Organic LEDs for optoelectronic neural networks
Name
826520134-MIT.pdf
Description
Full printable version
Size
7.75 MB
Format
Adobe PDF
Checksum (MD5)
f17e8fd59648bda7ddc4c75c05ddfd41
Author(s)
Mars, Risha R
Advisor(s)
Cardinal Warde.
Alternative Title
Organic Light-Emitting Diodes for optoelectronic neural networks
Date Issued
2012
Publisher
Massachusetts Institute of Technology
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.
Description
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).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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