Optical neural network with quantum well devices

Optical-mode neural network by use of the nonlinear response of a laser diode to external optical feedback

We present an intelligent all-optical neural network using a single laser diode that is provided with controlled external feedback. The outputs of the laser neural network (LNN) are represented in the optical domain by the longitudinal cavity modes of the laser diode. The inputs to the LNN are applied by means of adjusting the external feedback of each longitudinal mode through an optical vector-matrix multiplier. Supervised training of some basic input-output mappings is demonstrated by means of a stochastic learning algorithm. The stability and reproducibility of the LNN setup is examined.

Smart-pixel-based network interface chip

We present the design and experimental setup of an optically interconnected smart-pixel network interface chip designed to implement a collisionless multichannel-access control protocol. The design demonstrates the cointegration of optoelectronic pixel modules of various levels of complexity for dense, high-speed interconnection of highly functional digital logic components typical of multiprocessor network routers.

Optical implementation of a translation-invariant second-order neural network for multiple-pattern classification

A novel approach to the optical implementation of second-order neural networks that can recognize multiple patterns is reported. The systems issues, especially the accuracy required for the weighted interconnections, are discussed for numeric character (0-9) recognition. It is shown that the accuracy of the weighted interconnections has a far greater influence on the network performance during training than on classification. To lessen the problem, we introduce an adaptive learning rule, whereby the optical power is adjusted during training. Finally, numeric character recognition using an experimental system with a liquid-crystal display is demonstrated.