Micro-optic elements for a compact opto-electronic integrated neural coprocessor

Abstract

The research done for this thesis was aimed at developing the optical elements needed for the Compact Opto-electronic Integrated Neural coprocessor (COIN coprocessor) project. The COIN coprocessor is an implementation of a feed forward neural network using free-space optical interconnects to communicate between neurons. Prior work on this project had assumed these interconnects would be formed using Holographic Optical Elements (HOEs), so early work for this thesis was directed along these lines. Important limits to the use of HOEs in the COIN system were identified and evaluated. In particular, the problem of changing wavelength between the hologram recording and readout steps was examined and it was shown that there is no general solution to this problem when the hologram to be recorded is constructed with more than two plane waves interfering with each other. Two experimental techniques, the holographic bead lens and holographic liftoff, were developed as partial workarounds to the identified limitations. As an alternative to HOEs, an optical element based on the concept of the Fresnel Zone Plate was developed and experimentally tested. The zone plate based elements offer an easily scalable method for fabricating the COIN optical interconnects using standard lithographic processes and appear to be the best choice for the COIN coprocessor project at this time. In addition to the development of the optical elements for the COIN coprocessor, this thesis also looks at the impact of optical element efficiency on the power consumption of the COIN coprocessor. Finally, a model of the COIN network based on the current COIN design was used to compare the performance and cost of the COIN system with competing implementations of neural networks, with the conclusion that at this time the proposed COIN coprocessor system is still a competitive option for neural network implementations.