In this work, a detailed investigation is carried out on copper oxide, in the form of cupric oxide (CuO) nanocrystals. Particular attention is paid to the bandstructure and ultrafast charge-carrier dynamics. Transient absorption spectroscopy is carried out with an above-bandgap pump beam and below-bandgap probe beam to glean insight on the relaxation and recombination dynamics of the CuO nanocrystals at various pump fluences. Three time constants are apparent. The first time constant varies with pump fluence from 330 fs to 630 fs, and it is attributed to momentum relaxation via carrier-carrier scattering in the valence band as well as exciton-exciton annihilation. The second time constant is constant at 2 ps, and it is attributed to energy relaxation via carrier-phonon scattering within the valence band. The third time constant is constant at 50 ps, and it is attributed to trapping and recombination, due to the high density of trap states within the CuO nanocrystals. Such findings lay the foundation for future studies and applications of the emerging CuO material system.

All-optical switching is the foundation of emerging all-optical (terabit-per-second) networks and processors. All-optical switching has attracted considerable attention, but it must ultimately support operation with femtojoule switching energies and femtosecond switching times to be effective. Here we introduce an all-optical switch architecture in the form of a dielectric sphere that focuses a high-intensity photonic nanojet into a peripheral coating of semiconductor nanoparticles. Milli-scale spheres coated with Si and SiC nanoparticles yield switching energies of 200 and 100 fJ with switching times of 10 ps and 350 fs, respectively. Micro-scale spheres coated with Si and SiC nanoparticles yield switching energies of 1 pJ and 20 fJ with switching times of 2 ps and 270 fs, respectively. We show that femtojoule switching energies are enabled by localized photoinjection from the photonic nanojets and that femtosecond switching times are enabled by localized recombination within the semiconductor nanoparticles.

Implementing any linear transformation matrix through the optical channels of an on-chip reconfigurable multiport interferometer has been emerging as a promising technique for various fields of study, such as information processing and optical communication systems. Recently, the use of multiport optical interferometric-based linear structures in neural networks has attracted a great deal of attention. Optical neural networks have proven to be promising in terms of computational speed and power efficiency, allowing for the increasingly large neural networks that are being created today. This paper demonstrates the experimental analysis of programming a 4 × 4 reconfigurable optical processor using a unitary transformation matrix implemented by a single layer neural network. To this end, the Mach-Zehnder interferometers (MZIs) in the structure are first experimentally calibrated to circumvent the random phase errors originating from fabrication process variations. The linear transformation matrix of the given application can be implemented by the successive multiplications of the unitary transformation matrices of the constituent MZIs in the optical structure. The required phase shifts to construct the linear transformation matrix by means of the optical processor are determined theoretically. Using this method, a single layer neural network is trained to classify a synthetic linearly separable multivariate Gaussian dataset on a conventional computer using a stochastic optimization algorithm. Additionally, the effect of the phase errors and uncertainties caused by the experimental equipment inaccuracies and the device components imperfections is also analyzed and simulated. Finally, the optical processor is experimentally programmed by applying the obtained phase shifts from the matrix decomposition process to the corresponding phase shifters in the device. The experimental results show that the optical processor achieves 72% classification accuracy compared to the 98.9% of the simulated optical neural network on a digital computer.