Write Strategy Learning for Optical Dye Recording

Abstract

Write strategy tuning for optical recording devices (ORDs) is laborious and time consuming. An automated learning approach based on the genetic algorithm (GA) is proposed to automatically learn the write strategy implemented in an ORD for dye recording media. To evaluate the writing performance associated with every set of writing parameters in the write strategy, jitters corresponding to different lengths of pits and lands are utilized as the optimization index. A system is designed that integrates the jitter measurements with the GA based write strategy optimization. To optimize the writing parameters in write strategy, all the parameters are implemented as genes in GA's chromosome and the jitters are transmitted from the ORD to a PC through the integrated drive electronics bus. It will be shown that the proposed automated learning approach can successfully learn the write strategy for different dye recording optical discs at different recording speeds.