Organic LEDs for optoelectronic neural networks

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2012

Author dc:creator

• Mars, Risha R

Advisor dc:contributor.advisor

• Cardinal Warde.

• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/77537

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/77537