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Direct Imaging of Laser-driven Ultrafast Molecular Rotation

Direct Imaging of Laser-driven Ultrafast Molecular Rotation

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Loop mirror laser neural network with a fast liquid-crystal display.

E C Mos¹, J J Schleipen, H de Waardt

¹Department of Electrotechnical Engineering, Eindhoven University of Technology, PO Box 513, 5600 MB, Eindhoven, The Netherlands. mosec@natlab.research.philips.com

|March 8, 2008

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This study introduces an expanded laser neural network (LNN) with 32 neurons for all-optical computing. The LNN demonstrates efficient optical feedback for telecommunication data switching applications.

Area of Science:

Optoelectronics
Artificial Intelligence
Telecommunications

Background:

Laser neural networks (LNNs) offer a pathway for all-optical computing.
Previous LNNs had limitations in scale and training speed.

Purpose of the Study:

To present an extended experimental laser neural network (LNN) with enhanced capabilities.
To explore the application of LNNs in telecommunication data switching.

Main Methods:

Utilized a laser diode with controlled optical feedback for threshold action.
Implemented an optical matrix vector multiplier using a fast liquid-crystal display (125 training examples/s).
Incorporated a loop mirror to maximize optical feedback efficiency and employed a delta-rule learning algorithm.

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Main Results:

Demonstrated an all-optical threshold action in an LNN with up to 32 neurons and 12 inputs.
Achieved high training example presentation rates using a ferroelectric liquid-crystal display.
Successfully trained the LNN for functions relevant to telecommunication data switching.

Conclusions:

The extended LNN architecture is effective for all-optical information processing.
The use of advanced optical components enables high-speed training and operation.
LNNs show significant potential for future telecommunication data switching solutions.