Managing Growth and Dimensionality of Quasi 2D Perovskite Single-Crystalline Flakes for Tunable Excitons Orientation.

Hybrid perovskites are among the most promising materials for optoelectronic applications. Their 2D crystalline form is even more interesting since the alternating inorganic and organic layers naturally forge a multiple quantum-well structure, leading to the formation of stable excitonic resonances. Nevertheless, a controlled modulation of the quantum well width, which is defined by the number of inorganic layers (n) between two organic ones, is not trivial and represents the main synthetic challenge in the field. Here, a conceptually innovative approach to easily tune n in lead iodide perovskite single-crystalline flakes is presented. The judicious use of potassium iodide is found to modulate the supersaturation levels of the precursors solution without being part of the final products. This allows to obtain a fine tuning of the n value. The excellent optical quality of the as synthesized flakes guarantees an in-depth analysis by Fourier-space microscopy, revealing that the excitons orientation can be manipulated by modifying the number of inorganic layers. Excitonic out-of-plane component, indeed, is enhanced when "n" is increased. The combined advances in the synthesis and optical characterization fill in the picture of the exciton behavior in low-dimensional perovskite, paving the way to the design of materials with improved optoelectronic characteristics.

Dynamics of a Vortex Lattice in an Expanding Polariton Quantum Fluid.

If a quantum fluid is driven with enough angular momentum, at equilibrium the ground state of the system is given by a lattice of quantized vortices whose density is prescribed by the quantization of circulation. We report on the first experimental study of the Feynman-Onsager relation in a nonequilibrium polariton fluid, free to expand and rotate. Upon initially imprinting a lattice of vortices in the quantum fluid, we track the vortex core positions on picosecond timescales. We observe an accelerated stretching of the lattice and an outward bending of the linear trajectories of the vortices, due to the repulsive polariton interactions. Access to the full density and phase fields allows us to detect a small deviation from the Feynman-Onsager rule in terms of a transverse velocity component, due to the density gradient of the fluid envelope acting on the vortex lattice.

Polaritonic Neuromorphic Computing Outperforms Linear Classifiers.

Machine learning software applications are ubiquitous in many fields of science and society for their outstanding capability to solve computationally vast problems like the recognition of patterns and regularities in big data sets. In spite of these impressive achievements, such processors are still based on the so-called von Neumann architecture, which is a bottleneck for faster and power-efficient neuromorphic computation. Therefore, one of the main goals of research is to conceive physical realizations of artificial neural networks capable of performing fully parallel and ultrafast operations. Here we show that lattices of exciton-polariton condensates accomplish neuromorphic computing with outstanding accuracy thanks to their high optical nonlinearity. We demonstrate that our neural network significantly increases the recognition efficiency compared with the linear classification algorithms on one of the most widely used benchmarks, the MNIST problem, showing a concrete advantage from the integration of optical systems in neural network architectures.