It has been suggested that massively parallel optical routing networks and digital processors are unlikely to be 100% error free due to the myriad problems encountered in the fabrication, alignment and operation of the active components and their associated optics. This paper presents novel algorithms based on the back-propagation class of neural networks which can be implemented on optical hardware which can allow these networks or processors to correctly perform their functions in the event of failure or misalignment of a particular processing element. The algorithms are of particular interest since they are designed to exploit the particular optical characteristics and dynamics of the hardware, in this case nonlinear interference filters (NLIFs).
John Fraser Snowdon,
"Neural algorithms for fault-tolerant optical hardware", Proc. SPIE 1773, Photonics for Computers, Neural Networks, and Memories, (2 February 1993); doi: 10.1117/12.983184; https://doi.org/10.1117/12.983184