2026-09-19T03:26:22Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/775372022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Cardinal Warde.
Mars, Risha R
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2013-03-01T15:27:24Z
2013-03-01T15:27:24Z
2012
2012
http://hdl.handle.net/1721.1/77537
826520134
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.
81 p.
eng
Massachusetts Institute of Technology
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
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Organic LEDs for optoelectronic neural networks
Organic Light-Emitting Diodes for optoelectronic neural networks
Thesis
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<Abstract>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.</Abstract>
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