Photonics for technology : circuits, chip-scale LIDAR, and optical neural networks
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, 2017.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2017
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| Online Access: | http://hdl.handle.net/1721.1/112519 |
| _version_ | 1811073459874168832 |
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| author | Skirlo, Scott Alexander |
| author2 | Marin Soljačić. |
| author_facet | Marin Soljačić. Skirlo, Scott Alexander |
| author_sort | Skirlo, Scott Alexander |
| collection | MIT |
| description | Thesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, 2017. |
| first_indexed | 2024-09-23T09:33:06Z |
| format | Thesis |
| id | mit-1721.1/112519 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T09:33:06Z |
| publishDate | 2017 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1125192019-04-11T07:16:05Z Photonics for technology : circuits, chip-scale LIDAR, and optical neural networks Circuits, chip-scale LIDAR, and optical neural networks Skirlo, Scott Alexander Marin Soljačić. Massachusetts Institute of Technology. Department of Physics. Massachusetts Institute of Technology. Department of Physics. Physics. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, 2017. Cataloged from PDF version of thesis. Includes bibliographical references (pages 163-175). This thesis focuses on a wide range of contemporary topics in modern electromagnetics and technology including topologically protected one-way modes, integrated photonic LIDAR, and optical neural networks. First, we numerically investigate large Chern numbers in photonic crystals and explore their origin from simultaneously gapping multiple band degeneracies. Following this, we perform microwave transmission measurements in the bulk and at the edge of ferrimagnetic photonic crystals. Bandgaps with large Chern numbers of 2, 3, and 4 are present in the experimental results 'which show excellent agreement with theory. We measure the mode profiles and Fourier transform them to produce dispersion relations of the edge modes, whose number and direction match our Chern number calculations. We use these waveguides to realize reflectionless power splitters and outline their application to general one-way circuits. Next we create a new chip-scale LIDAR architecture in analogy to planar RF lenses. Instead of relying upon many continuously tuned thermal phase shifters to implement nonmechanical beam steering, we use aplanatic lenses excited in their focal plane feeding ID gratings to generate discrete beams. We design devices which support up to 128 resolvable points in-plane and 80 resolvable points out-of-plane, which are currently being fabricated and tested. These devices have many advantages over conventional optical phased arrays including greatly increased optical output power and decreased electrical power for in-plane beamforming. Finally we explore a new approach for implementing convolutional neural networks through an integrated photonics circuit consisting of Mach-Zehnder Interferometers, optical delay lines, and optical nonlinearity units. This new platform, should be able to perform the order of a thousand inferences per second, at [mu]J power levels per inference, with the nearest state of the art ASIC and GPU competitors operating 30 times slower and requiring three orders of magnitude more power. by Scott Alexander Skirlo. Ph. D. 2017-12-05T19:16:29Z 2017-12-05T19:16:29Z 2017 2017 Thesis http://hdl.handle.net/1721.1/112519 1012937987 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 175 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Physics. Skirlo, Scott Alexander Photonics for technology : circuits, chip-scale LIDAR, and optical neural networks |
| title | Photonics for technology : circuits, chip-scale LIDAR, and optical neural networks |
| title_full | Photonics for technology : circuits, chip-scale LIDAR, and optical neural networks |
| title_fullStr | Photonics for technology : circuits, chip-scale LIDAR, and optical neural networks |
| title_full_unstemmed | Photonics for technology : circuits, chip-scale LIDAR, and optical neural networks |
| title_short | Photonics for technology : circuits, chip-scale LIDAR, and optical neural networks |
| title_sort | photonics for technology circuits chip scale lidar and optical neural networks |
| topic | Physics. |
| url | http://hdl.handle.net/1721.1/112519 |
| work_keys_str_mv | AT skirloscottalexander photonicsfortechnologycircuitschipscalelidarandopticalneuralnetworks AT skirloscottalexander circuitschipscalelidarandopticalneuralnetworks |