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Droplet epitaxy (DE) has emerged as an alternative to Stranski-Krastanov (SK) as a method for epitaxial nanostructure formation. We find significant structural differences of similar sized nanostructures embedded in GaAs between the two methods. Atomic force microscopy and atom probe tomography measurements reveal that uncapped and capped SK structures resemble each other. However, the DE nanostructures appear as rings topographically but are quantum dots compositionally. A GaSb wetting layer is present regardless of the growth method and shares a nearly identical Sb concentration profile. DE nanostructures are shown to have a lower Sb concentration, and transmission electron microscopy measurements reveal that they produce less strain on the capping layer. Despite significant structural differences, SK and DE nanostructures exhibit the same photoluminescence response, suggesting that the emission is from a shared feature such as the wetting layer, rather than the nanostructures. [ABSTRACT FROM AUTHOR]

Atomic force microscopy was used to investigate the effects of templating parameters on focused ion beam patterned single-, two-, and three-layer InAs/GaAs(001) quantum dot structures. The number of layers, focused ion beam dwell time, and pattern spacing affected the fidelity of the quantum dots. The highest single dot fidelities were found in regions with 1 and 3 ms dwell times and 1 and 2 μm pattern spacings. A two-layer region patterned with 1 ms dwell time and 1 μm spacing was found to have 100% single quantum dot fidelity with no off-site dot nucleation in a 20 × 20 μm2 scan. Holes that were milled with 6 and 9 ms dwell times and 0.25 μm spacing became faceted, that is, deep, tightly packed, and rhombic, by the third layer. Autocorrelation of the images was used to analyze the periodicity and size of the features. [ABSTRACT FROM AUTHOR]

Photonic reservoir computers (RC) come in single mode ring and multimode array geometries. We propose and simulate a photonic RC architecture using speckle in a multimode waveguide ring resonator that requires neither the ultra-high-speed analog-digital conversion nor the spatial light modulator used in other designs. We show that the equations for propagation around a multimode (MM) ring resonator along with an optical nonlinearity, and optical feedback can be cast exactly in the standard RC form with speckle mixing performing the pseudo-random matrix multiplications. The hyperparameters are the outcoupling efficiency, the nonlinearity saturation intensity, the input bias, and the waveguide properties. In particular, the number of waveguide modes is a measure of the number of effective neurons in the RC. Simulations show a ring using a strongly guiding 50-µm planar waveguide gives 206 effective neurons and excellent predictions of Mackey-Glass waveforms for a broad range of the hyperparameters, while a weakly guiding MM 200-µm diameter fiber gives 4,238 effective neurons and excellent predictions of chaotic solutions of the Kuramoto-Sivashinsky equation. We discuss physical realizations for implementing the system with a chip-scale device or with discrete components and a MM optical fiber.

Reservoir computing is a recurrent machine learning framework that expands the dimensionality of a problem by mapping an input signal into a higher-dimension reservoir space that can capture and predict features of complex, non-linear temporal dynamics. Here, we report on a bulk electro-optical demonstration of a reservoir computer using speckles generated by propagating a laser beam modulated with a spatial light modulator through a multimode waveguide. We demonstrate that the hardware can successfully perform a multivariate audio classification task performed using the Japanese vowel speakers public data set. We perform full wave optical calculations of this architecture implemented in a chip-scale platform using an SiO 2 waveguide and demonstrate that it performs as well as a fully numerical implementation of reservoir computing. As all the optical components used in the experiment can be fabricated using a commercial photonic integrated circuit foundry, our result demonstrates a framework for building a scalable, chip-scale, reservoir computer capable of performing optical signal processing.

The increasing demand for miniature autonomous sensors requires low cost integration methods, but to date, material limitations have prevented the direct growth of optically active III-V materials on CMOS devices. We report on the deposition of GaAs nanowires on polycrystalline conductive films to allow for direct integration of optoelectronic devices on dissimilar materials. Undoped, Si-doped, and Be-doped nanowires were grown at T s  = 400 °C on oxide (indium tin oxide) and metallic (platinum and titanium) films. Be-doping is shown to significantly reduce the nanowire diameter and improve the nanowire aspect ratio to 50:1. Photoluminescence measurements of Be-doped nanowires are 1-2 orders of magnitude stronger than undoped and Si-doped nanowires and have a thermal activation energy of 14 meV, which is comparable to nanowires grown on crystalline substrates. Electrical measurements confirm that the metal-semiconductor junction is Ohmic. These results demonstrate the feasibility of integrating nanowire-based optoelectronic devices directly on CMOS chips.