Organic LEDs for optoelectronic neural networks
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | eng |
| Published: |
Massachusetts Institute of Technology
2013
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/77537 |
| _version_ | 1811089355986436096 |
|---|---|
| author | Mars, Risha R |
| author2 | Cardinal Warde. |
| author_facet | Cardinal Warde. Mars, Risha R |
| author_sort | Mars, Risha R |
| collection | MIT |
| description | Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012. |
| first_indexed | 2024-09-23T14:17:31Z |
| format | Thesis |
| id | mit-1721.1/77537 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T14:17:31Z |
| publishDate | 2013 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/775372019-04-12T21:40:52Z Organic LEDs for optoelectronic neural networks Organic Light-Emitting Diodes for optoelectronic neural networks Mars, Risha R Cardinal Warde. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012. Cataloged from PDF version of thesis. Includes bibliographical references (p. 79-81). In this thesis, I investigate the characteristics of Organic Light Emitting Diodes (OLEDs) and assess their suitability for use in the Compact Optoelectronic Integrated Neural (COIN) coprocessor. The COIN coprocessor, a prototype artificial neural network implemented in hardware, seeks to implement neural network algorithms in native optoelectronic hardware in order to do parallel type processing in a faster and more efficient manner than all-electronic implementations. The feasibility of scaling the network to tens of millions of neurons is the main reason for optoelectronics - they do not suffer from crosstalk and other problems that affect electrical wires when they are densely packed. I measured the optical and electrical characteristics different types of OLEDs, and made calculations based on existing optical equipment to determine the specific characteristics required if OLEDs were to be used in the prototype. The OLEDs were compared to Vertical Cavity Surface Emitting Lasers (VCSELs) to determine the tradeoffs in using one over the other in the prototype neural network. by Risha R. Mars. M.Eng. 2013-03-01T15:27:24Z 2013-03-01T15:27:24Z 2012 2012 Thesis http://hdl.handle.net/1721.1/77537 826520134 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 81 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Mars, Risha R Organic LEDs for optoelectronic neural networks |
| title | Organic LEDs for optoelectronic neural networks |
| title_full | Organic LEDs for optoelectronic neural networks |
| title_fullStr | Organic LEDs for optoelectronic neural networks |
| title_full_unstemmed | Organic LEDs for optoelectronic neural networks |
| title_short | Organic LEDs for optoelectronic neural networks |
| title_sort | organic leds for optoelectronic neural networks |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/77537 |
| work_keys_str_mv | AT marsrishar organicledsforoptoelectronicneuralnetworks AT marsrishar organiclightemittingdiodesforoptoelectronicneuralnetworks |