Plasmonically-enhanced all-optical integrated phase-change memory.

Integrated phase-change photonic memory devices offer a novel route to non-volatile storage and computing that can be carried out entirely in the optical domain, obviating the necessity for time and energy consuming opto-electrical conversions. Such memory devices generally consist of integrated waveguide structures onto which are fabricated small phase-change memory cells. Switching these cells between their amorphous and crystalline states modifies significantly the optical transmission through the waveguide, so providing memory, and computing, functionality. To carry out such switching, optical pulses are sent down the waveguide, coupling to the phase-change cell, heating it up, and so switching it between states. While great strides have been made in the development of integrated phase-change photonic devices in recent years, there is always a pressing need for faster switching times, lower energy consumption and a smaller device footprint. In this work, therefore, we propose the use of plasmonic enhancement of the light-matter interaction between the propagating waveguide mode and the phase-change cell as a means to faster, smaller and more energy-efficient devices. In particular, we propose a form of plasmonic dimer nanoantenna of significantly sub-micron size that, in simulations, offers significant improvements in switching speeds and energies. Write/erase speeds in the range 2 to 20 ns and write/erase energies in the range 2 to 15 pJ were predicted, representing improvements of one to two orders of magnitude when compared to conventional device architectures.

SHIP: a computational framework for simulating and validating novel technologies in hardware spiking neural networks.

Investigations in the field of spiking neural networks (SNNs) encompass diverse, yet overlapping, scientific disciplines. Examples range from purely neuroscientific investigations, researches on computational aspects of neuroscience, or applicative-oriented studies aiming to improve SNNs performance or to develop artificial hardware counterparts. However, the simulation of SNNs is a complex task that can not be adequately addressed with a single platform applicable to all scenarios. The optimization of a simulation environment to meet specific metrics often entails compromises in other aspects. This computational challenge has led to an apparent dichotomy of approaches, with model-driven algorithms dedicated to the detailed simulation of biological networks, and data-driven algorithms designed for efficient processing of large input datasets. Nevertheless, material scientists, device physicists, and neuromorphic engineers who develop new technologies for spiking neuromorphic hardware solutions would find benefit in a simulation environment that borrows aspects from both approaches, thus facilitating modeling, analysis, and training of prospective SNN systems. This manuscript explores the numerical challenges deriving from the simulation of spiking neural networks, and introduces SHIP, Spiking (neural network) Hardware In PyTorch, a numerical tool that supports the investigation and/or validation of materials, devices, small circuit blocks within SNN architectures. SHIP facilitates the algorithmic definition of the models for the components of a network, the monitoring of states and output of the modeled systems, and the training of the synaptic weights of the network, by way of user-defined unsupervised learning rules or supervised training techniques derived from conventional machine learning. SHIP offers a valuable tool for researchers and developers in the field of hardware-based spiking neural networks, enabling efficient simulation and validation of novel technologies.

An integrated photonics engine for unsupervised correlation detection.

With more and more aspects of modern life and scientific tools becoming digitized, the amount of data being generated is growing exponentially. Fast and efficient statistical processing, such as identifying correlations in big datasets, is therefore becoming increasingly important, and this, on account of the various compute bottlenecks in modern digital machines, has necessitated new computational paradigms. Here, we demonstrate one such novel paradigm, via the development of an integrated phase-change photonics engine. The computational memory engine exploits the accumulative property of Ge2Sb2Te5 phase-change cells and wavelength division multiplexing property of optics in delivering fully parallelized and colocated temporal correlation detection computations. We investigate this property and present an experimental demonstration of identifying real-time correlations in data streams on the social media platform Twitter and high-traffic computing nodes in data centers. Our results demonstrate the use case of high-speed integrated photonics in accelerating statistical analysis methods.

Spectral dependence of nonlinear absorption in ordered silver metallic nanoprism arrays.

Ordered metallic nanoprism arrays have been proposed as novel and versatile systems for the observation of nonlinear effects such as nonlinear absorption. The study of the effect of the local field reinforcement on the fast optical third order nonlinear response around the Surface Plasmon Resonance is of great interest for many plasmonic applications. In this work, silver nanoprism arrays have been synthesized by the nanosphere lithography method. A low repetition rate tunable picosecond laser source was used to study the irradiance and wavelength dependence of the nonlinear absorption properties around the dipolar and quadrupolar resonances of the nanoarray with the use of the z-scan technique. The irradiance dependence of the on-resonance nonlinearity was studied, and a spectral region where nonlinear absorption is negligible was identified. This is important for the possible application of these materials in optical information processing devices.