An all-optical neural network is presented that is based on coupled lasers. Each laser in the network lases at a distinct wavelength, representing one neuron. The network status is determined by the wavelength of the network's light output. Inputs to the network are in the optical power domain. The nonlinear threshold function required for neural-network operation is achieved optically by interaction between the lasers. The behavior of the coupled lasers is explained by a simple laser model developed in the paper. In particular, the winner take all (WTA) neural-network behavior of a system of many lasers is described. An experimental system is implemented using single mode fiber optic components at wavelengths near 1550 nm. A number of functions are implemented to demonstrate the practicality of the new network. The neural network is particularly robust against input wavelength variations.

A fully digital and simple to implement frequency detector for use with high frequency signals is presented. The new frequency detector can detect small phase movements of much less than one cycle, even though the frequency detector is clocked at a low rate. Existing digital techniques can only detect phase movements of more than one cycle, or require clock rates much higher than the frequency of the signal. The operating limits of the new frequency detector are derived. Examples of the application of the new frequency detector are also given.

We present an all-optical neuron by use of a multimode laser diode that is subjected to external optical feedback and light injection. The shape of the threshold function, that is needed for neural operation, is controlled by adjusting the external feedback level for two longitudinal cavity modes of the laser diode individually. One of the two modes corresponds to the output of the neuron, light injection at the wavelength of this mode provides excitatory input. Light injection in the other mode provides inhibitory input. When light corresponding to two input signals is injected in the same mode, summation of input signals can be achieved. A rate-equation model is used to explain the operating principle theoretically. The proposed injection seeding neuron is built using free-space optics to demonstrate the concept experimentally. The results are in good agreement with the predictions from the rate-equation model. Some experimental results show threshold functions that are preferable from a neural-network point of view. These results agree well with injection locking theory and experiments reported in literature.

Narrow bandwidth phase-locked loops (PLLs) can have difficulty acquiring lock reliably when there is a significant difference between the input signal and the free run frequency of the PLL's voltage-controlled oscillator (VCO). The new technique presented here incorporates an accurate local reference frequency into the PLL structure. The range of frequencies to which the new PLL structure can lock can be confined to a desired small region around the accurate local reference frequency. The new PLL structure also provides other benefits such as reduction of VCO phase noise. The new technique does not require any monitoring nor any switching of the local frequency reference signal which is always acting. The key parameters of the new PLL structure are identified and the performance characterized.