and processing units in conventional von Neumann machines does not support the requirements of emerging applications that rely extensively on large sets of data. More recent computing paradigms, such as high parallelization and near-memory computing, help alleviate the data communication bottleneck to some extent, but paradigm- shifting concepts are required. Memristors, a novel beyond-complementary metal-oxide-semiconductor (CMOS) technology, are a promising choice for memory devices due to their unique intrinsic device-level properties, enabling both storing and computing with a small, massively-parallel footprint at low power. Theoretically, this directly translates to a major boost in energy efficiency and computational throughput, but various practical challenges remain. In this work we review the latest efforts for achieving hardware-based memristive artificial neural networks (ANNs), describing with detail the working principia of each block and the different design alternatives with their own advantages and disadvantages, as well as the tools required for accurate estimation of performance metrics. Ultimately, we aim to provide a comprehensive protocol of the materials and methods involved in memristive neural networks to those aiming to start working in this field and the experts looking for a holistic approach.

Efficiency and loss mechanisms of plasmonic Luminescent Solar Concentrators.

Using a hybrid nanoscale/macroscale model, we simulate the efficiency of a luminescent solar concentrator (LSC) which employs silver nanoparticles to enhance the dye absorption and scatter the incoming light. We show that the normalized optical efficiency can be increased from 10.4% for a single dye LSC to 32.6% for a plasmonic LSC with silver spheres immersed inside a thin dye layer. Most of the efficiency enhancement is due to scattering of the particles and not due to dye absorption/re-emission.

Structural factors impacting carrier transport and electroluminescence from Si nanocluster-sensitized Er ions.

We present an analysis of factors influencing carrier transport and electroluminescence (EL) at 1.5 µm from erbium-doped silicon-rich silica (SiOx) layers. The effects of both the active layer thickness and the Si-excess content on the electrical excitation of erbium are studied. We demonstrate that when the thickness is decreased from a few hundred to tens of nanometers the conductivity is greatly enhanced. Carrier transport is well described in all cases by a Poole-Frenkel mechanism, while the thickness-dependent current density suggests an evolution of both density and distribution of trapping states induced by Si nanoinclusions. We ascribe this observation to stress-induced effects prevailing in thin films, which inhibit the agglomeration of Si atoms, resulting in a high density of sub-nm Si inclusions that induce traps much shallower than those generated by Si nanoclusters (Si-ncs) formed in thicker films. There is no direct correlation between high conductivity and optimized EL intensity at 1.5 µm. Our results suggest that the main excitation mechanism governing the EL signal is impact excitation, which gradually becomes more efficient as film thickness increases, thanks to the increased segregation of Si-ncs, which in turn allows more efficient injection of hot electrons into the oxide matrix. Optimization of the EL signal is thus found to be a compromise between conductivity and both number and degree of segregation of Si-ncs, all of which are governed by a combination of excess Si content and sample thickness. This material study has strong implications for many electrically-driven devices using Si-ncs or Si-excess mediated EL.

Electrically tailored resistance switching in silicon oxide.

Resistive switching in a metal-free silicon-based material offers a compelling alternative to existing metal oxide-based resistive RAM (ReRAM) devices, both in terms of ease of fabrication and of enhanced device performance. We report a study of resistive switching in devices consisting of non-stoichiometric silicon-rich silicon dioxide thin films. Our devices exhibit multi-level switching and analogue modulation of resistance as well as standard two-level switching. We demonstrate different operational modes that make it possible to dynamically adjust device properties, in particular two highly desirable properties: nonlinearity and self-rectification. This can potentially enable high levels of device integration in passive crossbar arrays without causing the problem of leakage currents in common line semi-selected devices. Aspects of conduction and switching mechanisms are discussed, and scanning tunnelling microscopy (STM) measurements provide a more detailed insight into both the location and the dimensions of the conductive filaments.

Nonideality-Aware Training for Accurate and Robust Low-Power Memristive Neural Networks.

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-1 W-1 to 381 TOPs-1 W-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.

Electrically pumped silicon waveguide light sources.

