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.

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.

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.

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.

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.

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.

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.

Nanoscale Transformations in Metastable, Amorphous, Silicon-Rich Silica.

Electrically biasing thin films of amorphous, substoichiometric silicon oxide drives surprisingly large structural changes, apparent as density variations, oxygen movement, and ultimately, emission of superoxide ions. Results from this fundamental study are directly relevant to materials that are increasingly used in a range of technologies, and demonstrate a surprising level of field-driven local reordering of a random oxide network.

Emulating the Electrical Activity of the Neuron Using a Silicon Oxide RRAM Cell.

In recent years, formidable effort has been devoted to exploring the potential of Resistive RAM (RRAM) devices to model key features of biological synapses. This is done to strengthen the link between neuro-computing architectures and neuroscience, bearing in mind the extremely low power consumption and immense parallelism of biological systems. Here we demonstrate the feasibility of using the RRAM cell to go further and to model aspects of the electrical activity of the neuron. We focus on the specific operational procedures required for the generation of controlled voltage transients, which resemble spike-like responses. Further, we demonstrate that RRAM devices are capable of integrating input current pulses over time to produce thresholded voltage transients. We show that the frequency of the output transients can be controlled by the input signal, and we relate recent models of the redox-based nanoionic resistive memory cell to two common neuronal models, the Hodgkin-Huxley (HH) conductance model and the leaky integrate-and-fire model. We employ a simplified circuit model to phenomenologically describe voltage transient generation.

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.

Electrospray synthesis and properties of hierarchically structured PLGA TIPS microspheres for use as controlled release technologies.

Microsphere-based controlled release technologies have been utilized for the long-term delivery of proteins, peptides and antibiotics, although their synthesis poses substantial challenges owing to formulation complexities, lack of scalability, and cost. To address these shortcomings, we used the electrospray process as a reproducible, synthesis technique to manufacture highly porous (>94%) microspheres while maintaining control over particle structure and size. Here we report a successful formulation recipe used to generate spherical poly(lactic-co-glycolic) acid (PLGA) microspheres using the electrospray (ES) coupled with a novel thermally induced phase separation (TIPS) process with a tailored Liquid Nitrogen (LN2) collection scheme. We show how size, shape and porosity of resulting microspheres can be controlled by judiciously varying electrospray processing parameters and we demonstrate examples in which the particle size (and porosity) affect release kinetics. The effect of electrospray treatment on the particles and their physicochemical properties are characterized by scanning electron microscopy, confocal Raman microscopy, thermogravimetric analysis and mercury intrusion porosimetry. The microspheres manufactured here have successfully demonstrated long-term delivery (i.e. 1week) of an active agent, enabling sustained release of a dye with minimal physical degradation and have verified the potential of scalable electrospray technologies for an innovative TIPS-based microsphere production protocol.

Conductance tomography of conductive filaments in intrinsic silicon-rich silica RRAM.

We present results from an imaging study of filamentary conduction in silicon suboxide resistive RAM devices. We used a conductive atomic force microscope to etch through devices while measuring current, allowing us to produce tomograms of conductive filaments. To our knowledge this is the first report of such measurements in an intrinsic resistance switching material.

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.