NEURAL PROCESSING-TYPE OPTICAL WAVELENGTH-DIVISION MULTIPLEXING DEMULTIPLEXER USING A GRATING

A neural processing-type optical wavelength division multiplexing (WDM ) demultiplexer (NP-ODEMUX), which consists of diffraction grating, de tector array, and neural network LSI, is proposed in order to improve receiver sensitivity while maintaining flexible demultiplexing charact eristics. In previous work, a neural processing-type optical WDM demul tiplexer using a multimode optical waveguide was proposed. This conven tional NP-ODEMUX and the proposed grating-type NP-ODEMUX are compared using power penalty calculation simulations. The results show that rec eiver sensitivity is improved using the proposed grating-type NP-ODEMU X. Also, a standard diffraction grating demultiplexer without neural p rocessing is compared with the proposed NP-ODEMUX using power penalty and it is shown that the proposed NP-ODEMUX can demultiplex WDM signal s without degrading receiver sensitivity. Next, the results of the dem ultiplexing experiment;for six-channel WDM signal which has been direc tly modulated at 150 Mb/s using nonreturn to zero (NRZ) pseudorandom s ignal by grating-type NP-ODEMUX prototype are described. Bit-error rat e measurement for both the conventional diffraction grating demultiple xer and the grating-type NP-ODEMUX using neural processing for the une qually spaced WDM signals revealed that the receiver sensitivity can b e greatly improved and that it can flexibly compensate wavelength chan ge.