Ultrafast Computing With Nonlinear Photonics
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| Publicado en: | ProQuest Dissertations and Theses (2025) |
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ProQuest Dissertations & Theses
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| Acceso en liña: | Citation/Abstract Full Text - PDF Full text outside of ProQuest |
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| 100 | 1 | |a Li, Gordon Han Ying | |
| 245 | 1 | |a Ultrafast Computing With Nonlinear Photonics | |
| 260 | |b ProQuest Dissertations & Theses |c 2025 | ||
| 513 | |a Dissertation/Thesis | ||
| 520 | 3 | |a Computers have revolutionized almost every facet of modern society, and as we approach the physical limits of digital electronics, it becomes imperative to investigate alternative computing hardware paradigms to enable the next generation of faster and more energy-efficient computers. This thesis embarks on building the foundation for a new kind of computer, based on ultrafast nonlinear photonics, aiming to overcome some of the limitations plaguing current computers. In particular, we primarily focus on the clock rate, which has stagnated at ∼5 GHz for conventional microprocessors over the past two decades.We begin by identifying single nonlinear devices in lithium niobate nanophotonics that can act as essential building blocks for computers, showing a variety of nonlinear functions with operational speeds > 13 THz for artificial intelligence computing workloads. Then, we progress to small-scale photonic computing circuits combining both strong nonlinearity and memory feedback in a physical reservoir computer for temporal information processing with ∼10 GHz clock rates. Additionally, we explore unconventional computer architectures such as Cellular Automata, which reveals key system-level considerations that maximize the benefits of ultrafast nonlinear photonics in large-scale computers. This culminates in the demonstration of truly end-to-end and all-optical computing with > 100 GHz clock rates, which represents over an order-of-magnitude advancement compared to existing electronic computers. Finally, we prove mathematically how coupled nonlinear optical resonators are Turing-complete computers.Overall, this work builds on the recent advances in nonlinear photonics and highlights a path for a new class of ultrafast photonic computers that can surpass the clock rate and latency limits of electronic computers, hence enabling nascent applications requiring real-time control or information processing at picosecond timescales. | |
| 653 | |a Computers | ||
| 653 | |a Deep learning | ||
| 653 | |a Computer peripherals | ||
| 653 | |a Personal computers | ||
| 653 | |a Neural networks | ||
| 653 | |a Digital electronics | ||
| 653 | |a Photonics | ||
| 653 | |a Energy | ||
| 653 | |a Preprints | ||
| 653 | |a Optics | ||
| 773 | 0 | |t ProQuest Dissertations and Theses |g (2025) | |
| 786 | 0 | |d ProQuest |t Publicly Available Content Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3195715579/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3195715579/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u https://resolver.caltech.edu/CaltechTHESIS:02032025-021547979 |