Near-optimal parallel distributed data detection for page-oriented optical memories

Volume optical storage systems suffer from numerous sources of noise and interference, the effects of which can seriously degrade retrieved data fidelity and produce unacceptable bit-error rates (BERs). We examine the problem of reliable two-dimensional data retrieval in the context of recently developed soft-decision methods for iterative decoding. We describe a novel near-optimal algorithm in which each pixel on the page is treated as a starting point for a simple iterative procedure so that a highly parallel, locally connected, distributed computational model emerges whose operation is well suited to the page-oriented memory (POM) interface format. We study the use of our two-dimensional distributed data detection (2D⁴) algorithm with both incoherent (linear) and coherent (nonlinear) finite-contrast POM channel models. We present BER results obtained using the 2D⁴ algorithm and compare these with three other typical methods [i.e., simple thresholding (THA), differential encoding (DC) and the decision feedback Viterbi algorithm (DFVA)]. The BER improvements are shown to have a direct impact on POM storage capacity and density and this impact is quantified for the special case of holographic POM. In a Rayleigh resolved holographic POM system with infinite contrast, we find that 2D⁴ offers capacity improvements of 84%, 56%, and 8% as compared with DC, THA, and DFVA respectively, with corresponding storage density gains of 85%, 26%, and 9%. In the case of finite contrast (C=4), similar capacity improvements of 93%, 18%, and 4% produce similar density improvements of 98%, 21%, and 6%. Implementational issues associated with the realization of this new distributed detection algorithm are also discussed and parallel neural and focal plane strategies are considered. A 2 cm² λ=0.1 μm digital VLSI real estate budget will support a 600×600 pixel 2D⁴ focal plane processor operating at 40 MHz with less than 1.7 W/cm² power dissipation