Connectionist Learning Architecture Based on an Optical Thin-Film Multilayer Model

Connectionist models consist of large numbers of simple but highly interconnected "units". They represent an approach that is quite different from that of classical models based on the structure of Von Neumann machines. Although the term "connectionist models" often refers to artificial neural network models, which are inspired directly by the biological neurons, there are also other connectionist architectures that differ significantly from this biological exemplar. This thesis describes such a "novel" connectionist learning architecture based on the technology of optical thin-film multilayer. The proposed connectionist model consists of multiple thin-film layers (similar to simple processing units in a neural network model), each with different refractive index and thickness. A light beam incident perpendicular to the surface of the multilayer stack is used to carry out the required computation. The reflectance of the light incident can be used as the general measurement of the outpu..