Single nanowire lasers based on bottom-up III-V materials have been shown to exhibit room-temperature near-infrared lasing, making them highly promising for use as nanoscale, silicon-integrable, and coherent light sources. While lasing behavior is reproducible, small variations in growth conditions across a substrate arising from the use of bottom-up growth techniques can introduce interwire disorder, either through geometric or material inhomogeneity. Nanolasers critically depend on both high material quality and tight dimensional tolerances, and as such, lasing threshold is both sensitive to and a sensitive probe of such inhomogeneity. We present an all-optical characterization technique coupled to statistical analysis to correlate geometrical and material parameters with lasing threshold. For these multiple-quantum-well nanolasers, it is found that low threshold is closely linked to longer lasing wavelength caused by losses in the core, providing a route to optimized future low-threshold devices. A best-in-group room temperature lasing threshold of ∼43 μJ cm under pulsed excitation was found, and overall device yields in excess of 50% are measured, demonstrating a promising future for the nanolaser architecture.

Please note: Changes made as a result of publishing processes such as copy-editing, formatting and page numbers may not be reflected in this version. For the definitive version of this publication, please refer to the published source. You are advised to consult the publisher's version if you wish to cite this paper.

Continuous room temperature nanowire lasing from silicon-integrated optoelectronic elements requires careful optimisation of both the lasing cavity Q-factor and population inversion conditions. We apply time-gated optical interferometry to the lasing emission from high-quality GaAsP/GaAs quantum well nanowire laser structures, revealing high Q-factors of 1250 ± 90 corresponding to end-facet reflectivities of R = 0.73 ± 0.02. By using optimised direct-indirect band alignment in the active region, we demonstrate a well-refilling mechanism providing a quasifour-level system leading to multi-nanosecond lasing and record low room temperature lasing thresholds (~6 μJ cm −2 pulse −1) for III-V nanowire lasers. Our findings demonstrate a highly promising new route towards continuously operating silicon-integrated nanolaser elements.

Fluence-dependent photoluminescence and ultrafast transient absorption spectroscopy are used to study the dynamic behavior of carriers in CsPbCl3 perovskite nanocrystals. At low excitation fluences, the radiative recombination rate is outcompeted by significant trapping of the charge carriers which then recombine nonradiatively, resulting in weak photoluminescence. As fluence is increased, the saturation of trap states deactivates these nonradiative relaxation paths giving rise to an increase in photoluminescence at first. However, with further increases in fluence, Auger recombination of multiexcitons results in a decline in photoluminescence efficiency. Analysis of this behavior yields an absorption cross section at 400 nm (3.1 eV) of (0.24 ± 0.05) × 10–14 cm2. Transient photoluminescence and absorption measurements yielded values for single exciton trapping lifetime (1.6 ± 0.7 ns), biexciton and trion lifetimes (20 ± 3 and 157 ± 20 ps, respectively), single exciton radiative lifetime (12.7 ± 0.2 ns), intraband cooling lifetime (290 ± 37 fs), and exciton–exciton interaction energy (10 ± 2 meV).

The ever-increasing demand for artificial intelligence (AI) systems is underlining a significant requirement for new, AI-optimised hardware. Neuromorphic (brain-like) processors are one highly-promising solution, with photonic-enabled realizations receiving increasing attention. Among these, approaches based upon vertical cavity surface emitting lasers (VCSELs) are attracting interest given their favourable attributes and mature technology. Here, we demonstrate a hardware-friendly neuromorphic photonic spike processor, using a single VCSEL, for all-optical image edge-feature detection. This exploits the ability of a VCSEL-based photonic neuron to integrate temporally-encoded pixel data at high speed; and fire fast (100 ps-long) optical spikes upon detecting desired image features. Furthermore, the photonic system is combined with a software-implemented spiking neural network yielding a full platform for complex image classification tasks. This work therefore highlights the potential of VCSEL-based platforms for novel, ultrafast, all-optical neuromorphic processors interfacing with current computation and communication systems for use in future light-enabled AI and computer vision functionalities.