Nature Photonics最新文献

Highly efficient and high-definition displays with deformable form factors are highly desirable for next-generation electronic devices. Despite the unique advantages of quantum dots (QDs), including high photoluminescence quantum yield, wide colour range and high colour purity, developing a QD patterning process for high-definition pixels and efficient QD light-emitting diodes (QLEDs) is in its early stages. Here we present highly efficient QLEDs through ultrahigh-definition double-layer transfer printing of a QD/ZnO film. Surface engineering of viscoelastic stamps enables double-layer transfer printing that can create RGB pixelated patterns with 2,565 pixels per inch and monochromic QD patterns with ~20,526 pixels per inch. The close packing of both QDs and ZnO nanoparticles by double-layer transfer printing substantially minimizes the leakage current, enhancing the external quantum efficiency of our devices to 23.3%. Furthermore, we demonstrate highly efficient wearable QLEDs fabricated by our technique. This study paves the way for the development of highly efficient, full-colour QD displays via the transfer printing technique, demonstrating great promise for next-generation display technologies.

Optical information processing and computing can potentially offer enhanced performance, scalability and energy efficiency. However, achieving nonlinearity—a critical component of computation—remains challenging in the optical domain. Here we introduce a design that leverages a multiple-scattering cavity to passively induce optical nonlinear random mapping with a continuous-wave laser at a low power. Each scattering event effectively mixes information from different areas of a spatial light modulator, resulting in a highly nonlinear mapping between the input data and output pattern. We demonstrate that our design retains vital information even when the readout dimensionality is reduced, thereby enabling optical data compression. This capability allows our optical platforms to offer efficient optical information processing solutions across applications. We demonstrate our design’s efficacy across tasks, including classification, image reconstruction, keypoint detection and object detection, all of which are achieved through optical data compression combined with a digital decoder. In particular, high performance at extreme compression ratios is observed in real-time pedestrian detection. Our findings open pathways for novel algorithms and unconventional architectural designs for optical computing.

Deep neural networks have achieved remarkable breakthroughs by leveraging multiple layers of data processing to extract hidden representations, albeit at the cost of large electronic computing power. To enhance energy efficiency and speed, the optical implementation of neural networks aims to harness the advantages of optical bandwidth and the energy efficiency of optical interconnections. In the absence of low-power optical nonlinearities, the challenge in the implementation of multilayer optical networks lies in realizing multiple optical layers without resorting to electronic components. Here we present a novel framework that uses multiple scattering, and which is capable of synthesizing programmable linear and nonlinear transformations concurrently at low optical power by leveraging the nonlinear relationship between the scattering potential, represented by data, and the scattered field. Theoretical and experimental investigations show that repeating the data by multiple scattering enables nonlinear optical computing with low-power continuous-wave light. Moreover, we empirically find that scaling of this optical framework follows a power law.