We report simulations of electrically pumped waveguide emitters in which the emissive layer contains silicon nanoclusters and erbium ions. Plasmonic coupling to metallic or semi-metallic overlayers provides enhancement of the radiative rate of erbium ions, enabling high quantum efficiency emission. Using 2D and 3D finite difference time domain (FDTD) simulations we show that up to 75% of the light emitted from the active layer can be coupled into a nanowire silicon rib waveguide. Our results suggest that such devices, which can readily be fabricated using CMOS processing techniques, pave the way for viable waveguide optical sources to be realized in silicon photonics.

Standards for the Characterization of Endurance in Resistive Switching Devices.

Resistive switching (RS) devices are emerging electronic components that could have applications in multiple types of integrated circuits, including electronic memories, true random number generators, radiofrequency switches, neuromorphic vision sensors, and artificial neural networks. The main factor hindering the massive employment of RS devices in commercial circuits is related to variability and reliability issues, which are usually evaluated through switching endurance tests. However, we note that most studies that claimed high endurances >106 cycles were based on resistance versus cycle plots that contain very few data points (in many cases even <20), and which are collected in only one device. We recommend not to use such a characterization method because it is highly inaccurate and unreliable (i.e., it cannot reliably demonstrate that the device effectively switches in every cycle and it ignores cycle-to-cycle and device-to-device variability). This has created a blurry vision of the real performance of RS devices and in many cases has exaggerated their potential. This article proposes and describes a method for the correct characterization of switching endurance in RS devices; this method aims to construct endurance plots showing one data point per cycle and resistive state and combine data from multiple devices. Adopting this recommended method should result in more reliable literature in the field of RS technologies, which should accelerate their integration in commercial products.

Modification of the Er3+ radiative lifetime from proximity to silicon nanoclusters in silicon-rich silicon oxide.

The lifetime of Er3+ in silicon-rich silicon oxide has been reported with quite widely varying values ranging from 9 ms to 2 ms. In this work, we consider the direct impact of silicon nanoclusters on the erbium radiative lifetime, and show that it is a function of the silicon nanocluster size, and also the erbium proximity to the nanocluster.

Memristor-Based Edge Detection for Spike Encoded Pixels.

Memristors have many uses in machine learning and neuromorphic hardware. From memory elements in dot product engines to replicating both synapse and neuron wall behaviors, the memristor has proved a versatile component. Here we demonstrate an analog mode of operation observed in our silicon oxide memristors and apply this to the problem of edge detection. We demonstrate how a potential divider exploiting this analog behavior can prove a scalable solution to edge detection. We confirm its behavior experimentally and simulate its performance on a standard testbench. We show good performance comparable to existing memristor based work with a benchmark score of 0.465 on the BSDS500 dataset, while simultaneously maintaining a lower component count.

Simulation of Inference Accuracy Using Realistic RRAM Devices.

Resistive Random Access Memory (RRAM) is a promising technology for power efficient hardware in applications of artificial intelligence (AI) and machine learning (ML) implemented in non-von Neumann architectures. However, there is an unanswered question if the device non-idealities preclude the use of RRAM devices in this potentially disruptive technology. Here we investigate the question for the case of inference. Using experimental results from silicon oxide (SiO x ) RRAM devices, that we use as proxies for physical weights, we demonstrate that acceptable accuracies in classification of handwritten digits (MNIST data set) can be achieved using non-ideal devices. We find that, for this test, the ratio of the high- and low-resistance device states is a crucial determinant of classification accuracy, with ~96.8% accuracy achievable for ratios >3, compared to ~97.3% accuracy achieved with ideal weights. Further, we investigate the effects of a finite number of discrete resistance states, sub-100% device yield, devices stuck at one of the resistance states, current/voltage non-linearities, programming non-linearities and device-to-device variability. Detailed analysis of the effects of the non-idealities will better inform the need for the optimization of particular device properties.

Sensing and Discrimination of Explosives at Variable Concentrations with a Large-Pore MOF as Part of a Luminescent Array.

