Uniformity improvement of two-dimensional surface relief grating waveguide display using particle swarm optimization

Tandem neural network-assisted inverse design of highly efficient diffractive slanted waveguide grating

Virtual reality devices featuring diffractive grating components have emerged as hotspots in the field of near-to-eye displays. The core aim of our work is to streamline the intricacies involved in devising the highly efficient slanted waveguide grating using the deep-learning-driven inverse design technique. We propose and establish a tandem neural network (TNN) comprising a generative flow-based invertible neural network and a fully connected neural network. The proposed TNN can automatically optimize the coupling efficiencies of the proposed grating at multi-wavelengths, including red, green, and blue beams at incident angles in the range of 0°-15°. The efficiency indicators manifest in the peak transmittance, average transmittance, and illuminance uniformity, reaching approximately 100%, 92%, and 98%, respectively. Additionally, the structural parameters of the grating can be deduced inversely based on the indicators within a short duration of hundreds of milliseconds to seconds using the TNN. The implementation of the inverse-engineered grating is anticipated to serve as a paradigm for simplifying and expediting the development of diverse types of waveguide gratings.

Compact pupil-expansion AR-HUD based on surface-relief grating

Augmented reality head-up display (AR-HUD) using diffractive waveguide is a challenging research field. It can drastically reduce the system volume compared with AR-HUD based on freeform mirror. However, one of the remaining challenges that affects the performance of the diffractive waveguide is to expand the eye-box while maintaining the illuminance uniformity. In this paper, a one-dimensional pupil expansion diffractive optical waveguide system for AR-HUD is presented. The optimization of grating parameters is based on scalar diffraction theory and rigorous coupled wave analysis (RCWA). Then, the illuminance uniformity is optimized through non-sequential ray tracing. We simulate and construct a waveguide-based AR-HUD. The presented AR-HUD realized an exit pupil size of 80 mm × 15 mm and a field of view of 10° × 5° at the wavelength of 532 nm.

Realizing multi-function absorptions through arbitrary octagonal meta-atoms

Metasurface absorbers (MA) typically exhibit a single type of absorption function due to their regular structures. In this study, we propose an irregular MA structure with octagonal meta-atoms. The presence of eight vertices in each meta-atom allows for tunable coordinates and offers a multitude of degrees of freedom in terms of geometry. As a result, the proposed MA exhibits diverse functionalities, including perfect absorption, multi-peaks absorption, and high absorption with a filtering window. To predict the geometric parameters of the MA structure based on a given target absorption spectrum, as well as the inverse design of the structure using the absorption spectrum as input, we employ a deep neural network combined with the particle swarm optimization algorithm. Remarkably, the mean-square error for spectrum prediction and inverse design of the MA structure is found to be as low as 0.0008 and 0.0031, respectively. This study opens up new possibilities for designing irregular electromagnetic structures and holds great potential for applications in multifunctional metasurfaces and metamaterials.

Waveguide-based augmented reality displays: a highlight

Augmented reality (AR), which emerged in the 1960s, remains a focal point of interest given its capacity to overlay the real world with digitally presented information through optical combiners. The prevalent combiner, commonly known as the waveguide in the AR literature, is prized for its compact design and generous eyebox-essential elements in human-centric technology. Nonetheless, these combiners encounter unique challenges in meeting various other requirements of the human visual system. This paper highlights a recent review of technological advancements and presents a forward-looking perspective on the future of AR technology.

Single-image-source binocular waveguide display based on polarization volume gratings and lenses

Waveguide displays, a highly competitive solution for augmented reality (AR), have attracted a lot of interest. A polarization-dependent binocular waveguide display using polarization volume lenses (PVLs) and polarization volume gratings (PVGs) as input and output couplers, respectively, is proposed. Light from a single image source is delivered to the left and right eyes independently according to its polarization state. Compared with traditional waveguide display systems, no additional collimation system is needed due to the deflection and collimation capabilities of PVLs. Leveraging the high efficiency, wide angular bandwidth, and polarization selectivity of liquid crystal elements, different images can be independently and accurately produced in the two eyes when the polarization of the image source is modulated. The proposed design paves the way for a compact and lightweight binocular AR near-eye display.

Grating waveguides by machine learning for augmented reality

We propose a machine-learning-based method for grating waveguides and augmented reality, significantly reducing the computation time compared with existing finite-element-based numerical simulation methods. Among the slanted, coated, interlayer, twin-pillar, U-shaped, and hybrid structure gratings, we exploit structural parameters such as grating slanted angle, grating depth, duty cycle, coating ratio, and interlayer thickness to construct the gratings. The multi-layer perceptron algorithm based on the Keras framework was used with a dataset comprised of 3000-14,000 samples. The training accuracy reached a coefficient of determination of more than 99.9% and an average absolute percentage error of 0.5%-2%. At the same time, the hybrid structure grating we built achieved a diffraction efficiency of 94.21% and a uniformity of 93.99%. This hybrid structure grating also achieved the best results in tolerance analysis. The high-efficiency artificial intelligence waveguide method proposed in this paper realizes the optimal design of a high-efficiency grating waveguide structure. It can provide theoretical guidance and technical reference for optical design based on artificial intelligence.

Eyebox uniformity optimization over the full field of view for optical waveguide displays based on linked list processing

Eyebox uniformity is an important indicator to evaluate the performance of optical waveguide displays. However, there is currently no standard design approach that achieves ideal uniformity over the full field of view (FOV) within the eyebox. Here, a novel method for eyebox uniformity optimization based on linked list processing is proposed. The linked list processing method can fast record the light trajectory and calculate the optimal numerical diffraction efficiency distribution of the coupler. We use the linked list method for an L-shaped diffractive optical waveguide and solve the matched coupler structure by combining rigorous coupled-wave analysis (RCWA) and the simplex method. By building the model on LightTools and demonstrating the illuminance uniformity, the feasibility of the method is verified. In the FOV range of 15°× 15°, the eyebox uniformity reaches 0.9 at the central viewing angle and the overall eyebox uniformity is 0.617.