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Yinggang Chen

Publications and source records attributed to Yinggang Chen.

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A 0.35 mm Silicon Carbide Diffractive Waveguide with Dual Parameter Apodization for Full Color Augmented Reality

Augmented reality eyewear offers a transformative interface poised to reshape human information interaction. In this context, silicon carbide (SiC) offers unique advantages for diffractive waveguides with its high refractive index and excellent thermal conductivity. However, in single-layer full-color displays, existing SiC waveguides generally remain thicker than 0.5 mm to reduce the bounce count of total internal reflection, thereby avoiding severe spatial variations in luminance and color. Here, we demonstrate a 0.35 mm SiC diffractive waveguide with an ultra-lightweight of only 1.98 g. The challenge of spatial non-uniformity is addressed by dual-parameter apodized gratings with continuously varying depth and duty cycle, enabling fine spatial control over local diffraction efficiency. To realize high-throughput production, a parallel gradient transfer method compatible with nanoimprint lithography is introduced, enabling wafer-scale patterning of four lens pairs per 8-inch SiC wafer. Furthermore, magnesium fluoride planarization suppresses grating visibility and achieves a high see-through transmittance of 92%. Optical simulations confirm that this architecture achieves balanced full-color transmission across a 30-degree field of view. This strategy provides a scalable route toward ultra-light and visually unobtrusive glasses, opening new opportunities for practical consumer-grade wearable displays.

physics.optics

Highly emissive, selective and omnidirectional thermal emitters mediated by machine learning for ultrahigh performance passive radiative cooling

Real-world passive radiative cooling requires highly emissive, selective, and omnidirectional thermal emitters to maintain the radiative cooler at a certain temperature below the ambient temperature while maximizing the net cooling power. Despite various selective thermal emitters have been demonstrated, it is still challenging to achieve these conditions simultaneously because of the extreme complexity of controlling thermal emission of photonic structures in multidimension. Here we demonstrated machine learning mediated hybrid metasurface thermal emitters with a high emissivity of ~0.92 within the atmospheric transparency window 8-13 μm, a large spectral selectivity of ~1.8 and a wide emission angle up to 80 degrees, simultaneously. This selective and omnidirectional thermal emitter has led to a new record of temperature reduction as large as ~15.4 degree under strong solar irradiation of ~800 W/m2, significantly surpassing the state-of-the-art results. The designed structures also show great potential in tackling the urban heat island effect, with modelling results suggesting a large energy saving and deployment area reduction. This research will make significant impact on passive radiative cooling, thermal energy photonics and tackling global climate change.

physics.app-ph