Laser Neural Network Research Articles

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

In our laser neural network (LNN) all-optical threshold action is obtained by application of controlled optical feedback to a laser diode. Here an extended experimental LNN is presented with as many as 32 neurons and 12 inputs. In the setup we use a fast liquid-crystal display to implement an optical matrix vector multiplier. This display, based on ferroelectric liquid-crystal material, enables us to present 125 training examples/s to the LNN. To maximize the optical feedback efficiency of the setup, a loop mirror is introduced. We use a delta-rule learning algorithm to train the network to perform a number of functions toward the application area of telecommunication data switching.

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.