Metal-organic frameworks (MOFs) have shown great promise for sensing of dangerous chemicals, including environmental toxins, nerve agents, and explosives. However, challenges remain, such as the sensing of larger analytes and the discrimination between similar analytes at different concentrations. Herein, we present the synthesis and development of a new, large-pore MOF for explosives sensing and demonstrate its excellent sensitivity against a range of relevant explosive compounds including trinitrotoluene and pentaerythritol tetranitrate. We have developed an improved, thorough methodology to eliminate common sources of error in our sensing protocol. We then combine this new MOF with two others as part of a three-MOF array for luminescent sensing and discrimination of five explosives. This sensor works at part-per-million concentrations and, importantly, can discriminate explosives with high accuracy without reference to their concentration.

Spike-Timing Dependent Plasticity in Unipolar Silicon Oxide RRAM Devices.

Resistance switching, or Resistive RAM (RRAM) devices show considerable potential for application in hardware spiking neural networks (neuro-inspired computing) by mimicking some of the behavior of biological synapses, and hence enabling non-von Neumann computer architectures. Spike-timing dependent plasticity (STDP) is one such behavior, and one example of several classes of plasticity that are being examined with the aim of finding suitable algorithms for application in many computing tasks such as coincidence detection, classification and image recognition. In previous work we have demonstrated that the neuromorphic capabilities of silicon-rich silicon oxide (SiOx) resistance switching devices extend beyond plasticity to include thresholding, spiking, and integration. We previously demonstrated such behaviors in devices operated in the unipolar mode, opening up the question of whether we could add plasticity to the list of features exhibited by our devices. Here we demonstrate clear STDP in unipolar devices. Significantly, we show that the response of our devices is broadly similar to that of biological synapses. This work further reinforces the potential of simple two-terminal RRAM devices to mimic neuronal functionality in hardware spiking neural networks.

Investigation of resistance switching in SiO x RRAM cells using a 3D multi-scale kinetic Monte Carlo simulator.

We employ an advanced three-dimensional (3D) electro-thermal simulator to explore the physics and potential of oxide-based resistive random-access memory (RRAM) cells. The physical simulation model has been developed recently, and couples a kinetic Monte Carlo study of electron and ionic transport to the self-heating phenomenon while accounting carefully for the physics of vacancy generation and recombination, and trapping mechanisms. The simulation framework successfully captures resistance switching, including the electroforming, set and reset processes, by modeling the dynamics of conductive filaments in the 3D space. This work focuses on the promising yet less studied RRAM structures based on silicon-rich silica (SiO x ) RRAMs. We explain the intrinsic nature of resistance switching of the SiO x layer, analyze the effect of self-heating on device performance, highlight the role of the initial vacancy distributions acting as precursors for switching, and also stress the importance of using 3D physics-based models to capture accurately the switching processes. The simulation work is backed by experimental studies. The simulator is useful for improving our understanding of the little-known physics of SiO x resistive memory devices, as well as other oxide-based RRAM systems (e.g. transition metal oxide RRAMs), offering design and optimization capabilities with regard to the reliability and variability of memory cells.

Controlling and modelling the wetting properties of III-V semiconductor surfaces using re-entrant nanostructures.

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.

Silicon Oxide (SiOx ): A Promising Material for Resistance Switching?

Interest in resistance switching is currently growing apace. The promise of novel high-density, low-power, high-speed nonvolatile memory devices is appealing enough, but beyond that there are exciting future possibilities for applications in hardware acceleration for machine learning and artificial intelligence, and for neuromorphic computing. A very wide range of material systems exhibit resistance switching, a number of which-primarily transition metal oxides-are currently being investigated as complementary metal-oxide-semiconductor (CMOS)-compatible technologies. Here, the case is made for silicon oxide, perhaps the most CMOS-compatible dielectric, yet one that has had comparatively little attention as a resistance-switching material. Herein, a taxonomy of switching mechanisms in silicon oxide is presented, and the current state of the art in modeling, understanding fundamental switching mechanisms, and exciting device applications is summarized. In conclusion, silicon oxide is an excellent choice for resistance-switching technologies, offering a number of compelling advantages over competing material systems.