Due to the ever-increasing demands of highly integrated optical devices in imaging, spectroscopy, communications, and so on, there is a compelling need to design and characterize novel compact photonic components. The traditional approaches to realizing compact optical devices typically result in large footprints and sizable optical thicknesses. Moreover, they offer few degrees of freedom (DOF), hampering on-demand functionalities, on-chip integration, and scalability.
This thesis will address the design and development of ultracompact diffractive devices for imaging and spectroscopy, utilizing advanced machine learning techniques and optimization algorithms. I first present the inverse design of ultracompact dual-focusing lenses and broad-band focusing spectrometers based on adaptive diffractive optical networks (a-DONs), which combine optical diffraction physics and deep learning capabilities for the inverse design of multi-layered diffractive devices. I designed two-layer diffractive devices that can selectively focus incident radiation over well-separated spectral bands at desired distances and also optimized a-DON-based focusing spectrometers with engineered angular dispersion for desired bandwidth and nanometer spectral resolution. Furthermore, I introduced a new approach based on a-DONs for the engineering of diffractive devices with arbitrary k-space, which produces improved imaging performances compared to contour-PSF approaches to lens-less computational imaging. Moreover, my method enables control of sparsity and isotropic k-space in pixelated screens of dielectric scatterers that are compatible with large-scale photolithographic fabrication techniques. Finally, by combining adjoint optimization with the rigorous generalized Mie theory, I developed and characterize functionalized compact devices, which I called "photonic patches," consisting of ~100 dielectric nanocylinders that achieve predefined functionalities such as beam steering, Fresnel zone focusing, local density of states (LDOS) enhancement, etc. My method enables the inverse design of ultracompact focusing spectrometers for on-chip planar integration. Leveraging multiple scattering of light in disordered random media, I additionally demonstrated a novel approach to on-chip spectroscopy driven by high-throughput multifractal (i.e., multiscale) media, resulting in sub-nanometer spectral resolution at the 50×50 µm²-scale footprint.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/47945 |
| Date | 18 January 2024 |
| Creators | Zhu, Yilin |
| Contributors | Dal Negro, Luca |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
| Rights | Attribution 4.0 International, http://creativecommons.org/licenses/by/4.0/ |
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