The development of modern Machine Learning (ML) frameworks trained on large datasets established a rapid increase in the performance of cognitive computing algorithms for a wide range of applications. However, due to the processing capacity restrictions of electronics, scaling up the existing state-of-the-art is currently meeting a bottleneck. Recently, flat-optics arose as a promising alternative to conventional electronics due to intrinsic parallelism, tuneability, and high-speed of optical computations. Finding scalable, highly effective designs that can tolerate fabrication defects brought on by nanoscale manufacturing processes and the demanding design specifications of the end task is one of the main hurdles of flat optics. In this study, we address this problem by introducing an end-to-end optimization methodology that is robust to fabrication intolerance and performance losses due to material absorption and can simultaneously optimize in tens of millions of degrees of freedom. The core of this technology is universal approximators, a single surface of optical nanoresonators mathematically equivalent to a single layer of an artificial neural network (ANN). For these structures, we provide theoretical guarantees for universal approximation, an ability to approximate arbitrary defined material's transfer function. We validate this framework's capability by creating several optical components achieving near unity efficiencies for vectorial light processing with broadband spectral responses and pre-defined wavefront characteristics. In addition, leveraging the high-dimensional capabilities of that system, we present a novel concept of spectral-informed imaging, which does not require the use of spectral analyzers or complex mechanical filters but uses an artificial-intelligence engineered, "hardware" flat-optics surface that processes spectral encoding at the speed of light inside silicon (Si) metasurface.
| Identifer | oai:union.ndltd.org:kaust.edu.sa/oai:repository.kaust.edu.sa:10754/686004 |
| Date | 11 1900 |
| Creators | Makarenko, Maksim |
| Contributors | Fratalocchi, Andrea, Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division, Moshkov, Mikhail, Ooi, Boon S., Kivshar, Yuri |
| Source Sets | King Abdullah University of Science and Technology |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
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